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Sistemi Distribuiti
Corso di Laurea in Ingegneria
                  Prof. Paolo Nesi
        Parte 17: Cloud Computing &Virtualization
           Department of Systems and Informatics
                    University of Florence
               Via S. Marta 3, 50139, Firenze, Italy
        tel: +39-055-4796523, fax: +39-055-4796363
Lab: DISIT, Sistemi Distribuiti e Tecnologie Internet
          nesi@dsi.unifi.it, paolo.nesi@unifi.it
              www: http://www.dsi.unifi.it/~nesi


                     Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   1
Structure
G   elementi di cloud Computing
     Motivazioni
     Definizioni
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions
G   SaaS Solutions
G   PaaS Solutions
                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   2
Motivations for
     Cloud computing and Virtualization
G   Physical systems are
     not flexible:
       The HW/SW has to be dimensioned for the worst case and not
          for the real/typical one
       It can be hardly reused for different purposes
     subjected to HW failure:
       Software has to be installed again when moving hardware
       OS has to be reinstalled in most cases
     Subjected to high costs of setup:
       Power, conditioning, network connection, etc.
     non scalable, to scale frequently implies hardware restructuring
       duplication of data, distribution of activities among HW,
       Balancing or workload, sharing databases, etc.

                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   3
Final Motivations
G   Reduction of costs for HW and operating system SW
    maintenance.
     High costs for high availability
     High costs for high reliability
     High costs to follow the HW trends
G   Sharing resources
G   Needs of High flexibility in terms of features
     The sofware is becoming a services and not anymore a
      product.




                        Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   4
Motivations for
               Applications on the Cloud
G   Single tier applications such as small web portals do not
    demand a cloud
G   Multitier application servers
       High performance web server, high number of users
       SN: Social network
       CMS: content management systems
       CRM: customer relationship management
       CDN: Content delivering network
       P2P torrent tracker: see Piratebay
G   …
G   All typically parallel applications that may run on grid
G   ….


                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   5
Datacenter, definition
G   Datacenter
     A computer factory/farm in which servers/computers are
      hosted and organized: power, net maintenance, etc.
       In most cases industrial computers, blades
     They can be exploited for private purposes, grid, cloud
      computing, renting/hosting, etc.




                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   6
Clusters, definition
G   Cluster is typically intended:
     Several kinds of computers and/or VMs may be used to compose
      a cluster, sharing the same domain or not.
     a set of computers/VMs into a datacenter which are dedicated to a
      unique problem, for example:
       A microgrid……………
       A social network……….
       A web portal with its multi-tier servers: front-end portals,
          balancer, database computers, backofficer microgrid nodes,
          etc.




                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   7
Infrastructure, definition
G   A set of datacenter and clusters
G   A set of
     NAS: Network Area Storage
     SAN: Storage Area Network
G   Etc..




                    Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   8
Typical Features for renting a Computer
 or a Virtual Machine into a Datacenter
G   Requesting to have one or more Computers/Hosts and/or VMs
G   Hardware:
      CPU: 32/64 bit, number of cores/CPU, frequency of work, intel/amd, etc.
      RAM memory: size and frequency of work
      Power supply: fault-tolerant or not; with UPS or not, etc..
      HD: space, speed (7.2, 15Kgiri), security level/RAID, type SAS/SATA, SCSI
      Network features:
        Number of connections/cards, number of IP addresses, static/DHCP
        Transfer rate: minimum guaranteed, maximum possible, down/upload
        Maximum transferred bytes: per day, per month, etc.
      NAS/SAN, Network area storage/SAN, fiber/internet: size, RAID, etc.
G   Software:
      Operating systems
      Software preinstalled into the Computer/VM, see in the following
G   Services:
      Periodic back up: details on HD space
      Access to VM/Computer: remote desktop or KVM tool
      Reboot or not of the Server, for example via Plesk.
                                  Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   9
Hosting, definition
G   HPC: high performance computing
     Solution based on cloud/grid for parallel execution of algorithms
      and tools.
G   Hosting web portal into a datacenter/cluster
     Renting a web space via some SLA (service level agreement),
      contract, monthly rate to publishing web pages: service httpd
     Additional services: mysql, php, asp, ftp, ssh, https, etc..
     Features:
       Space on disk, networking
       Domain space, etc.
G   Hosting a machine (computer/VM) into a datacenter
     According to some SLA, contract…
     Renting a Computer/VM/Cluster into a data center


                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   10
From Clustering VM to Cloud
G   The first step has been the exploitation of unused resources
    to provide them as a service to third party.




                         Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   11
Motivations for
     Cloud computing and Virtualization
G   Most of the datacenters capabilities are not exploited at 100%
    in every time instant
     They are typically present in large industries/institutions or in large
      services: google, amazon, tiscali, dada, ibm, cnr, etc
     Their size is typically defined on the basis of the worst case
G   If they are big: the exploitation of the remaining resources for
    cloud/hosting is a solution to recover money, since a non
    working machine (like a caw) is a costs without any return.
G   If they are small: it could be a solution to host machines on a
    professional infrastructure to reduce annual costs for HW/SW
     Delegating to cluster owners the costs for maintenance, renovating
      hardware, renovating software, network costs, back up costs,
      power supply, etc.


                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   12
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
     emulation, para-virtualization
     virtual resources
     snapshots
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions, SaaS Solutions, PaaS Solutions


                             Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   13
Virtual Machine, Virtualization
G   Virtual Machine:
      An image of an operating system that can be put in execution into a real
       host/computer creating a virtual computer that exploits a part of all of the
       host resources
    G E.g.: Host may be Linux-like while the VM may be Window, Mac, Linux, …

G   Virtualization
      Transforming a physical computer into a VM, virtual machine, hosted on
       some Host computer
G   Hypervisor (VM Monitor) to manage the several VMs on the
    host
                          Computer 1
                           Virtual         Virtual
                          Machine         Machine

                               Hypervisor


                                Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   14
Benefits of Virtual Machine
G   Main benefits:
     Separation of OS/SW with respect to the needed HW
     Exploiting legacy solutions which can be wrapped into a VM and
      protected with a physical firewall without reinstalling and recompiling
      old applications.
G   For example:
     An old Linux Server hosting several web portals with a old versions
      of: MySQL, PHP, etc.. and many configuration aspects: users,
      mailing lists, etc. very time consuming to port on a new server
     An old Cobol application running only on an old Windows 2000
      Server, which cannot be recompiled into a new Windows Server
      2008 at 64 bits without spending months of work.
     A Cobol application running on machine based on an IBM system
      36….
     Etc.

                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   15
VM on PC workstations
G   On a single Computer it is possible to put in execution a VM by
    using a standalone Hypervisor
G   VMware Workstation can play on Windows:
     VMware Virtual Machines with: MS Win, Linux, Mac, ChromeOS,
      etc.
G   VMware Player can play on Windows or Linux
     Free of charge
     VMware Virtual Machines with: MS Win, Linux, Mac, ChromeOS,
      etc
G   VMware Fusion can play on MAC:
     VMware Virtual Machines with: MS Win, Win 7, ChromeOS, etc.
G   Microsoft Virtual PC on Windows 7:
     Free of charge
     Create VM with Win XP, Linux Ubuntu,

                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   16
Cloud Computing with VMs
Several Hypervisors on a Clusters
                                                              internet




                      Computer 1                           Computer 2
                                                                                     ……………

                        Virtual       Virtual               Virtual       Virtual
                       Machine       Machine               Machine       Machine


                        Hypervisor                           Hypervisor

                                          Virtual cluster manager
                                    Virtualized Infrastructure --- control servers




            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012                     17
Software/Components preinstalled
           into rented Computers/VMs
G   Several kinds of software components/tools that can be
    accessible on rented VM and/or Computers.
G   Availability of HW/SW: for example at 98% or 99.999%
G   Typical components that could be requested:
     DB: MySQL
     FTP: server and client
     Web Server: Apache, IIS
       Add-on: PHP, Perl, Python, cache tools on several levels
     SMTP address, antispam
     Antivirus
     Web Application Server: TomCat, ….
G   A full server can be customized, so that any other tool
    can be installed as well from the user

                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   18
Approaches for Virtualization
G   Hardware Emulation
     Fully emulation of Hardware devices and features
     It is possible to use an original Operating system without changes, may be
      with some drivers installed, but not kernel changes
     Higher isolation among VMs, strong robustness, limited efficiency.
     Used by VMware
     Typically 10% of overhead
G   Paravirtualization (total or only on some devices)
     The execution is performed via specific API and the hosted Operating system
      has to be modified to use them instead of the original HW
     Lower isolation, higher efficiency,
     Lower robustness: VM crashes may crash the whole system
     Used by: HP-VM, Xen (both of them which can also go in emulation mode)
     Used by VMware: VMXNET (100 Gbps net), PVSCSI in vSphere4
     Typically 2% of overhead



                               Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   19
Para-virtualization
G   It is intended as a solution to cut out a part of the I/O stack,
    for example for the HD access or network.
G   The following example is related to HP VM v. 3.5




                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   20
Implementing Host Profiles
           G Host   Profile
        Memory Reservation
        Storage
        Networking
        Date and Time
        Firewall
        Security
        Services
        Users and User
         Groups
        Security




     I Reference     Host                                                         I Cluster


D1

                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012        21
“Speeds and Feeds”
Optimization for the Highest Consolidation Ratios

  Virtual Machines          VM Scale Up                                   8-way vSMP and 255 GB of
                         Virtual hardware scale out                       RAM per VM
 APP     APP
 OSAPP   OSAPP    APP
   OS      OS     OS
                                                                          64 cores and 512GB of
          ESX              Hardware Scale Up                              physical RAM



                            Hardware Assist
                                                                          Lowest CPU overhead
                            Purpose Built Scheduler

            CPU             Hardware Assist
                                                                          Maximum memory efficiency
                            Page Sharing

            Memory          Ballooning

                           VMXNET3                                        Wirespeed network access
                           VMDirectPath I/O
            Networking
                                                                          Greater than 360k iops per second
                           Storage stack optimization                     Lower than 20 microsecond latency
                           VMDirectPath I/O
            Storage
                            Current          NEW



                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012                          22
ESX 4.0 Performance with
                    SQL Server 2008
     Relative Scaling Ratio                                         G ESX   achieves 90%
VM                               147.24
                                                                      of native performance on
Native                                    133.12                      4.0 vCPU VM

                 94.04                                              G Workload  transaction
                         79.88
                                                                      latency unchanged
51.08
                                                                      between ESX 4.0
         45.22                                                        and Native



  1 vCPU           2 vCPU           4 vCPU



                                          Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   23
Virtual Resources, 1/2
G   The idea of Virtual Resources (CPU, Mem, HD, net.) consists in
    providing a number of resources larger than those that physically
    available and manage them virtually and/or dynamically
G   For example, one Host may have 2 VM and HW resources:
     2 cores at 1300 Mhz
                                                          VM1:
     1.8 Gbyte RAM
                                                          I 1 CPU 1300 Mhz, 400 Mhz reserved
     2 network cards
                                                          I 1 Gbyte RAM max, 400 Mbyte
     Best case:                                            reserved
        CPU=400+800 Mhz                                  I 2 network cards, 2 IPs

        RAM=400+400 Mbyte
        2 Network cards shared                           VM2:
                                                          I 2 CPU 1300 Mhz, 800 Mhz reserved
     Worst case  no resources enough:
                                                          I 1 Gbyte RAM max, 400 Mbyte
        CPU=3 cores at 1300 Mhz                            reserved
        RAM=2 Gbyte                                      I 2 network cards, 2 IPs
        2 Network cards shared

                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012       24
Virtual Resources, 2/2
G   The server hosting the VMs                               VM1:
                                                             I 1 CPU 1300 Mhz, 400 Mhz reserved
     Min Mhz:
                                                             I 1 Gbyte RAM max, 400 Mbyte
       400Mhz + 800Mhz                                        reserved
     Max Mhz:                                               I 2 network cards, 2 IPs

       1300Mhz + 2*1300Mhz
                                                             VM2:
     Min RAM:                                               I 2 CPU 1300 Mhz, 800 Mhz reserved
       400Mbyte + 400Mbye                                   I 1 Gbyte RAM max, 400 Mbyte

     Max RAM:                                                 reserved
                                                             I 2 network cards, 2 IPs
       1000Mbyte + 1000Mbye
     Network:
       No limits on the number of
         virtual IP addresses/cards




                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012         25
Limiting VM Resources
G   VM Resources (CPU, Mem, HD, net.) consists also in providing
    support for:
     Dinamically providing resources over the reserved values that can
      be negotiation into the SLA/ contract.


