Wicsa2011 cloud tutorial

Architecting Cloud Applications Dr. Anna Liu Research Group Leader Software Systems National ICT Australia

5 About NICTA National ICT Australia ,[object Object]

Largest ICT research resource in Australia

National impact is an important success metric

~700 staff/students working in 5 labs across major capital cities

Providing R&D services, knowledge transfer to Australian (and global) ICT industryNICTA technology is in over 1 billion mobile phones

Research Areas at NICTA Networks Machine Learning Software Systems Aruna Seneviratne Bob Williamson Anna Liu Gernot Heiser Computer Vision Optimisation Control & Signal Processing Nick Barnes,br /> Richard Hartley Peter Corke Mark Wallace, Sylvie Thiebaux, Toby Walsh Rob Evans 6

NICTA’s mission: to be an enduring world-class ICT research institute that generates national benefit. Australia’s National Centre of Excellence in ICT Research Research focused on areas of importance to Australia Publicly funded, not for profit Best of breed research teams (400 staff + 300 students) Industry engagement Industry outcomes Enduring solutions ‘Spinout’ companies Engagement models include… ,[object Object]

Our team’s mission: help enterprises take full advantage as software extends into cloud! Cost optimised High availability Hybrid cloud Onsite/offsite Real-time monitoring Disaster recovery Actionable analytics Business continuity Intelligent management Systems resilience Elastic Dynamic Real time Our applied R&D capability spans cloud computing, web, SOA, distributed systems, data management, analytics, performance monitoring, DR, automated reasoning, ontologies, AI… High performance 8

Agenda Introduction to Cloud Computing Characteristics, Deployment and Delivery Models Enterprise Architecture and Migration Framework Usage Scenarios Evaluating Cloud Computing Enterprise context, Business opportunities, risks Technical qualities of platforms Platform Architectural Insights Proof of Concept Experiences Advanced Architecture Issues Future Directions Industry happenings Research Agenda

What is Cloud Computing? Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics, three service models, and four deployment models. - US National Institute of Standards and Technology

Characterising Cloud Computing

Five Characteristics – NIST Definition On-demand Self-Service A consumer can provision computing capabilities without human interaction Broad network access Computing capabilities are available over the network and accessed through standard mechanisms Resource pooling Provider’s computing resources are pooled to serve multiple consumers with different resources dynamically assigned according to consumers’ demands Rapid elasticity Computing capabilities can be rapidly and elastically provisioned to quickly scale out and rapidly released to scale in Measured service Resource usage can be monitored, controlled, and reported. Providing transparency for both the provider and consumer

Leading Provider: Amazon EC2 Let’s see how Amazon EC2, a leading commercial cloud, looks I want my cloud!

1. Grab your credit card and create an account. (10 min) Then, access to a console 3. Hit this button 2. Select where you want to create your virtual machines (US East, US West, Ireland or Singapore)

4. Select a machine image ,[object Object]

You can register your machine images as well,[object Object]

1.0Ghz CPU + 1.7GB RAM  0.04 USD/hour

3.0Ghz x 8 CPUs + 68GB RAM  1.1 USD/hour

You can pay Win/SQL Server license fees in pay-per-hour,[object Object]

7. Done! (< 5 minutes in total) ,[object Object],I got my virtual machine!

8. Connect to my virtual machine ,[object Object]

You have a root access!!You’re in an Amazon Datacenter in CA This is my desktop in Sydney

If you like Windows, just launch a Windows virtual machine and remote-desktop to it Connected through a VPN connection You’re in an Amazon Datacenter in NV This is my desktop in Sydney

9. Terminate or hibernate virtual machines when they are not in use ,[object Object]

Restart instances in the morning if necessary. It takes just a couple of minutes,[object Object]

Three Service Models – NIST definition Technology exposed to customers Providers Software as a Service Platform as a Service Infrastructure as a Service Datacenter Infrastructure

Three Delivery Models Infrastructure as a Service (IaaS) The consumer has control over operating systems, storage and deployed applications Platform as a Service (PaaS) Consumers can deploy applications created using programming languages and tools supported by the provider (e.g., Java Servlet) The provider shields the complexity of its infrastructure Scale up/down, load balancing, replication, disaster recovery, database management, … Software as a Service (SaaS) Consumers use the provider’s applications The consumer does not manage the underlying cloud infrastructure

Leading Provider: Google App Engine PaaS is the hottest area because many players (e.g., VMware) are currently moving into PaaS. Let’s see how Google App Engine, a leading commercial PaaS, looks I want my PaaS!

