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Scientific Cloud Computing: Present & Future
Charles (Cal) Loomis (CNRS/LAL & SixSq Sàrl)
INFN CNAF, Bologna, Italy (22 May 2013)

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Cloud Marketing
“Cloud” is currently very trendy, used everywhere
 Many definitions that are often incompatible
 Used (often) to market pre-existing (non-cloud) software
CommodityComputing(Sun)
UtilityComputing(IBM,HP,…)
AmazonEC2
AmazonEBS
Mature Virtualization
Simple APIs
Excess Capacity

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In two pages NIST defines:
 Essential characteristics
 Deployment models
 Service models
What is a Cloud?
http://csrc.nist.gov/publications/
nistpubs/800-145/SP800-145.pdf

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On-demand self-service
 No human intervention
Broad network access
 Fast, reliable remote access
Rapid elasticity
 Scale based on app. needs
Resource pooling
 Multi-tenant sharing
Measured service
 Direct or indirect economic
model with measured use
Essential Characteristics
http://csrc.nist.gov/publications/
nistpubs/800-145/SP800-145.pdf

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Private
 Single administrative domain,
limited number of users
Community
 Different administrative domains
with common interests & proc.
Public
 People outside of institute’s
administrative domain
Hybrid
 Federation via combination of
other deployment models
Deployment Models
http://csrc.nist.gov/publications/
nistpubs/800-145/SP800-145.pdf

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Software as a Service (SaaS)
 Direct (scalable) hosting of end
user applications
Platform as a Service (PaaS)
 Framework and infrastructure
for creating web applications
Infrastructure as a Service (IaaS)
 Access to remote virtual
machines with root access
Service Models
http://csrc.nist.gov/publications/
nistpubs/800-145/SP800-145.pdf

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Advantages
 No software installation
 Universally accessible
Disadvantages
 Questions about data access,
ownership, reliability, etc.
 Integration of services & novel
uses of data are (often) difficult
Trends
 Social scientific computing
 Service APIs to allow
integration  PaaS
Software as a Service (SaaS)

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Advantages
 Programmers take advantage of
integrated load balancing,
automatic failover, etc.
Disadvantages
 Restricted number of languages
 Applications strongly locked to a
particular provider
Trends
 Dearth of “pure” PaaS offers
 Encroachment from both SaaS
and IaaS sides
Platform as a Service (PaaS)

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Advantages
 Customized environment with
“root” access
 Easy access to scalable
resources
Disadvantages
 Variety of APIs and interfaces
 VM image creation is difficult
and time-consuming
Trends
 Lots of specialized cloud
providers appearing
 Orchestration pushing into
PaaS space
Infrastructure as a Service (IaaS)

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Focus: Infrastructure as a Service

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State of the Art
Commercial Provider: Amazon Web Services (AWS)
 Leading and largest IaaS service provider
 Improving and adding new services at a phenomenal rate
 Almost all IaaS providers use AWS-like service semantics, but
differentiate based on price, SLAs, location, etc.
Commercial Cloud Distribution: VM-ware
 VM-ware: extremely good and complete, but very expensive
 Provide ESXi virtual machine host for free
Open Source Cloud Distributions
 Essentially none in 2004; now easily a dozen different distributions
 StratusLab, WNoDeS, …, OpenStack, OpenNebula, CloudStack
 Very different levels of maturity, stability, scalability, etc.

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Why are cloud technologies useful?
For (scientific) users
 Custom environment: no rewriting or porting applications to fit into a
resource provider’s environment
 Simple access: most providers use a REST or RPC API allowing
simple access from all programming languages
 Reasonable cost: only pay for what resources are used, especially
attractive for individuals/groups that do not have large, existing
hardware investment

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Why are cloud technologies useful?
Separation of responsibilities
 Hardware / Services / Platforms / Users
 People at each layer can focus on their responsibilities with minimal
interactions with people in the other layers.
Resource Providers
 Better utilization of shared resource because wider range of
applications (and disciplines) can use the cloud
 With hybrid cloud infrastructures, providers can outsource excess
demand to other providers

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Trend for Scientific Computing
Will we all just be users of the Amazon cloud?
Pendulum swinging towards large data centers with “fat” machines
 These can offer elastic cloud services at a reasonable price
 With scientific clouds there is low barrier to entry and users can
maintain administrative control of services and data
 Providing shared resource between scientific disciplines much easier
because of virtualization
Migration will be gradual…

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Overcoming inertia…
Users
 How to use virtual machines to get my work done?
 How to structure, store, access, and protect data?
 Realize shared infrastructures with customized env. are possible
Application Developers
 How to use cloud techniques to improve my applications?
 … and my development workflows?
 Applications can be services (with assoc. pluses and minuses)
Data Centers
 Reuse existing (commodity) hardware investments
 Take advantage of (and train) existing system administrators
 How to manage/use a (private, community, public) cloud?
Significant benefits from cloud even without large scale elasticity!