G   Controlling and limiting access and the exploitation of HW
    resources:
     A limit on the number of CPUs
       A limit on the number of Clocks, over of a reserved number of
          clocks
     A limit on the maximum size of the RAM, over of the reserved
      number of Mbytes
     A limit on the size of the HD, SAN/NAS access
     A limit on the number of network cards, number of Mbps, etc.

                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   26
Virtual Machine: Snapshots
G   Working on VM Snapshots
     Creating a Snapshot:
       A point from which it is possible to reboot, restart
       Consuming HD space
       Making back up since the core image of the VM is not
         changed, changes are confined in the files representing the
         last status “you are here” and not the previous conditions
     Restarting from a past snapshot
       Losing current point: “you are here”, to avoid this do
         another snapshot!!
     Deleting a past snapshot
       Recovering HD space, removing a past restarting point
     Removing all snapshots
G   Defragmenting images of the HDs into the VM

                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   27
VM Snapshots
G   VM Snapshots can be at VM Off or ON
     Snapshots of running VM have implications…
G   Removing Snapshots
     Defragmenting images of the HDs into the VM
     Consolidating the changes in the previous version


G   VMware WS has an automated Snapshot model to plan the
    periodic snapshotting of the VM, for example:
     every hour, day, week, …


G   A way to make back up
     A different way can be to clone the VM on different host or
      NAS. In most cases, the cloning implies the lost of performed
      snapshots

                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   28
START   Virtual Machine: Snapshots, 1/3

                                                               YOU are
                                                                here




                   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   29
START   Virtual Machine: Snapshots, 2/3




                                    YOU are
                                     here




                   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   30
START   Virtual Machine: Snapshots, 3/3




                                                                              YOU are
                                                                               here




                   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012             31
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
     cloud vs grid
     goals of cloud computing
     soluzioni as a Service
G   High Availability
G   vSphere Infrastructure
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions, SaaS Solutions, PaaS Solutions


                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   32
Cloud Computing
G   A Cloud: a type of parallel and distributed system consisting
    of a collection of interconnected and virtualized computers,
    VMs
     They are dynamically provisioned and presented as one or more unified
      computing resources
     based on service-level agreements, SLA, established through
      negotiation between the service provider and consumers
G   Subset of grid computing where the allocated process are
    virtual computers
G   Implies
       An alternative way to have local servers or GRIDs
       Outsourcing: HW and SW tools, they may be grid elements
       Outsourcing: network, CPU, memory, HD, etc.
       Definition of some service agreement, monthly rate, minimum network
        capability, kind of HW, mem space, minimum level of CPU/mem, etc.



                               Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   33
Abstraction of Cloud Computing, (Buyya & Yeo)




                Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   34
Cloud vs GRID computing
G   Cloud computing is an evolution of GRID computing
G   Renting a GRID service means:
     Parallelize the algorithm, demand the execution, wait for the results, etc.
     To do no know where the processes are executed. In most cases batch
      processing such as on Globus
G   Renting a VM/Computer into a Cloud means:
     Simple contracts, remote access to servers
       Get access to virtual or physical resources at 100%
     Privacy of the allocated processes in your VM/Host
     Processes running on your preferred OS and do not have to be recompiled
      as in most Grid solutions.
     Scalability in terms of number of CPU, Mem, network performance,
      computers, etc. OR you have to change your OWN architecture
       Simple creation and reconfiguration of multitier solutions
            • Creating fault tolerant solutions
            • Balancing load of CPU, Memory, network, etc.

                                Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   35
Cloud vs SuperComputers
G   Cloud Computing is based on a set of Hosts for hosting VM or
    providing access to single/multiple Servers fully at disposal of
    the customers accessing to the cloud via remote access (see
    later).
     They are MIMD computers
     Hosts may contains hypervisors to host VM of diff. OSs.
     Hosts may be single computers with Linux, Windows,…
G   Super Computers, such as Blue Gene/P:
       Processor PowerPC 450 with 4 cores, 850 Mhz
       Each board: 32 CPU processors
       Each Rack: 32 boards
       Jülich Research Centre in Germania has a Blue
        Gene with 65536 processors  167 teraFlops,
        the strongest computer in the world!


                               Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   36
Comparing Computing Services




         Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   37
Comparing Computing Services
G   In the previous table, different solution for computing
    services in the network are compared.

G   They are mainly: cloud computing, grid services,
    application services,

G   The solutions taken have been selected as
    representative of their category
     Please see the slides on GRID for more details and a wider
      comparison on the grid solutions
     Please see the slides regarding the general distributed
      systems for multitier applications




                        Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   38
Advantages of Cloud Coumputing
G   Grids are difficult to use and to maintain:
     GRID customers have too many different needs that make the
      creation of fully open grid very difficult.
G   Cloud computing
     HW/SW is hosting virtual computers that can be moved to other
      solutions with low costs
     Lower costs since the HW/SW is seen as a service
       No maintenance, centralized services such as back up,
          scaling, etc.
     Lower costs for Small Business:
       Reduction of costs since the admortment can be performed by
          who is exploiting the computer
     Scalability similar to grid:
       Horizontal scaling
       Parallelization as MIMD

                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   39
Goals of Cloud Computing
G   Scalability.
     scaling with workload demands so that performance and compliance
      with service levels remain on target
G   Availability.
     users of Internet applications expect them to be up and running every
      minute of every day, i.e.: h24, 24/7
G   Reliability
     physical system components rarely fail, but it happen. So that, they
      can be replaced without disruption.
     Today, reliability means that applications do not fail and most
      importantly they do not lose data, and the service is not stopped.
G   Security.
     Applications need to provide access only to authorized, authenticated
      users, that need to be able to trust that their data is secure.


                             Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   40
Goals of Cloud Computing
G   Flexibility and agility:
     Adapt rapidly to changes of business conditions by increasing
      the velocity at which applications are delivered into customer
      hands. E.g.: more CPU, more clock, more memory, more
      network cards, etc.
G   Serviceability:
     In the past this meant using servers that could be repaired
      without, or with minimal, downtime.
     Today it means that an application’s underlying infrastructure
      components can be updated or even replaced without
      disrupting its characteristics including availability and security.
G   Efficiency:
     differentiates the cloud computing. The process allocation and
      costs have to be very effective with respect to the investment.


                             Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   41
Definizioni
G   Classificazione NIST
      Software as a Service (SaaS)
      Platform as a Service (PaaS)
      Infrastructure as a Service
       (IaaS)

G   Business Process as a Service
    (BPaaS)
     Aggiunto in seguito


G   Everything as a Service (XaaS)


                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   42
Infrastructure as a Service (IaaS)
G   erogazione di servizi infrastrutturali relativi a capacità
    elaborativa, storage, rete e altri elementi di base
    assolutamente indipendenti da servizi applicativi di
    qualunque tipo.
G   Si utilizza quindi l’infrastruttura messa a disposizione dal
    provider per eseguire la propria applicazione,
     pagamento in base al consumo dell’infrastruttura
     lasciando sotto la responsabilità dell’utente la gestione del
      sistema operativo, dell’eventuale middleware e della parte
      di runtime, oltre che dell’applicazione stessa.
G   Amazon EC2 è un esempio di servizio IaaS.




                         Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   43
Platform as a Service (PaaS)
G   erogazione di servizi applicativi di base come sistemi
    operativi, middleware, linguaggi, tecnologie di base dati e
    l’ambiente runtime necessari per eseguire l’applicazione,
G   L’applicazione rimane l’unica cosa sotto la responsabilità
    dell’utente, oltre alla definizione del modello (e.g.,
    numero e dimensione dei server, datacenter,
    caratteristiche del networking) da utilizzare per
    l’esecuzione dell’applicazione.
G   Google AppEngine è un esempio di Platform as a
    Service.
G   A livello PaaS viene anche collocato l’insieme dei servizi
    MaaS, Middleware as a Service.




                       Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   44
Software as a Service (SaaS)
G   erogazione di servizi applicativi di qualunque tipo, accessibili
    indipendentemente dalla collocazione e dal tipo di device
    utilizzato.
G   Non è eseguita un’applicazione proprietaria del cliente, ma il
    cliente stesso paga il diritto (mediante licenza o canone di affitto)
    di utilizzo di un’applicazione messa a disposizione dal provider,
    senza preoccuparsi di come essa venga realizzata e gestita nel
    cloud.
G   L’unica preoccupazione del cliente in questo caso, oltre
    ovviamente alla scelta della corretta applicazione che soddisfi le
    sue necessità, è gestire il numero di licenze richieste in funzione
    del numero di utenti.
G   SalesForce.com Customer Relationship Management (CRM) è
    un esempio di soluzione in cui il software è venduto in modalità
    as a service.
                             Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   45
Business Process as a Service (BPaaS):
G   erogazione di servizi non esclusivamente riferiti ad ambiti
    applicativi ma direttamente alle funzionalità di business o
    di processo, potenzialmente trasversali rispetto alle
    piattaforme applicative.
G   un processo di business mappato interamente nel cloud
    (composto da servizi, applicazioni web, applicazioni
    legacy, servizi di integrazione, etc.).
G   Il processo di business è un pattern di servizi ed include
    problemi di sicurezza, costi, scalabilità connessione fra
    local e cloud bidirezionale, il cloud può essere un burst
    per l’azienda, e può sgravare i costi nel momento del
    bisogno.




                       Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   46
Modello Generale




   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   47
Definizioni
G   Private Cloud. abilitata per operare soltanto per un’organizzazione.
    può essere gestita dalla stessa organizzazione o da parte di terzi.
G   Community Cloud. condivisa da più organizzazioni a supporto di una
    singola community che ha interessi e obbiettivi comuni. Questa può
    essere gestita dalle stesse organizzazioni o da terzi in modalità on-
    premise e off-premise.
G   Public Cloud. resa disponibile in maniera pubblica ed è di proprietà di
    un organizzazione che vi gestisce la vendita di servizi cloud.
G   Hybrid Cloud. Infrastruttura composizione di due o più cloud (siano
    essi private, community o pubblici), rimangono entità separate, ma
    comunque accomunate da standard o tecnologie proprietarie che
    abilitano un certo livello di portabilità di dati e/o applicazioni di
    migrazione e/o bursting.




                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   48
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
     Workload Balancing
     RAID on HD
     SAN/NAS
G   vSphere Infrastructure
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions, SaaS Solutions, PaaS Solutions


                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   49
High Availability
G   The high availability has to be guaranteed only
    by the integration of features of:
      High Reliability
      High Serviceability
      Fault tolerance
      Migration of VM to different HW
      Disaster recovering




                     Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   50
High Availability
G   High Availability,
    available 99.999 %
    (called “Five Nines”)
    percent of the time.

G   Five Nines is the term
    for saying a service or
    system will be up almost
    100 percent of the time.

G   In case of failure:
      the path changes to
       guarantee the service



                               Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   51
High Availability
G   May be achieved by redundancy of :
        Servers and Services in hot spare or balancing
          • VM based architectures
        HW/SW: power supply, network connections, etc.

     Load Balancing/Balancer according to server traffic/requests
       Server Clustering, multitier solution
     Hot Spare:
       Server: cloned server to be used when the main is not
         functioning: heartbeat to detect the server availability and thus
         failover. (heartbeat signal to communicate the correct running
         of a process/CPU)
       HD: Raid based on SAN/NAS, LUN, in host
     Mixed: balancing and hot spare

                             Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   52
High Availability: Load Balancing
G   Service distributed/cloned on more servers
     According to different policies
       Round robin
       Network traffic
G   Single transfer rate capability on the front-
    end of the load balancer
G   Sensing the availability of the server
    on balancer +
     heartbeat solutions to
      understand if the servers are
      alive or not
G   Common NAS/SAN

G   That is a Cluster of servers on the cloud !