1. Create an account. (5 min) GAE offers a large amount of quota for free 2. Write an application using GAE’s framework

3. Deploy your application on GAE! Scale up/down, load balancing, replication, disaster recovery, database management, … many functions are implemented by GAE’s framework

4. Check your resource usage (CPU, storage, # of API calls, …) Pay only when usage exceeds the free quota

Four Deployment Models – NIST Definition Hybrid Cloud Private Cloud Public Cloud Community Cloud Exclusively own, maintain and use Share and justuse when need Share and maintain by a group Organization General public A group of organizations

Four Deployment Models Public cloud Generally very secure but risk of discontinuity and less control Community cloud Enjoy cost savings and relatively easy to retain governance Private cloud More control but less cost savings compared to public cloud Hybrid cloud Hot area in the next couple years: Enjoy both high security and cost savings from private and public cloud, respectively

Why Cloud Computing? High Elasticity/Scalability Virtually infinite amount of resources is available on demand Reduce cost and complexity Pay per usage, economies of scale Generally speaking, non-7x24x365 systems with higher resource usage bring large cost savings No in-house IT maintenance No up-front cost for geographically distributed disaster recovery Innovation Possibilities Ease of Use You can implement your idea with minimum overhead and cost Processing Big Data Cost of 1 machine for 100 hours = Cost of 100 machines for 1 hour

Issues and What NICTA is doing? #1 Benefits and risks trade-off analysis Helps with decision making: use cloud or not? Suitable architecture for hybrid cloud? A model to show benefits (e.g., operational cost savings and elasticity) and risks (e.g., security, performance degradation, migration cost) Cost estimation Everybody’s question: Ok, then what is the actual cost for me? A model to estimate the actual initial and operational cost from application’s profile We’re collaborating with various Australian organizations to answer these questions through building and migrating systems in and to cloud

Issues and What NICTA is doing? #2 Automatic reconfiguration in hybrid cloud “outsource” your workload to public clouds only when needed Move some components/VMs/data to or from a public cloud to achieve certain performance Monitoring and management Monitor whether Service Level Agreements are guaranteed Secure the transparency of SLA monitoring Developing the new yardstick for cloud platforms, measuring elasticity for SPEC RG Exploring the possibility of new applications What we can do using huge computing resources? Collaborating with Microsoft Research on Azure Cloud Platform

Cloud, Cloud, Cloud,... Cloud Computing is the No. 1 in the top 10 strategic technologies for 2011 Cloud is everywhere? No Middle to large enterprises see huge opportunity in public cloud but also anxiety/pain due to… The lack of governance, i.e., visibility and control The lack of “architectures for (hybrid) cloud” The lack of migration methodology The lack of common cost structure The lack of automation across cloud and in-house … 35

Cloud Computing - The Enterprise Context 36 STATE OF PLAY Clear benefits in cloud adoption Reduced IT cost, agility, efficiency, innovation opportunities Top risks/adoption issues: Security & privacy - Migration challenges Ownership of data – Service levels Lock-in / interoperability – Performance Availability / reliability – Cost and ROI Monitoring & control – Governance Compliance and regulation – Competencies Software licensing in the cloud - Operational challenges Contracts and commercials - new roles and responsibilities Payment model, metering/charge backs Risks vary with service model and provider Many progressive organisations evaluating cloud Proof of concepts, pilots, cloud computing strategy papers Some good adoptions in certain verticals, SME, Software as a service… CIOs need greater visibility and control over their assets running in local servers and in the cloud before reaping the benefits of cloud computing.