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Challenges

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Elasticity
Can we have infinite elasticity with limited resources?
“Local” solutions
 Economic models to avoid hitting infrastructure limits?
 Spot instances and/or different service classes?
 Aside: IPv6 addressing is necessary for large (scientific) clouds
Federated solutions
 Hybrid (scale-out) infrastructures?
 Higher-level brokers or orchestrators?
Cannot have elasticity without some kind of accounting!

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Data Management (Legal)
Transfer and treatment of data across borders
 Differing legal protections in different jurisdictions
 Legal constraints for data locality (banking, medical data)
 Unclear responsibilities for data: guardian, custodian, owner, etc.
 Europe working hard to come to a consistent legal framework
Protection of data in the cloud
 Consistent access controls for all data locations
 Guarantees about data protection from cloud provider personnel
 Reliability of the provider’s storage
 Knowledge about provider’s policies for data protection

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Data Management (Technical)
Efficient exploitation of large datasets
 Need significant computing next to storage, AND/OR
 High bandwidth remote access
Locality matters
 Sometimes it is inconvenient to transfer raw data away from instrument
 Clouds can be used to reduce data locally before transfer
“Open Data” requirements
 Cloud may help meet such requirements
 Does not remove need for well defined dataset metadata and format
 Need long-term funding for the curation of those datasets

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Security
How to maintain the security of a cloud infrastructure?
Shifting some responsibility to users:
 Users have root access and must secure services within their VMs
 Users have less security experience  need for education & help
 Dynamic network configurations can help improve security
Changing expectations from administrators:
 Leave firewall policies to users running VMs
 Should not expect to run security software inside of VMs
 Need to enhance monitoring to discover abnormal behavior

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Image Management
Image metadata
 What does an image contain (OS, services, configuration, etc.)?
 What versions of the kernel, software, etc. are included?
 Who is responsible and/or supports a given image?
 How do I identify a given image?
Creating machine images
 How can an image be created for multiple clouds?
 What do I have to do to create a secure machine image?
Sharing images
 How can I make my images available to others?
 Can I parameterize my images to make them useful to more people?
 Can the images be transported and used efficiently?

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Vendor Lock-in vs. Federation
Common API
 Fully interoperable API avoids duplication in cloud control software
 Has very limited impact on applications and services in the cloud
 Current (quasi-) standards: EC2, OCCI, CIMI, CDMI, …
Common Semantics
 Semantics determine how apps and services operate in the cloud
 For IaaS, cloud providers have a broadly similar semantics, but…
 File-based and block storage is one difference.
Contextualization
 Can a user run the same validated image on all clouds?
 Can a user share the same parameterized image with others?
 Neglected issue in standardization; CloudInit becoming de facto std.

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Solutions

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StratusLab History
Informal collaboration to investigate
running grid services on Amazon
EC2 (2007)
StratusLab Project (6/2010 to
5/2012) co-funded by EC with
6 partners from 5 countries
Open collaboration
to continue the
development and support of
the StratusLab software
Website: http://stratuslab.eu
Twitter: @StratusLab
Support: support@stratuslab.eu
Source: http://github.com/StratusLab
Identified need for open
source cloud distribution.
Production dist. with academic
& commercial deployments.

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StratusLab
Complete Infrastructure as a Service cloud distribution
 Developed within EU project, software maintained by partners
 Focus: Simple to install and simple to use
Services
 Compute: Virtual machine management (currently uses OpenNebula)
 Storage: Volume-based storage service
 Network: Simple configuration for public, local, and private VM access
 Image mgt.: Complete system for trusted sharing of VM images
 Tools (python CLI) and APIs (Libcloud) to facilitate use of cloud
 Tools to facilitate installation of services

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SlipStream
Cloud orchestrator and deployment engine
 Facilitates testing, deployment, and maintenance of complex systems
 Transparent access
to multiple cloud
infrastructures
 Allows automated
deployment of systems
in one or more clouds

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Image Management

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Image Management
Image metadata
 What does an image contain (OS, services, configuration, etc.)?
 What versions of the kernel, software, etc. are included?
 Who is responsible and/or supports a given image?
 How do I identify a given image?
Creating machine images
 How can an image be created for multiple clouds?
 What do I have to do to create a secure machine image?
Sharing images
 How can I make my images available to others?
 Can I parameterize my images to make them useful to more people?
 Can the images be transported and used efficiently?