                                Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   53
Balancing multi-tier, 3-tier




        Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   54
High Availability: Hot spare
G   Fully cloned servers to be
    alternatively used when the
    running one fails
G   Internal Network
     Heartbeat to detect the server
      availability and thus failover.
     [To keep servers aligned on
      context and data]
G   Shared data storage is a
    simplification and optional.
     A different solution may be to
      have a cloned storage to keep
      aligned among the two servers



                               Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   55
High Availability: Hot spare, hw
G   Three separate networks cards
     Front end
     Heartbeat
     Database NAS/SAN
G   UPS/APC solutions with
     2 UPS, each of which with
      network card
G   NAS/SAN
     Raid 5 or 6, 60
     Fiber connection




                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   56
Network and Virtual Networks


                                  G      The same VM with access
                                         to 2 different network via
                                         real network adapters

                                  G      A virtual network




         Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012     57
Partitioning the database
G   The database may be
     stored in a single NAS
     stored into a distributed solution by
      partitioning the tables horizontally
      or vertically


G   Horizontal partitioning of a table:
     According to a certain primary key
     Distributing the table on different
      servers




                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   58
HD RAID

                                    I RAID          0: HD0+HD1



                                    I RAID          1: HD0 cloned on HD1




                                    I RAID  5: gestione del guasto
                                    di un disco
                                        I valido fino a 14
                                        I per la doppia parità
                                              RAID 6



Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012              59
An Example of RAID 50




G   RAID 50: A1,A2,A3,A4,A5,A6 and three Parities
     Min 9HD, can support failures of three HDs
     improves upon the performance of RAID 5
G   In this case:
     Size: 6 over 9 HD, you lose a HD per R5
     Read/Write speed up: n(m − 1)XHD
        n=3, m=3  6x in this case


                          Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   60
SAN vs NAS
G   In most cases they are wrongly considered the same staff
     It is true that: NAS/SAN typically are HD in Raid connected/shared
      to/by Servers
G   NAS: Network Area Storage
     Network HD sharing content at level of File via HTTP, NFS, CIFS,
      etc., protocols
     Multiple servers may access to them
     Reduced performances
G   SAN: Storage Area Network
     HD system segmented in LUN, mounted by the Server/VM
      operating system at level of disk block
     The Operating System has to format with it own file system
     Possible protocols: low level IP, iSCSI, Fiber, etc.



                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   61
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure
       Vmotion
       Power Management
       Resource Scheduling
       Fault Tolerance
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions, SaaS Solutions, PaaS Solutions

                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   62
vSphere 4 infrastructure of VMware
G   High level features:
       HA: high availability
       DRS: Distributed Resource Scheduling
       Creating Fault Tolerance architectures
       DPM: datacenter power management, based on VMotion
       Converting VM into VM for infrastructure, from physical to VM
       vApp: are Virtual Application Services
       Cloning and Moving VM
       Making Templates for VM
       Backup VM and thus virtual servers
G   Low level features:
     VMotion: VM moving among Hosts
     Dynamic increment of: CPU ck, MEM, net…


                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   63
Summary of
 VMware
vSphere 4.0




              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   64
VMotion of
     VMware vSphere
G   you need:
     VM without snapshots
     VM must be powered off to
      simultaneously migrate both
      host and datastore
     Compatibility among host CPU
      and VMs
     Dedicated virtual network
     The VMs can be ON
G   Steps:
    1. moving HD images
    2. aligning OS and CPU status
    3. off-the old and then on-new

                             Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   65
VMware DPM, Power Management
G   DPM consolidates workloads to reduce power consumption
      Cuts power and cooling costs
      Automates management of energy efficiency
      optimizing host resources
G   Based on VMotion




                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   66
VMware DRS, Distrib. Res. Scheduling
G  DRS is used to balance the workload among Hosts
G Moving VMs is a tools for balancing the workload
on Hosts
G ..




                   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   67
VMware Fault Tolerance, FT
Single identical VMs running in lockstep on separate hosts
Zero downtime, zero data loss failover for all virtual machines in case
of hardware failures
Single common mechanism for all applications and Operating
systems
Need to have a storage shared
by the same VM
The VM itself can be
stored in the SAN                  APP                                  APP           APP
                                    OS                                  OS            OS

                                                             VMware vSphere™




                                                              SAN/NAS
                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012         68
How VMware FT Works

          Primary                                                          Secondary
  Virtual Machine                                                          Virtual Machine


VMkernel            VMM                                      VMM                     VMkernel
           Log Update?                                             Log Read?
                             Record Logs
           Log Buffer                                             Log Buffer
                                 Heartbeat?

            Read/Write                                           Read




              Single Copy of Disks on Shared Storage (SAN)



                          Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012              69
HA: High Avalability of vSphere
G   When a host fails, the
    running VM on the host
    may be turned ON on
    another hosts
     Just the time to turn on
      again the host
G   HOT Spare solution:
     It is also possible to
      keep aligned 2 distinct
      hosts to make a faster
      switch OFFON of the
      VM on the faulty host
     implies to have
      duplicated resources:
      Host, CPU etc.

                                 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   70
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions, SaaS Solutions, PaaS Solutions




                          Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   71
Security on the cloud
G   Protecting VMs from external access
G   Protecting VMs each other in the cloud
G   Technologies:
      Accessing to other VM via dedicated virtual
       networks, using Virtual Networking, Virtual Switch
      Avoiding shared disk, at least using authenticated
       connections
      Using Firewall
      Communicating with other VMs via protected
       connections: protected WS, HTTPs, SSL, SFTP,
       etc.


                     Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   72
Virtual Networking
G   Per isolare meglio dei guest
    OS da altri che non sono
    nello stesso trust domain
G   isolare i virtual switch dei
    singoli trust domain.
G   Solo i guest che condividono
    lo stesso domain hanno
    schede di rete virtuali sullo
    stesso virtual switch.
G   virtual switch su porte logiche
    del sistema host che non
    hanno indirizzo ip
    configurato.


                               Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   73
Vmware Vsphere VMsafe
                                                      Security VM
                                                                                         • HIPS
                                                                                         • Firewall
                                                                                         • IPS/IDS
                    Security API
                                                                                         • Anti-Virus
                    ESX Server




 Creates a new, stronger layer of defense – fundamentally changes
  protection profile for VMs running on VMware Infrastructure
 Protect the VM by inspection of virtual components (CPU, Memory,
  Network and Storage)
 Complete integration and awareness of VMotion, Storage VMotion, HA,
  etc.
 Provides an unprecedented level of security for the application and the
  data inside the VM


                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012                  74
VMware vSphere
                                                              Data Recovery
                                                                Backup Only




   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012                   75
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions, SaaS Solutions, PaaS Solutions




                          Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   76
VM Converter, the migration
G   Conversion possibilities, migration possibilities

G   P2V, from Physical  Virtual machine
     Reusing legacy servers into stronger and new HW machines
     From ISO CD of an OS  VM


G   V2V, from Virtual  to Virtual
       Import/export a VM from/to different standards
       From VM Workstation  Infrastructure VM
       From Infrastructure VM  template for VM with some parameters
       From Infrastructure VM VM Workstation
       From Infrastructure VM  Infrastructure VM changing parameters
       …..


                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   77
P2V, vConverter vSphere
                      Virtual to be
                      converted


                                                               Converter on
                                                               vSphere Client
  Physical To be virtualized
  with VMware workstation



                                                                                                            Helper
                                                                                                        ….is becomes
                                                                                                         the new VM



Physical to be virtualized                Converter
Hosting the: Convert                  Stand Alone, Client
Server

                                         Data, disks, …




                                             Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012                  78
P2V, OpenCD vConverter vSphere

        Boot from CD with
        OpenCD Converter
      containing the: Convert                        Converter on
        Stand Alone Client                           vSphere Client
       and Converter Server




                                                                                                  Helper
                                                                                              ….is becomes
                                Data, disks, …                                                 the new VM
Physical
To be virtualized

Cloning HD physically:
     -No resize (+-)
     -No partitions
     -RAID drivers needed




                                   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012                  79
P2V conversion
G   How to convert, typically:
     From VM Converter Stand Alone on Windows or Linux
       it includes a Server side and a client consol,
           • server side has to be installed on the P machine to be
             converted.
           • Client side is a consol
       From Linux to VM on ESX Host
       From Windows to CM on ESX Host
     From VM converter as plugin of vCenter, via vCenter Client
       From Windows to VM on ESX Host
     From OpenCD, with included VM Converter and alone
       From Windows to CM on ESX Host
     From VMware Workstation
       From Windows to CM on ESX Host


                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   80
P2V conversion
G   Hypothesis:
     Converter server installed on Physical machine
     Converter in third machine or on the physical (windows)
     All snapshots are removed and the disks re-compacted.
G   First step:
     The Converter Client is launched
     The Converter Client creates an Helper VM on the vCenter Host
G   Second step:
     The Helper VM works independently to transfer the data from VM
      Converter Server to the new VM via the Helper VM, under the
      control of Client.
     The Converter Client provides commands to close and destroy the
      Helper VM and put in execution the new VM with the final shape



                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   81
P2V conversion
G   Third step:
     Reboot the new machine as Virtual machine
        Then install the VMware tools on the VM from the vCenter or
           from the VMware Workstation hypervisor.
        Revise the general config, network, etc.
        See if it possible to use paravirtualized drivers instead of
           physical drivers.
     If the converted VM is not booting
        Verify the VM setting: disk, operating system, disk drivers, etc.
        Regenerate the VM tools on the VM and try again




                             Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   82
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions, SaaS Solutions, PaaS Solutions


                      Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   83
vCenter vSphere
G   The so called Inventory
G   Datacenter
     Cluster00
       Host….
       Host….
       Application:
           • VM1
           • VM2
       VM3
       VM4
     Cluster01
       H2..
       …



                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   84
Some Snapshots of
                             vSphere




Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   85
Storage View




 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   86
A Larger Datacenter




     Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   87
Partially Collapsed with Separate Physical
               Trust Zones




               Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   88
Monitoring the Solution
G   Monitoring and assessing performance at level of:
     Datacenter, Cluster, Host
     Virtual Machine from outside

     Virtual Machine inside:
       this has to be performed by using tools inside the VM
         operating system
       Windows:
           • System monitoring hosts, detailed performances
       Linux:
           • Top or other tools


                         Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   89
Performance Analysis, Cluster/DC




           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   90
Datacenter space consumed again time




            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   91
Performance Analysis, Single VM




           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   92
Performance Analysis, Single VM




           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   93
Performance Analysis, Single VM




           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   94
Performances Analysis of VM on the Host




               Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   95
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions, SaaS Solutions, PaaS Solutions


                      Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   96
Virtualization Solutions
G   VMware vSphere
      Datacenter and VM management, a large range of OS
G   Microsoft Hyper-V
      Based on Microsoft Windows Server 2008
G   HP Integrity
    G Datacenter and VM management, x86, linux, etc.
    G No GUI for monitoring
G   XEN
     Virtual machine monitor, hypervisor: Suse, RedHat, Sun Solaris, Debian
     X86, 64, PowerPC 970
     GPL licensing
G   others
     SWsoft with its Virtuozzo
     IBM Power VM
     VirtualBox of Sun, open source

                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   97
Comparison (www.ctistrategy.com)




           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   98
Comparison, 2




  Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   99
Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   100
Standards in Virtual Machine formats
G   Virtual Machine formats on the HD

G   OVF: Open Virtualization Format, on the push of VMware
     Format for VM


G   VM disk format of VMware is a standard which is
    supported by:
     VMware workstation
     vSphere VMware
     VirtualBox of SUN




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Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure
G   Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions, SaaS Solutions, PaaS Solutions


                      Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   102
How to WORK with VMs in the Cloud
G   KVM solutions
     Local access via local KVM
     Local server access via HTTP
G   Windowing Terminal
     MS Windows Remote Desktop
     X Terminal to linux
G   Remote Solutions:
     VNC, Radmin, etc.
     VNC: Also possible via HTTP port
G   Telnet, char based consol, SSH
     VT100 terminal for example




                        Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   103
Windows Remote Desktop
                          I Remotemachine
                          I Windows 2003




I Localmachine
I Windows Vista




                   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   104
VNC remote connection
                            I Remotemachine
                            I Windows XP




I Localmachine
I Windows Vista




                     Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   105
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure, Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions
G   SaaS Solutions
G   PaaS Solutions

                      Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   108
Modello Generale




   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   109
Infrastructure as a Service (IaaS)
G   erogazione di servizi infrastrutturali relativi a:
      CPU come clock, storage as HD, rete, mem
      altri elementi di base assolutamente indipendenti da servizi
       applicativi di qualunque tipo.
G   infrastruttura messa a disposizione dal provider per
    eseguire la propria applicazione:
      Affitto di Server e/o macchine virtuali
      pagamento in base al consumo dell’infrastruttura
      lascia sotto la responsabilità dell’utente la gestione del
       sistema operativo, dell’eventuale middleware e della parte di
       runtime, oltre che dell’applicazione stessa.
G   Amazon EC2 è un esempio di servizio IaaS.