Integration Challenges Integration Challenges ,[object Object]

Process IntegrationIdentity Challenges: Access Control AuthN, SSO, AuthZ Identity Lifecycle Identity Portability Interoperability Management Challenges ,[object Object]

Halting, Pausing, Throttling…

Programmatic access to health model,[object Object]

Internet Service Bus REST on .NET Service Bus Simple to implement for interop across different languages Less overhead packages SOAP on .NET Service Bus Only available for .NET Frameworks communications atm Other languages are not fully supported (Java can only pass Access Control on .NET Service) More overhead packages when communicate between C# and Java, than C# to C#

40 / 25 Overview of Cloud Computing Offerings

Overview of Three Leading Cloud Computing Platforms

Cloud Computing Environment from AWS On-demand instances operate on a virtual environment EC2 is a IaaS offering Scaling computing environment Datacenter located in different regions, including US (North and East), EU and APAC. Types of instances: Standard Small (1 ECU, 1 Core, 1.7GB memory) Large (4 ECUs, 2 Cores, 7.5 GB memory) Extra Large (8 ECUs, 4 Cores, 15GB memory) High-Memory Extra Large (6.5 ECUs, 2 Cores, 17.1GB memory) Double Extra Large (13 ECUs, 4 Cores, 34.2GB memory) Quadruple Extra Large (26 ECUs, 8 Cores, 68.4GB memory) High-CPU Medium (5 ECUs, 2 Cores, 1.7GB memory) Extra Large (20 ECUs, 8 Cores, 7GB memory)

Cloud Computing Environment from AWS (contd) Database Support S3 Bucket storage Relational Database Service (RDS) Scalable SQL database Elastic Block Store (EBS) Disk partition (< 1TB) Supported environment Operating System Linux (e.g. Fedora, Ubuntu & Debian) Windows (e.g. Windows 7 & Windows Server 2008) Other licensed environment IBM WebSphere Application Server sMash Portal Server Oracle Database Oracle Enterprise Linux

Cloud Computing Environment from GAE Cloud hosting environment for web applications GAE is PaaS offering Automatic Scaling and load balancing Hardware specification is unknown No notion of geographical regions Database Support BigTable Other Support Google Documents Google Calendar Upcoming Products AppEngine for Business SLA and SQL support Data store (bucket storage) Used in conjunction with Prediction and BigQuery API Prediction and BigQuery API Analytics support

Cloud Computing Environment from Azure Windows Azure has 3 main components: Compute, Storage and Fabric Compute is based on Web Role and Worker Role Storage are scalable storage (see below) Azure is PaaS offering Database Support Small (1 CPU, 1.75GB memory) Medium (2 CPUs, 3.5GB memory) Large (4CPUs, 7 GB memory) Extra Large (8CPUs, 14GB memory) Storage support Types of storage Blob Queue Table Drive

Storage Offerings from AWS AWS S3 Stores blobs (up to 5GB per blob) Access via REST/SOAP. Sneakernet option (i.e., fedexing) is offered AWS EBS Network attached disk storage Used as an external HDDs of EC2 instances (Up to 1TB per volume) No direct access from the outside Allow for creating point-in-time snapshots of volumes in S3 High performance It’s reported that sequential access is faster than 70MB/sec (0.54Gbps) Allow disk striping by attaching multiple volumes to an EC2 instance

RDB Offerings from AWS Amazon RDS An EC2 instance with pre-installed MySQL 5.1 (Up to 1TB storage) Automatically patches Automated transaction logs backup up to last eight days and user-initiated DB snapshot Replication between multiple Availability Zones Amazon Relational Database Offers IBM DB2 9.5, Informix Dynamic Server Oracle 11g, 10g SQL Server Express, 2005 Sybase SQL Anyware 11 Postgres Plus Vertica Analytic Database