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Image Management Actors

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Marketplace
Priorities
 Mechanism for sharing and trusting images
 Possible to distribute fixed, read-only data sets as well
 Split the storage of image metadata and image contents
 Define roles for creator, user, administrator, and validator
Implementation
 Marketplace API: Proprietary REST API for create, read, search
 Marketplace acts as image registry and handles only metadata
 Image contents can be located on any public (web) server
 ‘Private’ images can also be held in cloud storage

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Marketplace: Key service in larger ecosystem
Trust
 Marketplace metadata plays key role in providing information about the
image contents and provenance
Factories
 stratus-create-image facilitates customization of images
 VirtualBox and other local virtual tools
 Bitnami and many similar services for build of standard OS services
 Quattor and other fabric management can be used
Transport (& Storage)
 StratusLab uses simple HTTP(S) for image transport
 Could imagine using ftp, gridftp, bittorrent, etc.
 vm-caster/catcher in EGI federated cloud task force

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Image Handling Workflow

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Cloud Federation

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StratusLab Federated Cloud Infrastructure
Features
 Two sites operating (LAL and GRNET) for ~3 years
 Common user authentication
 Ability to use the same images across resources
 StratusLab client allows easy switching between sites
 StratusLab Libcloud binding allows common view of both sites
Need to go further…
 To sites running different cloud software
 Helix Nebula and EGI Fed. Cloud Task Force active in this area

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Federation Models
Transparent Federation
 Site operators “outsource” to other providers
 Completely transparent to end users
 Difficult to achieve in practice because of data protection concerns and
network access/performance
Brokered Federation
 Variety of different cloud infrastructures are visible to users
 Users choose to place virtual machines in particular locations
 Simple clients can handle federation if differences are small
 Orchestrators are needed for larger differences between clouds
Both Helix Nebula and EGI take the brokered approach

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Vendor Lock-in vs. Federation
Common API — minor issue
 Fully interoperable API avoids duplication in cloud control software
 Has very limited impact on applications and services in the cloud
 Current (quasi-) standards: EC2, OCCI, CIMI, CDMI, …
Common Semantics — important issue
 Semantics determine how apps and services operate in the cloud
 For IaaS, cloud providers have a broadly similar semantics, but…
 File-based and block storage is one difference.
Contextualization — critical issue
 Can a user run the same validated image on all clouds?
 Can a user share the same parameterized image with others?
 Neglected in standardization but CloudInit becoming de facto std.

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“Our” Contributions
StratusLab
 Adopt CIMI as the standard interface to services
 Provide Libcloud (python) driver for StratusLab
 Provide EC2, OCCI, etc. as adaptors to CIMI interface
 Move to CloudInit for contextualization framework
 Flexible authentication system to support different methods
SlipStream
 Chosen as main interface to supported clouds in Helix Nebula
 Already supports multi-cloud deployments
 Support for all clouds within Helix Nebula

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Running Clouds in Production

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Cloud Experience at LAL
Private cloud for laboratory services
 Works well, plan to migrate all services including grid worker nodes
and experiment-specific servers
 Services switched to VMs without users being aware of change
 Very different way of working, need to change administrator habits
 Have seen some stability issues related to SL6 kernel/virtualization
Public cloud open to university
 Very positive reaction to cloud; LAL resources nearly 100% used
 Variety of disciplines: biology, software eng., statistics, astrophysics,
bioinformatics, …
 After initial introduction, users require only low level of support
 Other labs offering StratusLab training without our direct involvement

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Summary

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Summary
Cloud for scientific computing
 Already a common tool for many scientific disciplines
 Cloud technologies will become more pervasive with time
 Associated swing back to larger, centralized data centers
Many challenges for cloud
 Elasticity with limited resources
 Data management (legal & technical)
 Security
 Image management — unique holistic approach from StratusLab
 Federation — brokered federation with StratusLab & SlipStream
LAL cloud experience
 Very positive feedback from both administrators and users

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Questions and Discussion
Website: http://stratuslab.eu/
Twitter: @StratusLab
Support: support@stratuslab.eu
Source: http://github.com/StratusLab
SixSq: http://sixsq.com
SlipStream: https://github.com/organizations/slipstream

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http://stratuslab.eu/
Copyright © 2013, Members of the StratusLab collaboration.
This work is licensed under the Creative Commons Attribution 3.0
Unported License (http://creativecommons.org/licenses/by/3.0/).