                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   110
IaaS
G   Una serie di Host, VM e NAS/SAN
G   Un gestore di macchine virtuali
G   Un sistema per il deploy basato su templates
G   Un sistema per l’accounting e il deploy automatico
G   Alcuni servizi per l’accesso diretto a Host e VM




                       Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   111
IaaS Solutions
G   Based on Virtualization
G   A set of Datacenters, Hosts, NAS/SAN, networks, etc.
G   IaaS commerciali come
     Amazon’s Elastic Compute Cloud (EC2) and Simple Storage Service
      (S3). Amazon EC2 è il leader del mercato dei provider IasS.
G   Come Technology provider di soluzioni IaaS si hanno:
       Oracle,
       VMware: Vcenter, Vsphere, etc.;
       Nimbus;
       OpenNebula;
       HP;
       Eucalyptus; Ubuntu Enterprise Cloud (basato su Eucalyptus);
       GoGrid;
       Flexiscale;
       UNISYS,
       Enterprise Cloud Manager, ECM, on Hybrid.

                              Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   112
Service Model                                                                                      IaaS




                                                                         Hosting.com




                                                                                                                                                                             OpenNebula
                                                                                       NephoScale
                                      BitRefinery




                                                                                                                                                                Eucalyptus
                                                                                                               Rackspace

                                                                                                                           ReliaCloud



                                                                                                                                                    Terremark
                                                                                                    OpSource
                                                      GoDaddy




                                                                                                                                        Softlayer
                             Amazon




                                                                                                                                                                                          Nimbus
                                                                GoGrid
            Infrastructure




                              EC2
Feature

Pay As You Go                  x                        x        x                       x           x           x                        x           x
Dynamic Service Level
Agreement
Certificazioni (e.g., PCI
                               x      x                                    x                         x           x           x            x           x
o SAS 70)
Scale Up                              x                 x        x         x             x           x           x                                    x
Scale Out                      x      x                 x        x         x             x           x           x           x            x           x           x            x           x
Live Support                          x                 x        x         x             x           x           x           x
Monitoring Tools               x                        x                  x                         x           x                        x                                    x
APIs                           x                                 x                       x           x           x           x            x           x           x            x           x
Free Tier                      x                                                         x                                                                        x            x
Highly customizable
                                      x                                    x                         x                                                x
istances
Cloud Burst                                                                                                                                                                    x

                                                    Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012                                                                                       113
IaaS confronto (legenda)
G   Pay per use – se si paga a consumo, in realtà molti provider hanno una filigrana di offerte
    più ampia, includendo anche piani mensili, sconti e promozioni ecc.
G   Dynamic SLA – se viene offerta la possibilità di ridefinire gli SLA.
G   Certifications – se il provider offer certificazioni sulla compliance/sicurezza come PCI o
    SAS 70.
G   Scale Up – se è possibile lo scale up di single istanze di server, tramite l’aggiunta di
    memoria, extra CPU o storage.
G   Scale Out – se è possibile fare il deploy veloce di nuove istanze dei server.
G   Live Support – può essere diviso in:
      Poor (n) – solo forum di supporto for free; in alternativa a pagamento.
      Average (y) – supporto 24×7 gratis (telefono, chat, forum).
      Extensive (y) – offerte di supporto multiplo per ogni soluzione proposta.
G   Monitoring – può essere diviso in:
      Poor (n) – nessuna soluzione di monitoring/alert integrata, sono necessari strumenti di terze parti
       da acquistare separatamente.
      Average (y) – strumenti di monitoring minimali e senza servizi di alert.
      Extensive (y) – soluzioni complete e integrate di strumenti di monitoring compresi nel prezzo.
G   APIs – se vi sono API per interagire con i server.
G   Free Tier – se vengono offerte soluzioni di prova per il test dei servizi

                                         Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012        114
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure, Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions
G   SaaS Solutions
G   PaaS Solutions

                      Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   115
Platform as a Service (PaaS)
G   erogazione di servizi applicativi di base come:
     sistemi operativi, middleware, linguaggi,
     tecnologie di base dati
     ambiente runtime necessari per eseguire l’applicazione,
G   rimane sotto la responsabilità dell’utente,
     applicazione
     la definizione del modello (e.g., numero e dimensione dei
      server, datacenter, caratteristiche del networking) da utilizzare
      per l’esecuzione dell’applicazione.
G   Google AppEngine è un esempio di Platform as a Service.
G   A livello PaaS viene anche collocato l’insieme dei servizi
    MaaS, Middleware as a Service.



                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   116
Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   117
PaaS
G   Middleware per accesso ai servizi di base
     Middleware as a Service, MaaS
     API per accesso a questi servizi
G   Sistema di sviluppo per applicazioni che possono usare
    le API
G   Servizi e risorse base

G   Business Process Modeling:
     permette definire come allocare e configurare le
      macchine virtuali ed i servizi sul cloud.
G   Processo di lavoro




                         Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   118
Appliant Process Modeler




       Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   119
Apache Hadoop
G   framework che supporta applicazioni distribuite
G   applicazioni distribuite con elevato accesso ai dati
G   Hadoop può lavorare direttamente con qualsiasi file system
    distribuito, che possa essere montato da un sistema
    operativo sottostante semplicemente usando un file:// URL




                         Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   120
Alcuni esempi di PaaS
G   Google App Engine (https://appengine.google.com/) detto
    anche GAE
G   Windows Azure Platform
    (http://www.microsoft.com/windowsazure/)
G   Force (http://www.salesforce.com/platform/ )
G   Oracle Fusion Middleware
    (http://www.oracle.com/it/products/middleware/index.html)
G   Eccentex AppBase
    (http://www.eccentex.com/platform/features.html)
G   3Tera AppLogic (http://www.ca.com/us/cloud-platform.aspx)




                          Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   121
Service Model                                                                         SaaS




                                                                                                                            Salesforce
                                                                                       Dynamics




                                                                                                                 RightNow
                                                                                       Microsoft
                                                                   NetSuite
                                                Aplicor
                                  Software




                                                                                                                              .com
Feature

Campaign Management
                                                  x                  x                    x                          x          x
Lead management                                   x                  x                    x                          x          x
Event Management                                  x                                                                  x
Pay Per Click (PPC) integration
                                                  x                  x                                                          x
E-mail con trackable link e click-through
                                                  x                  x                    x                          x          x
Account Management                                x                  x                    x                          x          x
Contact Management                                x                  x                    x                          x          x
Opportunity Management                            x                  x                    x                          x          x
Competitive Intelligence                          x
Sales Analytics                                   x                  x                    x                          x          x
Data Deduplication                                                   x
Ticket/case/incident management                   x                  x                    x                          x          x
Email to case creation                            x                                                                  x          x
Routing                                           x                  x                    x                          x          x
Escalation                                        x                  x                    x                          x          x
Customer Surveys                                  x                  x                                               x          x
Knowledge base                                    x                  x                                               x
Self Service Portal                               x                  x                    x                          x          x
Dashboard                                         x                  x                    x                          x          x
Ad Hoc Report Writer                              x                  x                    x                          x          x
Data Warehouses                                   x                                                                             x
OLAP                                              x                                       x                                     x
Groupware Integration                             x                  x                    x                          x          x
Partner relationship management
                                                  x                  x                                               x
E-commerce Suite                                                     x
Sales Order Processing                           x                   x
Workflow Designer                                x                                                                   x
Offline Edition                                  x                   x                                                          x
Enterprise Resource Planning                     x                   x                    x
Certifications                               ISO, NIST     SAS70                                                            SAS70
SLA with Guarantee                                x


                                                          Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012                       122
Structure
G   elementi di cloud Computing
G   La Virtualizzazione
G   Cloud Computing
G   High Availability
G   vSphere Infrastructure, Security on the Cloud
G   Conversions among VM and physical machines
G   vCenter, datacenters and cluster management
G   Comparison among virtual computing solutions
G   How to work with Virtual Machines
G   IaaS solutions
G   SaaS Solutions
G   PaaS Solutions

                      Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   123
Software as a Service (SaaS)
G   erogazione di servizi applicativi accessibili indipendentemente
    dalla collocazione e dal tipo di device utilizzato.
G   Non sono eseguite applicazioni del cliente
     il cliente paga il diritto (mediante licenza o canone di affitto) di
      utilizzo di un’applicazione messa a disposizione dal provider, senza
      preoccuparsi di come essa venga realizzata e gestita nel cloud.


G   Il cliente deve scegliere la corretta applicazione che soddisfi le
    sue necessità

G   SalesForce.com Customer Relationship Management (CRM) è
    un esempio di soluzione in cui il software è venduto in modalità
    as a service.



                             Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   124
Architetture SaaS
G   Interfacce esterne
G   L’insieme di API, rappresentano i metodi con cui le
    applicazioni possono venir relazionate con altri servizi o
    software,
      Cloud support for mobile, come Apple
      Push service come Apple
G   Middleware è lo strumento che permette all’applicazione
    di sfruttare tutti i servizi e le risorse di livello più basso




                         Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   125
Soluzioni SaaS
G   Google Apps (Google) - Google offre gratuitamente ai privati i
    propri servizi applicativi come:
     Google Documents, GMail, Calendars, Sites.
G   Oracle CRM On Demand in grado di supportare tipologie di
    offerta sia in multi tenancy sia in single tenancy
G   SalesForce.com (Salesforce) - Customer Relationship Manager.
G   Zoho Office (Zoho) - Propone un insieme di applicazioni mirate
    alle aziende. Il portfolio di servizi prevede più di 20 applicazioni
    tra cui ad esempio posta elettronica, office app CRM
G   IBM LotusLive (IBM) - Consiste di una raccolta di soluzioni di
    collaborazione aziendale online e servizi di social networking.




                            Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   126
GAP: Google App Engine




       Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   127
GAP: Google App Engine
G   Datastore: un database non relazionale distribuito e scalabile,
    accessibile attraverso un set di API e gestibile attraverso il pannello di
    controllo dell’applicazione. Il servizio di basa su BigTable, un sistema
    proprietario per l’archiviazione di dati strutturati.
G   Google accounts: un set di API che permette l’autenticazione di utenti
    attraverso account Google.
G   URL Fetch: API che permette di ricavare risorse sul web.
G   Mail: API che permette l’invio di e-mail.
G   Memcache: sistema di caching di tipo chiave-valore. I contenuti di tale
    cache sono condivisi tra le varie istanze in esecuzione dell’applicazione.
G   Image manipulation: API utilizzabile per la manipolazione di immagini.
G   Tasks: Lo sviluppatore ha inoltre la possibilità di schedulare task
    secondo determinati orari o utilizzare una coda di task per permettere
    alla propria applicazione di eseguire delle operazioni di background
    mentre delle richieste web vengono servite.


                                Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   128
SalesForce              Java, .NET, Ruby,                                                   SOQL, SOSL        REST
          Force.com               PHP, Python, Perl,
                                                                                                                                           129




                                  Adobe Flex e AIR
           Red Hat                 Java, Java EE,        Multi IaaS                 Git,                MySQL,          REST           x
           OpenShift              Python, Perl, PHP,      Provider                 SSH,                MongoDB,
                                        Ruby            (AWS, etc.)                rsync               MemBase,
                                                                                                       Memcache
          Rackspace                PHP, ASP, .NET      Multi-tenant    Rackspa     FTP/S             MySQL 5 & MS       object         x
          Cloud Sites                                  PaaS            ce Cloud     FTP             SQL Server 2008    storage/
                                                                       Files/Aka                                         CDN
                                                                          mai
           Microsoft                C#, Java, PHP,
           Windows                                                                                                      REST
                                         Ruby
            Azure
           Jelastic                                                                                 Maria DB, MySQL,
                                                                                                                                           Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012
                                                                                   Mave
                                                                                                      PostgreSQL,
                                        Java                                       n, Git,                                             x
                                                                                                       MongoDB,
                                                                                    SVN
                                                                                                        CouchDB
            Heroku
                                                                                                     Amazon RDS
                                                                                                       (MySQL),
                                        Ruby
                                                                                                    MongoDB, Redis,
                                                                                                       CouchDB
 PaaS




          Google App                                                                                  Google Cloud     JDBC,
                                     Java, Python
            Engine                                                                                       SQL           DB-API
          CumuLogic                                      Amazon
                                                          EC2,
                                                       OpenStack,
                                                                                   Git,              MySQL, MySQL
                                     Java, Spring      CloudStack,                                                      REST           x
                                                                                   SVN              cluster, MongoDB
                                                       Eucalyptus,
                                                         VMware
                                                         vSphere
           Cloudify                                      deploy su
                                     Java, .NET,
                                                          qualsiasi                                   Cassandra,        CLI,
                                  Groovy, Ruby, C++,
                                                       IaaS e BYON                  Git               MongoDB,         REST,           x
                                   Node.JS, Spring
                                                        (bring-your-                                 MySQL, HSQL        Web
                                     (more soon)
                                                        own-nodes)
                        Java Spring,
          CloudFound   Groovy Grails,                                                                 MongoDB,
                                                                                    Git                                  CLI
              ry     Ruby Rails Sinatra,                                                             MySQL, Redis
                          Node.js
                                                                                   Mave
                                                                                                                       REST,
          CloudBees                     Java                                       n, Git,              MySQL                          x
                                                                                                                        CLI
                                                                                    SVN
             AWS
            Elastic                     Java            AWS Cloud
           Beanstalk




                                                                                                                                  Dashboard/C
                                                                                     Repositories




                                                                                                                                  centralizzata
                                          Tecnologie
Service




                                                                                                           Databases
Model

             Platform




                                                                       Network
                                                                       Delivery
                                                                       Content
                        Feature




                                                           hosting




                                                                                                                                  onsole
                                                                       (CDN)
                                                           Cloud




                                                                                                                          APIs
References
G   VMware: http://www.vmware.com
G   HP: www.hp.com/go/integrityvm
G   Microsoft Hyper-V: http://www.microsoft.com/hyper-v-
    server/en/us/default.aspx
G   “Windows Server 2008 Hyper-V Technical Overview”-
    http://download.microsoft.com/download/4/2/b/42bea8d6-9c77-4db8-b405-
    6bffce59b157/Hyper-V%20Technical%20Overview.docx
G   Comparison of Hypervisors by VMware -
    http://www.vmware.com/technology/whyvmware/architectures.html#c132894
G   “VMware vSphere 4” Datasheet - http://www.vmware.com/products/vsphere/



G   All the pictures representing VMware staff have been taken from Vmware library and
    are under rights of VMware. These slides represents only the view of the author and not
    that of VMware or of other companies mentioned in the slides such as HP, microsoft or
    others. The slides are only provided for didactical purpose and no for profit.


                                   Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   130
Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   131
Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012   132

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CLoud computing, virtualization, scalability, media computing, course

  • 1. Sistemi Distribuiti Corso di Laurea in Ingegneria Prof. Paolo Nesi Parte 17: Cloud Computing &Virtualization Department of Systems and Informatics University of Florence Via S. Marta 3, 50139, Firenze, Italy tel: +39-055-4796523, fax: +39-055-4796363 Lab: DISIT, Sistemi Distribuiti e Tecnologie Internet nesi@dsi.unifi.it, paolo.nesi@unifi.it www: http://www.dsi.unifi.it/~nesi Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 1
  • 2. Structure G elementi di cloud Computing  Motivazioni  Definizioni G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions G SaaS Solutions G PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 2
  • 3. Motivations for Cloud computing and Virtualization G Physical systems are  not flexible: The HW/SW has to be dimensioned for the worst case and not for the real/typical one It can be hardly reused for different purposes  subjected to HW failure: Software has to be installed again when moving hardware OS has to be reinstalled in most cases  Subjected to high costs of setup: Power, conditioning, network connection, etc.  non scalable, to scale frequently implies hardware restructuring duplication of data, distribution of activities among HW, Balancing or workload, sharing databases, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 3
  • 4. Final Motivations G Reduction of costs for HW and operating system SW maintenance.  High costs for high availability  High costs for high reliability  High costs to follow the HW trends G Sharing resources G Needs of High flexibility in terms of features  The sofware is becoming a services and not anymore a product. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 4
  • 5. Motivations for Applications on the Cloud G Single tier applications such as small web portals do not demand a cloud G Multitier application servers  High performance web server, high number of users  SN: Social network  CMS: content management systems  CRM: customer relationship management  CDN: Content delivering network  P2P torrent tracker: see Piratebay G … G All typically parallel applications that may run on grid G …. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 5
  • 6. Datacenter, definition G Datacenter  A computer factory/farm in which servers/computers are hosted and organized: power, net maintenance, etc. In most cases industrial computers, blades  They can be exploited for private purposes, grid, cloud computing, renting/hosting, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 6
  • 7. Clusters, definition G Cluster is typically intended:  Several kinds of computers and/or VMs may be used to compose a cluster, sharing the same domain or not.  a set of computers/VMs into a datacenter which are dedicated to a unique problem, for example: A microgrid…………… A social network………. A web portal with its multi-tier servers: front-end portals, balancer, database computers, backofficer microgrid nodes, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 7
  • 8. Infrastructure, definition G A set of datacenter and clusters G A set of  NAS: Network Area Storage  SAN: Storage Area Network G Etc.. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 8
  • 9. Typical Features for renting a Computer or a Virtual Machine into a Datacenter G Requesting to have one or more Computers/Hosts and/or VMs G Hardware:  CPU: 32/64 bit, number of cores/CPU, frequency of work, intel/amd, etc.  RAM memory: size and frequency of work  Power supply: fault-tolerant or not; with UPS or not, etc..  HD: space, speed (7.2, 15Kgiri), security level/RAID, type SAS/SATA, SCSI  Network features: Number of connections/cards, number of IP addresses, static/DHCP Transfer rate: minimum guaranteed, maximum possible, down/upload Maximum transferred bytes: per day, per month, etc.  NAS/SAN, Network area storage/SAN, fiber/internet: size, RAID, etc. G Software:  Operating systems  Software preinstalled into the Computer/VM, see in the following G Services:  Periodic back up: details on HD space  Access to VM/Computer: remote desktop or KVM tool  Reboot or not of the Server, for example via Plesk. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 9
  • 10. Hosting, definition G HPC: high performance computing  Solution based on cloud/grid for parallel execution of algorithms and tools. G Hosting web portal into a datacenter/cluster  Renting a web space via some SLA (service level agreement), contract, monthly rate to publishing web pages: service httpd  Additional services: mysql, php, asp, ftp, ssh, https, etc..  Features: Space on disk, networking Domain space, etc. G Hosting a machine (computer/VM) into a datacenter  According to some SLA, contract…  Renting a Computer/VM/Cluster into a data center Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 10
  • 11. From Clustering VM to Cloud G The first step has been the exploitation of unused resources to provide them as a service to third party. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 11
  • 12. Motivations for Cloud computing and Virtualization G Most of the datacenters capabilities are not exploited at 100% in every time instant  They are typically present in large industries/institutions or in large services: google, amazon, tiscali, dada, ibm, cnr, etc  Their size is typically defined on the basis of the worst case G If they are big: the exploitation of the remaining resources for cloud/hosting is a solution to recover money, since a non working machine (like a caw) is a costs without any return. G If they are small: it could be a solution to host machines on a professional infrastructure to reduce annual costs for HW/SW  Delegating to cluster owners the costs for maintenance, renovating hardware, renovating software, network costs, back up costs, power supply, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 12
  • 13. Structure G elementi di cloud Computing G La Virtualizzazione  emulation, para-virtualization  virtual resources  snapshots G Cloud Computing G High Availability G vSphere Infrastructure G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions, SaaS Solutions, PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 13
  • 14. Virtual Machine, Virtualization G Virtual Machine:  An image of an operating system that can be put in execution into a real host/computer creating a virtual computer that exploits a part of all of the host resources G E.g.: Host may be Linux-like while the VM may be Window, Mac, Linux, … G Virtualization  Transforming a physical computer into a VM, virtual machine, hosted on some Host computer G Hypervisor (VM Monitor) to manage the several VMs on the host Computer 1 Virtual Virtual Machine Machine Hypervisor Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 14
  • 15. Benefits of Virtual Machine G Main benefits:  Separation of OS/SW with respect to the needed HW  Exploiting legacy solutions which can be wrapped into a VM and protected with a physical firewall without reinstalling and recompiling old applications. G For example:  An old Linux Server hosting several web portals with a old versions of: MySQL, PHP, etc.. and many configuration aspects: users, mailing lists, etc. very time consuming to port on a new server  An old Cobol application running only on an old Windows 2000 Server, which cannot be recompiled into a new Windows Server 2008 at 64 bits without spending months of work.  A Cobol application running on machine based on an IBM system 36….  Etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 15
  • 16. VM on PC workstations G On a single Computer it is possible to put in execution a VM by using a standalone Hypervisor G VMware Workstation can play on Windows:  VMware Virtual Machines with: MS Win, Linux, Mac, ChromeOS, etc. G VMware Player can play on Windows or Linux  Free of charge  VMware Virtual Machines with: MS Win, Linux, Mac, ChromeOS, etc G VMware Fusion can play on MAC:  VMware Virtual Machines with: MS Win, Win 7, ChromeOS, etc. G Microsoft Virtual PC on Windows 7:  Free of charge  Create VM with Win XP, Linux Ubuntu, Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 16
  • 17. Cloud Computing with VMs Several Hypervisors on a Clusters internet Computer 1 Computer 2 …………… Virtual Virtual Virtual Virtual Machine Machine Machine Machine Hypervisor Hypervisor Virtual cluster manager Virtualized Infrastructure --- control servers Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 17
  • 18. Software/Components preinstalled into rented Computers/VMs G Several kinds of software components/tools that can be accessible on rented VM and/or Computers. G Availability of HW/SW: for example at 98% or 99.999% G Typical components that could be requested:  DB: MySQL  FTP: server and client  Web Server: Apache, IIS Add-on: PHP, Perl, Python, cache tools on several levels  SMTP address, antispam  Antivirus  Web Application Server: TomCat, …. G A full server can be customized, so that any other tool can be installed as well from the user Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 18
  • 19. Approaches for Virtualization G Hardware Emulation  Fully emulation of Hardware devices and features  It is possible to use an original Operating system without changes, may be with some drivers installed, but not kernel changes  Higher isolation among VMs, strong robustness, limited efficiency.  Used by VMware  Typically 10% of overhead G Paravirtualization (total or only on some devices)  The execution is performed via specific API and the hosted Operating system has to be modified to use them instead of the original HW  Lower isolation, higher efficiency,  Lower robustness: VM crashes may crash the whole system  Used by: HP-VM, Xen (both of them which can also go in emulation mode)  Used by VMware: VMXNET (100 Gbps net), PVSCSI in vSphere4  Typically 2% of overhead Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 19
  • 20. Para-virtualization G It is intended as a solution to cut out a part of the I/O stack, for example for the HD access or network. G The following example is related to HP VM v. 3.5 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 20
  • 21. Implementing Host Profiles G Host Profile  Memory Reservation  Storage  Networking  Date and Time  Firewall  Security  Services  Users and User Groups  Security I Reference Host I Cluster D1 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 21
  • 22. “Speeds and Feeds” Optimization for the Highest Consolidation Ratios Virtual Machines VM Scale Up 8-way vSMP and 255 GB of Virtual hardware scale out RAM per VM APP APP OSAPP OSAPP APP OS OS OS 64 cores and 512GB of ESX Hardware Scale Up physical RAM Hardware Assist Lowest CPU overhead Purpose Built Scheduler CPU Hardware Assist Maximum memory efficiency Page Sharing Memory Ballooning VMXNET3 Wirespeed network access VMDirectPath I/O Networking Greater than 360k iops per second Storage stack optimization Lower than 20 microsecond latency VMDirectPath I/O Storage Current NEW Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 22
  • 23. ESX 4.0 Performance with SQL Server 2008 Relative Scaling Ratio G ESX achieves 90% VM 147.24 of native performance on Native 133.12 4.0 vCPU VM 94.04 G Workload transaction 79.88 latency unchanged 51.08 between ESX 4.0 45.22 and Native 1 vCPU 2 vCPU 4 vCPU Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 23
  • 24. Virtual Resources, 1/2 G The idea of Virtual Resources (CPU, Mem, HD, net.) consists in providing a number of resources larger than those that physically available and manage them virtually and/or dynamically G For example, one Host may have 2 VM and HW resources:  2 cores at 1300 Mhz VM1:  1.8 Gbyte RAM I 1 CPU 1300 Mhz, 400 Mhz reserved  2 network cards I 1 Gbyte RAM max, 400 Mbyte  Best case: reserved CPU=400+800 Mhz I 2 network cards, 2 IPs RAM=400+400 Mbyte 2 Network cards shared VM2: I 2 CPU 1300 Mhz, 800 Mhz reserved  Worst case  no resources enough: I 1 Gbyte RAM max, 400 Mbyte CPU=3 cores at 1300 Mhz reserved RAM=2 Gbyte I 2 network cards, 2 IPs 2 Network cards shared Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 24
  • 25. Virtual Resources, 2/2 G The server hosting the VMs VM1: I 1 CPU 1300 Mhz, 400 Mhz reserved  Min Mhz: I 1 Gbyte RAM max, 400 Mbyte 400Mhz + 800Mhz reserved  Max Mhz: I 2 network cards, 2 IPs 1300Mhz + 2*1300Mhz VM2:  Min RAM: I 2 CPU 1300 Mhz, 800 Mhz reserved 400Mbyte + 400Mbye I 1 Gbyte RAM max, 400 Mbyte  Max RAM: reserved I 2 network cards, 2 IPs 1000Mbyte + 1000Mbye  Network: No limits on the number of virtual IP addresses/cards Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 25
  • 26. Limiting VM Resources G VM Resources (CPU, Mem, HD, net.) consists also in providing support for:  Dinamically providing resources over the reserved values that can be negotiation into the SLA/ contract. G Controlling and limiting access and the exploitation of HW resources:  A limit on the number of CPUs A limit on the number of Clocks, over of a reserved number of clocks  A limit on the maximum size of the RAM, over of the reserved number of Mbytes  A limit on the size of the HD, SAN/NAS access  A limit on the number of network cards, number of Mbps, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 26
  • 27. Virtual Machine: Snapshots G Working on VM Snapshots  Creating a Snapshot: A point from which it is possible to reboot, restart Consuming HD space Making back up since the core image of the VM is not changed, changes are confined in the files representing the last status “you are here” and not the previous conditions  Restarting from a past snapshot Losing current point: “you are here”, to avoid this do another snapshot!!  Deleting a past snapshot Recovering HD space, removing a past restarting point  Removing all snapshots G Defragmenting images of the HDs into the VM Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 27
  • 28. VM Snapshots G VM Snapshots can be at VM Off or ON  Snapshots of running VM have implications… G Removing Snapshots  Defragmenting images of the HDs into the VM  Consolidating the changes in the previous version G VMware WS has an automated Snapshot model to plan the periodic snapshotting of the VM, for example:  every hour, day, week, … G A way to make back up  A different way can be to clone the VM on different host or NAS. In most cases, the cloning implies the lost of performed snapshots Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 28
  • 29. START Virtual Machine: Snapshots, 1/3 YOU are here Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 29
  • 30. START Virtual Machine: Snapshots, 2/3 YOU are here Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 30
  • 31. START Virtual Machine: Snapshots, 3/3 YOU are here Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 31
  • 32. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing  cloud vs grid  goals of cloud computing  soluzioni as a Service G High Availability G vSphere Infrastructure G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions, SaaS Solutions, PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 32
  • 33. Cloud Computing G A Cloud: a type of parallel and distributed system consisting of a collection of interconnected and virtualized computers, VMs  They are dynamically provisioned and presented as one or more unified computing resources  based on service-level agreements, SLA, established through negotiation between the service provider and consumers G Subset of grid computing where the allocated process are virtual computers G Implies  An alternative way to have local servers or GRIDs  Outsourcing: HW and SW tools, they may be grid elements  Outsourcing: network, CPU, memory, HD, etc.  Definition of some service agreement, monthly rate, minimum network capability, kind of HW, mem space, minimum level of CPU/mem, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 33
  • 34. Abstraction of Cloud Computing, (Buyya & Yeo) Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 34
  • 35. Cloud vs GRID computing G Cloud computing is an evolution of GRID computing G Renting a GRID service means:  Parallelize the algorithm, demand the execution, wait for the results, etc.  To do no know where the processes are executed. In most cases batch processing such as on Globus G Renting a VM/Computer into a Cloud means:  Simple contracts, remote access to servers Get access to virtual or physical resources at 100%  Privacy of the allocated processes in your VM/Host  Processes running on your preferred OS and do not have to be recompiled as in most Grid solutions.  Scalability in terms of number of CPU, Mem, network performance, computers, etc. OR you have to change your OWN architecture Simple creation and reconfiguration of multitier solutions • Creating fault tolerant solutions • Balancing load of CPU, Memory, network, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 35
  • 36. Cloud vs SuperComputers G Cloud Computing is based on a set of Hosts for hosting VM or providing access to single/multiple Servers fully at disposal of the customers accessing to the cloud via remote access (see later).  They are MIMD computers  Hosts may contains hypervisors to host VM of diff. OSs.  Hosts may be single computers with Linux, Windows,… G Super Computers, such as Blue Gene/P:  Processor PowerPC 450 with 4 cores, 850 Mhz  Each board: 32 CPU processors  Each Rack: 32 boards  Jülich Research Centre in Germania has a Blue Gene with 65536 processors  167 teraFlops, the strongest computer in the world! Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 36
  • 37. Comparing Computing Services Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 37
  • 38. Comparing Computing Services G In the previous table, different solution for computing services in the network are compared. G They are mainly: cloud computing, grid services, application services, G The solutions taken have been selected as representative of their category  Please see the slides on GRID for more details and a wider comparison on the grid solutions  Please see the slides regarding the general distributed systems for multitier applications Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 38
  • 39. Advantages of Cloud Coumputing G Grids are difficult to use and to maintain:  GRID customers have too many different needs that make the creation of fully open grid very difficult. G Cloud computing  HW/SW is hosting virtual computers that can be moved to other solutions with low costs  Lower costs since the HW/SW is seen as a service No maintenance, centralized services such as back up, scaling, etc.  Lower costs for Small Business: Reduction of costs since the admortment can be performed by who is exploiting the computer  Scalability similar to grid: Horizontal scaling Parallelization as MIMD Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 39
  • 40. Goals of Cloud Computing G Scalability.  scaling with workload demands so that performance and compliance with service levels remain on target G Availability.  users of Internet applications expect them to be up and running every minute of every day, i.e.: h24, 24/7 G Reliability  physical system components rarely fail, but it happen. So that, they can be replaced without disruption.  Today, reliability means that applications do not fail and most importantly they do not lose data, and the service is not stopped. G Security.  Applications need to provide access only to authorized, authenticated users, that need to be able to trust that their data is secure. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 40
  • 41. Goals of Cloud Computing G Flexibility and agility:  Adapt rapidly to changes of business conditions by increasing the velocity at which applications are delivered into customer hands. E.g.: more CPU, more clock, more memory, more network cards, etc. G Serviceability:  In the past this meant using servers that could be repaired without, or with minimal, downtime.  Today it means that an application’s underlying infrastructure components can be updated or even replaced without disrupting its characteristics including availability and security. G Efficiency:  differentiates the cloud computing. The process allocation and costs have to be very effective with respect to the investment. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 41
  • 42. Definizioni G Classificazione NIST  Software as a Service (SaaS)  Platform as a Service (PaaS)  Infrastructure as a Service (IaaS) G Business Process as a Service (BPaaS)  Aggiunto in seguito G Everything as a Service (XaaS) Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 42
  • 43. Infrastructure as a Service (IaaS) G erogazione di servizi infrastrutturali relativi a capacità elaborativa, storage, rete e altri elementi di base assolutamente indipendenti da servizi applicativi di qualunque tipo. G Si utilizza quindi l’infrastruttura messa a disposizione dal provider per eseguire la propria applicazione,  pagamento in base al consumo dell’infrastruttura  lasciando sotto la responsabilità dell’utente la gestione del sistema operativo, dell’eventuale middleware e della parte di runtime, oltre che dell’applicazione stessa. G Amazon EC2 è un esempio di servizio IaaS. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 43
  • 44. Platform as a Service (PaaS) G erogazione di servizi applicativi di base come sistemi operativi, middleware, linguaggi, tecnologie di base dati e l’ambiente runtime necessari per eseguire l’applicazione, G L’applicazione rimane l’unica cosa sotto la responsabilità dell’utente, oltre alla definizione del modello (e.g., numero e dimensione dei server, datacenter, caratteristiche del networking) da utilizzare per l’esecuzione dell’applicazione. G Google AppEngine è un esempio di Platform as a Service. G A livello PaaS viene anche collocato l’insieme dei servizi MaaS, Middleware as a Service. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 44
  • 45. Software as a Service (SaaS) G erogazione di servizi applicativi di qualunque tipo, accessibili indipendentemente dalla collocazione e dal tipo di device utilizzato. G Non è eseguita un’applicazione proprietaria del cliente, ma il cliente stesso paga il diritto (mediante licenza o canone di affitto) di utilizzo di un’applicazione messa a disposizione dal provider, senza preoccuparsi di come essa venga realizzata e gestita nel cloud. G L’unica preoccupazione del cliente in questo caso, oltre ovviamente alla scelta della corretta applicazione che soddisfi le sue necessità, è gestire il numero di licenze richieste in funzione del numero di utenti. G SalesForce.com Customer Relationship Management (CRM) è un esempio di soluzione in cui il software è venduto in modalità as a service. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 45
  • 46. Business Process as a Service (BPaaS): G erogazione di servizi non esclusivamente riferiti ad ambiti applicativi ma direttamente alle funzionalità di business o di processo, potenzialmente trasversali rispetto alle piattaforme applicative. G un processo di business mappato interamente nel cloud (composto da servizi, applicazioni web, applicazioni legacy, servizi di integrazione, etc.). G Il processo di business è un pattern di servizi ed include problemi di sicurezza, costi, scalabilità connessione fra local e cloud bidirezionale, il cloud può essere un burst per l’azienda, e può sgravare i costi nel momento del bisogno. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 46
  • 47. Modello Generale Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 47
  • 48. Definizioni G Private Cloud. abilitata per operare soltanto per un’organizzazione. può essere gestita dalla stessa organizzazione o da parte di terzi. G Community Cloud. condivisa da più organizzazioni a supporto di una singola community che ha interessi e obbiettivi comuni. Questa può essere gestita dalle stesse organizzazioni o da terzi in modalità on- premise e off-premise. G Public Cloud. resa disponibile in maniera pubblica ed è di proprietà di un organizzazione che vi gestisce la vendita di servizi cloud. G Hybrid Cloud. Infrastruttura composizione di due o più cloud (siano essi private, community o pubblici), rimangono entità separate, ma comunque accomunate da standard o tecnologie proprietarie che abilitano un certo livello di portabilità di dati e/o applicazioni di migrazione e/o bursting. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 48
  • 49. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability  Workload Balancing  RAID on HD  SAN/NAS G vSphere Infrastructure G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions, SaaS Solutions, PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 49
  • 50. High Availability G The high availability has to be guaranteed only by the integration of features of:  High Reliability  High Serviceability  Fault tolerance  Migration of VM to different HW  Disaster recovering Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 50
  • 51. High Availability G High Availability, available 99.999 % (called “Five Nines”) percent of the time. G Five Nines is the term for saying a service or system will be up almost 100 percent of the time. G In case of failure:  the path changes to guarantee the service Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 51
  • 52. High Availability G May be achieved by redundancy of : Servers and Services in hot spare or balancing • VM based architectures HW/SW: power supply, network connections, etc.  Load Balancing/Balancer according to server traffic/requests Server Clustering, multitier solution  Hot Spare: Server: cloned server to be used when the main is not functioning: heartbeat to detect the server availability and thus failover. (heartbeat signal to communicate the correct running of a process/CPU) HD: Raid based on SAN/NAS, LUN, in host  Mixed: balancing and hot spare Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 52
  • 53. High Availability: Load Balancing G Service distributed/cloned on more servers  According to different policies Round robin Network traffic G Single transfer rate capability on the front- end of the load balancer G Sensing the availability of the server on balancer +  heartbeat solutions to understand if the servers are alive or not G Common NAS/SAN G That is a Cluster of servers on the cloud ! Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 53
  • 54. Balancing multi-tier, 3-tier Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 54
  • 55. High Availability: Hot spare G Fully cloned servers to be alternatively used when the running one fails G Internal Network  Heartbeat to detect the server availability and thus failover.  [To keep servers aligned on context and data] G Shared data storage is a simplification and optional.  A different solution may be to have a cloned storage to keep aligned among the two servers Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 55
  • 56. High Availability: Hot spare, hw G Three separate networks cards  Front end  Heartbeat  Database NAS/SAN G UPS/APC solutions with  2 UPS, each of which with network card G NAS/SAN  Raid 5 or 6, 60  Fiber connection Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 56
  • 57. Network and Virtual Networks G The same VM with access to 2 different network via real network adapters G A virtual network Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 57
  • 58. Partitioning the database G The database may be  stored in a single NAS  stored into a distributed solution by partitioning the tables horizontally or vertically G Horizontal partitioning of a table:  According to a certain primary key  Distributing the table on different servers Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 58
  • 59. HD RAID I RAID 0: HD0+HD1 I RAID 1: HD0 cloned on HD1 I RAID 5: gestione del guasto di un disco I valido fino a 14 I per la doppia parità  RAID 6 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 59
  • 60. An Example of RAID 50 G RAID 50: A1,A2,A3,A4,A5,A6 and three Parities  Min 9HD, can support failures of three HDs  improves upon the performance of RAID 5 G In this case:  Size: 6 over 9 HD, you lose a HD per R5  Read/Write speed up: n(m − 1)XHD  n=3, m=3  6x in this case Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 60
  • 61. SAN vs NAS G In most cases they are wrongly considered the same staff  It is true that: NAS/SAN typically are HD in Raid connected/shared to/by Servers G NAS: Network Area Storage  Network HD sharing content at level of File via HTTP, NFS, CIFS, etc., protocols  Multiple servers may access to them  Reduced performances G SAN: Storage Area Network  HD system segmented in LUN, mounted by the Server/VM operating system at level of disk block  The Operating System has to format with it own file system  Possible protocols: low level IP, iSCSI, Fiber, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 61
  • 62. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure  Vmotion  Power Management  Resource Scheduling  Fault Tolerance G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions, SaaS Solutions, PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 62
  • 63. vSphere 4 infrastructure of VMware G High level features:  HA: high availability  DRS: Distributed Resource Scheduling  Creating Fault Tolerance architectures  DPM: datacenter power management, based on VMotion  Converting VM into VM for infrastructure, from physical to VM  vApp: are Virtual Application Services  Cloning and Moving VM  Making Templates for VM  Backup VM and thus virtual servers G Low level features:  VMotion: VM moving among Hosts  Dynamic increment of: CPU ck, MEM, net… Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 63
  • 64. Summary of VMware vSphere 4.0 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 64
  • 65. VMotion of VMware vSphere G you need:  VM without snapshots  VM must be powered off to simultaneously migrate both host and datastore  Compatibility among host CPU and VMs  Dedicated virtual network  The VMs can be ON G Steps: 1. moving HD images 2. aligning OS and CPU status 3. off-the old and then on-new Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 65
  • 66. VMware DPM, Power Management G DPM consolidates workloads to reduce power consumption  Cuts power and cooling costs  Automates management of energy efficiency  optimizing host resources G Based on VMotion Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 66
  • 67. VMware DRS, Distrib. Res. Scheduling G DRS is used to balance the workload among Hosts G Moving VMs is a tools for balancing the workload on Hosts G .. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 67
  • 68. VMware Fault Tolerance, FT Single identical VMs running in lockstep on separate hosts Zero downtime, zero data loss failover for all virtual machines in case of hardware failures Single common mechanism for all applications and Operating systems Need to have a storage shared by the same VM The VM itself can be stored in the SAN APP APP APP OS OS OS VMware vSphere™ SAN/NAS Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 68
  • 69. How VMware FT Works Primary Secondary Virtual Machine Virtual Machine VMkernel VMM VMM VMkernel Log Update? Log Read? Record Logs Log Buffer Log Buffer Heartbeat? Read/Write Read Single Copy of Disks on Shared Storage (SAN) Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 69
  • 70. HA: High Avalability of vSphere G When a host fails, the running VM on the host may be turned ON on another hosts  Just the time to turn on again the host G HOT Spare solution:  It is also possible to keep aligned 2 distinct hosts to make a faster switch OFFON of the VM on the faulty host  implies to have duplicated resources: Host, CPU etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 70
  • 71. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions, SaaS Solutions, PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 71
  • 72. Security on the cloud G Protecting VMs from external access G Protecting VMs each other in the cloud G Technologies:  Accessing to other VM via dedicated virtual networks, using Virtual Networking, Virtual Switch  Avoiding shared disk, at least using authenticated connections  Using Firewall  Communicating with other VMs via protected connections: protected WS, HTTPs, SSL, SFTP, etc. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 72
  • 73. Virtual Networking G Per isolare meglio dei guest OS da altri che non sono nello stesso trust domain G isolare i virtual switch dei singoli trust domain. G Solo i guest che condividono lo stesso domain hanno schede di rete virtuali sullo stesso virtual switch. G virtual switch su porte logiche del sistema host che non hanno indirizzo ip configurato. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 73
  • 74. Vmware Vsphere VMsafe Security VM • HIPS • Firewall • IPS/IDS Security API • Anti-Virus ESX Server  Creates a new, stronger layer of defense – fundamentally changes protection profile for VMs running on VMware Infrastructure  Protect the VM by inspection of virtual components (CPU, Memory, Network and Storage)  Complete integration and awareness of VMotion, Storage VMotion, HA, etc.  Provides an unprecedented level of security for the application and the data inside the VM Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 74
  • 75. VMware vSphere Data Recovery Backup Only Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 75
  • 76. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions, SaaS Solutions, PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 76
  • 77. VM Converter, the migration G Conversion possibilities, migration possibilities G P2V, from Physical  Virtual machine  Reusing legacy servers into stronger and new HW machines  From ISO CD of an OS  VM G V2V, from Virtual  to Virtual  Import/export a VM from/to different standards  From VM Workstation  Infrastructure VM  From Infrastructure VM  template for VM with some parameters  From Infrastructure VM VM Workstation  From Infrastructure VM  Infrastructure VM changing parameters  ….. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 77
  • 78. P2V, vConverter vSphere Virtual to be converted Converter on vSphere Client Physical To be virtualized with VMware workstation Helper ….is becomes the new VM Physical to be virtualized Converter Hosting the: Convert Stand Alone, Client Server Data, disks, … Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 78
  • 79. P2V, OpenCD vConverter vSphere Boot from CD with OpenCD Converter containing the: Convert Converter on Stand Alone Client vSphere Client and Converter Server Helper ….is becomes Data, disks, … the new VM Physical To be virtualized Cloning HD physically: -No resize (+-) -No partitions -RAID drivers needed Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 79
  • 80. P2V conversion G How to convert, typically:  From VM Converter Stand Alone on Windows or Linux it includes a Server side and a client consol, • server side has to be installed on the P machine to be converted. • Client side is a consol From Linux to VM on ESX Host From Windows to CM on ESX Host  From VM converter as plugin of vCenter, via vCenter Client From Windows to VM on ESX Host  From OpenCD, with included VM Converter and alone From Windows to CM on ESX Host  From VMware Workstation From Windows to CM on ESX Host Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 80
  • 81. P2V conversion G Hypothesis:  Converter server installed on Physical machine  Converter in third machine or on the physical (windows)  All snapshots are removed and the disks re-compacted. G First step:  The Converter Client is launched  The Converter Client creates an Helper VM on the vCenter Host G Second step:  The Helper VM works independently to transfer the data from VM Converter Server to the new VM via the Helper VM, under the control of Client.  The Converter Client provides commands to close and destroy the Helper VM and put in execution the new VM with the final shape Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 81
  • 82. P2V conversion G Third step:  Reboot the new machine as Virtual machine Then install the VMware tools on the VM from the vCenter or from the VMware Workstation hypervisor. Revise the general config, network, etc. See if it possible to use paravirtualized drivers instead of physical drivers.  If the converted VM is not booting Verify the VM setting: disk, operating system, disk drivers, etc. Regenerate the VM tools on the VM and try again Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 82
  • 83. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions, SaaS Solutions, PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 83
  • 84. vCenter vSphere G The so called Inventory G Datacenter  Cluster00 Host…. Host…. Application: • VM1 • VM2 VM3 VM4  Cluster01 H2.. … Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 84
  • 85. Some Snapshots of vSphere Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 85
  • 86. Storage View Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 86
  • 87. A Larger Datacenter Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 87
  • 88. Partially Collapsed with Separate Physical Trust Zones Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 88
  • 89. Monitoring the Solution G Monitoring and assessing performance at level of:  Datacenter, Cluster, Host  Virtual Machine from outside  Virtual Machine inside: this has to be performed by using tools inside the VM operating system Windows: • System monitoring hosts, detailed performances Linux: • Top or other tools Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 89
  • 90. Performance Analysis, Cluster/DC Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 90
  • 91. Datacenter space consumed again time Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 91
  • 92. Performance Analysis, Single VM Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 92
  • 93. Performance Analysis, Single VM Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 93
  • 94. Performance Analysis, Single VM Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 94
  • 95. Performances Analysis of VM on the Host Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 95
  • 96. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions, SaaS Solutions, PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 96
  • 97. Virtualization Solutions G VMware vSphere  Datacenter and VM management, a large range of OS G Microsoft Hyper-V  Based on Microsoft Windows Server 2008 G HP Integrity G Datacenter and VM management, x86, linux, etc. G No GUI for monitoring G XEN  Virtual machine monitor, hypervisor: Suse, RedHat, Sun Solaris, Debian  X86, 64, PowerPC 970  GPL licensing G others  SWsoft with its Virtuozzo  IBM Power VM  VirtualBox of Sun, open source Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 97
  • 98. Comparison (www.ctistrategy.com) Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 98
  • 99. Comparison, 2 Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 99
  • 100. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 100
  • 101. Standards in Virtual Machine formats G Virtual Machine formats on the HD G OVF: Open Virtualization Format, on the push of VMware  Format for VM G VM disk format of VMware is a standard which is supported by:  VMware workstation  vSphere VMware  VirtualBox of SUN Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 101
  • 102. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure G Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions, SaaS Solutions, PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 102
  • 103. How to WORK with VMs in the Cloud G KVM solutions  Local access via local KVM  Local server access via HTTP G Windowing Terminal  MS Windows Remote Desktop  X Terminal to linux G Remote Solutions:  VNC, Radmin, etc.  VNC: Also possible via HTTP port G Telnet, char based consol, SSH  VT100 terminal for example Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 103
  • 104. Windows Remote Desktop I Remotemachine I Windows 2003 I Localmachine I Windows Vista Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 104
  • 105. VNC remote connection I Remotemachine I Windows XP I Localmachine I Windows Vista Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 105
  • 106. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure, Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions G SaaS Solutions G PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 108
  • 107. Modello Generale Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 109
  • 108. Infrastructure as a Service (IaaS) G erogazione di servizi infrastrutturali relativi a:  CPU come clock, storage as HD, rete, mem  altri elementi di base assolutamente indipendenti da servizi applicativi di qualunque tipo. G infrastruttura messa a disposizione dal provider per eseguire la propria applicazione:  Affitto di Server e/o macchine virtuali  pagamento in base al consumo dell’infrastruttura  lascia sotto la responsabilità dell’utente la gestione del sistema operativo, dell’eventuale middleware e della parte di runtime, oltre che dell’applicazione stessa. G Amazon EC2 è un esempio di servizio IaaS. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 110
  • 109. IaaS G Una serie di Host, VM e NAS/SAN G Un gestore di macchine virtuali G Un sistema per il deploy basato su templates G Un sistema per l’accounting e il deploy automatico G Alcuni servizi per l’accesso diretto a Host e VM Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 111
  • 110. IaaS Solutions G Based on Virtualization G A set of Datacenters, Hosts, NAS/SAN, networks, etc. G IaaS commerciali come  Amazon’s Elastic Compute Cloud (EC2) and Simple Storage Service (S3). Amazon EC2 è il leader del mercato dei provider IasS. G Come Technology provider di soluzioni IaaS si hanno:  Oracle,  VMware: Vcenter, Vsphere, etc.;  Nimbus;  OpenNebula;  HP;  Eucalyptus; Ubuntu Enterprise Cloud (basato su Eucalyptus);  GoGrid;  Flexiscale;  UNISYS,  Enterprise Cloud Manager, ECM, on Hybrid. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 112
  • 111. Service Model IaaS Hosting.com OpenNebula NephoScale BitRefinery Eucalyptus Rackspace ReliaCloud Terremark OpSource GoDaddy Softlayer Amazon Nimbus GoGrid Infrastructure EC2 Feature Pay As You Go x x x x x x x x Dynamic Service Level Agreement Certificazioni (e.g., PCI x x x x x x x x o SAS 70) Scale Up x x x x x x x x Scale Out x x x x x x x x x x x x x x Live Support x x x x x x x x Monitoring Tools x x x x x x x APIs x x x x x x x x x x x Free Tier x x x x Highly customizable x x x x istances Cloud Burst x Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 113
  • 112. IaaS confronto (legenda) G Pay per use – se si paga a consumo, in realtà molti provider hanno una filigrana di offerte più ampia, includendo anche piani mensili, sconti e promozioni ecc. G Dynamic SLA – se viene offerta la possibilità di ridefinire gli SLA. G Certifications – se il provider offer certificazioni sulla compliance/sicurezza come PCI o SAS 70. G Scale Up – se è possibile lo scale up di single istanze di server, tramite l’aggiunta di memoria, extra CPU o storage. G Scale Out – se è possibile fare il deploy veloce di nuove istanze dei server. G Live Support – può essere diviso in:  Poor (n) – solo forum di supporto for free; in alternativa a pagamento.  Average (y) – supporto 24×7 gratis (telefono, chat, forum).  Extensive (y) – offerte di supporto multiplo per ogni soluzione proposta. G Monitoring – può essere diviso in:  Poor (n) – nessuna soluzione di monitoring/alert integrata, sono necessari strumenti di terze parti da acquistare separatamente.  Average (y) – strumenti di monitoring minimali e senza servizi di alert.  Extensive (y) – soluzioni complete e integrate di strumenti di monitoring compresi nel prezzo. G APIs – se vi sono API per interagire con i server. G Free Tier – se vengono offerte soluzioni di prova per il test dei servizi Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 114
  • 113. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure, Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions G SaaS Solutions G PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 115
  • 114. Platform as a Service (PaaS) G erogazione di servizi applicativi di base come:  sistemi operativi, middleware, linguaggi,  tecnologie di base dati  ambiente runtime necessari per eseguire l’applicazione, G rimane sotto la responsabilità dell’utente,  applicazione  la definizione del modello (e.g., numero e dimensione dei server, datacenter, caratteristiche del networking) da utilizzare per l’esecuzione dell’applicazione. G Google AppEngine è un esempio di Platform as a Service. G A livello PaaS viene anche collocato l’insieme dei servizi MaaS, Middleware as a Service. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 116
  • 115. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 117
  • 116. PaaS G Middleware per accesso ai servizi di base  Middleware as a Service, MaaS  API per accesso a questi servizi G Sistema di sviluppo per applicazioni che possono usare le API G Servizi e risorse base G Business Process Modeling:  permette definire come allocare e configurare le macchine virtuali ed i servizi sul cloud. G Processo di lavoro Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 118
  • 117. Appliant Process Modeler Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 119
  • 118. Apache Hadoop G framework che supporta applicazioni distribuite G applicazioni distribuite con elevato accesso ai dati G Hadoop può lavorare direttamente con qualsiasi file system distribuito, che possa essere montato da un sistema operativo sottostante semplicemente usando un file:// URL Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 120
  • 119. Alcuni esempi di PaaS G Google App Engine (https://appengine.google.com/) detto anche GAE G Windows Azure Platform (http://www.microsoft.com/windowsazure/) G Force (http://www.salesforce.com/platform/ ) G Oracle Fusion Middleware (http://www.oracle.com/it/products/middleware/index.html) G Eccentex AppBase (http://www.eccentex.com/platform/features.html) G 3Tera AppLogic (http://www.ca.com/us/cloud-platform.aspx) Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 121
  • 120. Service Model SaaS Salesforce Dynamics RightNow Microsoft NetSuite Aplicor Software .com Feature Campaign Management x x x x x Lead management x x x x x Event Management x x Pay Per Click (PPC) integration x x x E-mail con trackable link e click-through x x x x x Account Management x x x x x Contact Management x x x x x Opportunity Management x x x x x Competitive Intelligence x Sales Analytics x x x x x Data Deduplication x Ticket/case/incident management x x x x x Email to case creation x x x Routing x x x x x Escalation x x x x x Customer Surveys x x x x Knowledge base x x x Self Service Portal x x x x x Dashboard x x x x x Ad Hoc Report Writer x x x x x Data Warehouses x x OLAP x x x Groupware Integration x x x x x Partner relationship management x x x E-commerce Suite x Sales Order Processing x x Workflow Designer x x Offline Edition x x x Enterprise Resource Planning x x x Certifications ISO, NIST SAS70 SAS70 SLA with Guarantee x Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 122
  • 121. Structure G elementi di cloud Computing G La Virtualizzazione G Cloud Computing G High Availability G vSphere Infrastructure, Security on the Cloud G Conversions among VM and physical machines G vCenter, datacenters and cluster management G Comparison among virtual computing solutions G How to work with Virtual Machines G IaaS solutions G SaaS Solutions G PaaS Solutions Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 123
  • 122. Software as a Service (SaaS) G erogazione di servizi applicativi accessibili indipendentemente dalla collocazione e dal tipo di device utilizzato. G Non sono eseguite applicazioni del cliente  il cliente paga il diritto (mediante licenza o canone di affitto) di utilizzo di un’applicazione messa a disposizione dal provider, senza preoccuparsi di come essa venga realizzata e gestita nel cloud. G Il cliente deve scegliere la corretta applicazione che soddisfi le sue necessità G SalesForce.com Customer Relationship Management (CRM) è un esempio di soluzione in cui il software è venduto in modalità as a service. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 124
  • 123. Architetture SaaS G Interfacce esterne G L’insieme di API, rappresentano i metodi con cui le applicazioni possono venir relazionate con altri servizi o software,  Cloud support for mobile, come Apple  Push service come Apple G Middleware è lo strumento che permette all’applicazione di sfruttare tutti i servizi e le risorse di livello più basso Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 125
  • 124. Soluzioni SaaS G Google Apps (Google) - Google offre gratuitamente ai privati i propri servizi applicativi come:  Google Documents, GMail, Calendars, Sites. G Oracle CRM On Demand in grado di supportare tipologie di offerta sia in multi tenancy sia in single tenancy G SalesForce.com (Salesforce) - Customer Relationship Manager. G Zoho Office (Zoho) - Propone un insieme di applicazioni mirate alle aziende. Il portfolio di servizi prevede più di 20 applicazioni tra cui ad esempio posta elettronica, office app CRM G IBM LotusLive (IBM) - Consiste di una raccolta di soluzioni di collaborazione aziendale online e servizi di social networking. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 126
  • 125. GAP: Google App Engine Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 127
  • 126. GAP: Google App Engine G Datastore: un database non relazionale distribuito e scalabile, accessibile attraverso un set di API e gestibile attraverso il pannello di controllo dell’applicazione. Il servizio di basa su BigTable, un sistema proprietario per l’archiviazione di dati strutturati. G Google accounts: un set di API che permette l’autenticazione di utenti attraverso account Google. G URL Fetch: API che permette di ricavare risorse sul web. G Mail: API che permette l’invio di e-mail. G Memcache: sistema di caching di tipo chiave-valore. I contenuti di tale cache sono condivisi tra le varie istanze in esecuzione dell’applicazione. G Image manipulation: API utilizzabile per la manipolazione di immagini. G Tasks: Lo sviluppatore ha inoltre la possibilità di schedulare task secondo determinati orari o utilizzare una coda di task per permettere alla propria applicazione di eseguire delle operazioni di background mentre delle richieste web vengono servite. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 128
  • 127. SalesForce Java, .NET, Ruby, SOQL, SOSL REST Force.com PHP, Python, Perl, 129 Adobe Flex e AIR Red Hat Java, Java EE, Multi IaaS Git, MySQL, REST x OpenShift Python, Perl, PHP, Provider SSH, MongoDB, Ruby (AWS, etc.) rsync MemBase, Memcache Rackspace PHP, ASP, .NET Multi-tenant Rackspa FTP/S MySQL 5 & MS object x Cloud Sites PaaS ce Cloud FTP SQL Server 2008 storage/ Files/Aka CDN mai Microsoft C#, Java, PHP, Windows REST Ruby Azure Jelastic Maria DB, MySQL, Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 Mave PostgreSQL, Java n, Git, x MongoDB, SVN CouchDB Heroku Amazon RDS (MySQL), Ruby MongoDB, Redis, CouchDB PaaS Google App Google Cloud JDBC, Java, Python Engine SQL DB-API CumuLogic Amazon EC2, OpenStack, Git, MySQL, MySQL Java, Spring CloudStack, REST x SVN cluster, MongoDB Eucalyptus, VMware vSphere Cloudify deploy su Java, .NET, qualsiasi Cassandra, CLI, Groovy, Ruby, C++, IaaS e BYON Git MongoDB, REST, x Node.JS, Spring (bring-your- MySQL, HSQL Web (more soon) own-nodes) Java Spring, CloudFound Groovy Grails, MongoDB, Git CLI ry Ruby Rails Sinatra, MySQL, Redis Node.js Mave REST, CloudBees Java n, Git, MySQL x CLI SVN AWS Elastic Java AWS Cloud Beanstalk Dashboard/C Repositories centralizzata Tecnologie Service Databases Model Platform Network Delivery Content Feature hosting onsole (CDN) Cloud APIs
  • 128. References G VMware: http://www.vmware.com G HP: www.hp.com/go/integrityvm G Microsoft Hyper-V: http://www.microsoft.com/hyper-v- server/en/us/default.aspx G “Windows Server 2008 Hyper-V Technical Overview”- http://download.microsoft.com/download/4/2/b/42bea8d6-9c77-4db8-b405- 6bffce59b157/Hyper-V%20Technical%20Overview.docx G Comparison of Hypervisors by VMware - http://www.vmware.com/technology/whyvmware/architectures.html#c132894 G “VMware vSphere 4” Datasheet - http://www.vmware.com/products/vsphere/ G All the pictures representing VMware staff have been taken from Vmware library and are under rights of VMware. These slides represents only the view of the author and not that of VMware or of other companies mentioned in the slides such as HP, microsoft or others. The slides are only provided for didactical purpose and no for profit. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 130
  • 129. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 131
  • 130. Sistemi Distribuiti, Univ. Firenze, Paolo Nesi 2011-2012 132