Storage Offerings from Azure Windows Azure Blob Stores blobs (up to 1TB per blob) Read/write a blob 4MB piece by piece Access via REST/SOAP/ADO.NET Windows Azure Drive NTFS volume on Azure Blob accessed from Azure instances Azure SQL Support a subset of Transact-SQL, which SQL Server fully supports (up to 50GB per database) Automatically patches Automatic high availability (no details are available) SQL Azure Data Sync is offered to sync on-premise DB and Azure SQL

Storage Offerings from GAE GAE Blobstore Stores blobs (up to 2GB per blob) Read/write a blob 1MB piece by piece Access via HTTP and no access control GAE Datastore Support SQL-like language and JDO (no storage size limit?) Services on the way Google Storage for Developers Extended version of GAE Blobstore Store blobs (100GB per blob), REST interface, fine access control BigQuery Analyze massive data in Google Storage using SQL-like language A query against 60TB of data takes less than 1 min

Comparison of Storages (con’t)

Cost Example Have 400GB data. Transfer 7GB/day log data into cloud and add 0.5 GB/day to a storage. Read 0.1GB/day from a storage. 1M requests/day on a storage. Cost for one year? AWS S3 (400*12 + Σ13650.5*i/30)*0.15 + (0.1*7 + 0.15*0.1)*365 + 0.1 * 365 $1,185 Amazon RDS (400*12 + Σ13650.5*i/30)*0.1 + (0.1*7 + 0.15*0.1)* 365 + 0.1 * 365 $889 + CPU fees (min $1,000/year) Azure Blob (400*12 + Σ13650.5*i/30)*0.15 + (0.1*7 + 0.15*0.1)*365 + 1 * 365 $1,513 Azure SQL (400*12 + Σ13650.5*i/30)*10+ (0.1*7 + 0.15*0.1)*365  $59,427 Azure SQL is quite expensive as a data storage but cheap as a small-mid scale high-performance and reliable SQL server

Cost Example 2 Have 5GB data. Transfer 0.01GB/day log data into cloud and add 0.01 GB/day to a storage. Read 0.1GB/day from a storage. 0.1M requests/day on a storage. Cost for one year? AWS S3 (5*12 + Σ13650.01*i/30)*0.15 + (0.01*7 + 0.15*0.1)*365 + 0.1*0.1*365 $235 Amazon RDS (5*12 + Σ13650.01*i/30)*0.1 + (0.01*7 + 0.15*0.1)* 365 + 0.1*0.1 * 365 $167 + CPU fees (min $1,000/year) Azure Blob (5*12 + Σ13650.01*i/30)*0.15 + (0.01*7 + 0.15*0.1)*365 + 0.1*1 * 365 $268 Azure SQL (5*12 + Σ13650.01*i/30)*10+ (0.01*7 + 0.15*0.1)*365  $853

Security Support AWS Firewall support to control network access to and from instances Amazon Virtual Private Cloud Isolates instances by IP range Connect to existing private infrastructure via encrypted Ipsec VPN Charged based on number of VPN connections and duration, as well as data transfer through VPN connection S3 Bucket policies Access Control List (ACL) Query string authentication GAE Google Secure Data Connector Encrypted Connection from Google Apps to internal applications behind firewall Filters traffic by users and applications OAuth Denial of Service (DoS) protection Blacklist IP addresses or subnets Impose limits

Security Support (contd) Azure AppFabric Service Bus Connects Azure applications and databases to internal infrastructure AppFabric Access Control Provide federated authorisation to applications and servers

Elastic Compute Capability Elasticity is the defining characteristic of cloud computing The aim is to allocate sufficient resource to do the job, but not too much such that it wastes resources There are broadly 2 architectures that achieves elastic compute capability Push architecture Pull architecture 57

Elastic Compute Capability Reference Architecture –Push Architecture The Push architecture is typically used for web applications Web browser (client) send a request to the web application side Load balancer receives the request and “push” to one of the web servers running on a compute node Requests are forwarded immediately (or at a certain rate) Load balancer is aware of the intensity of the workload 58

Elastic Compute Capability Reference Architecture –Push Architecture 59 Fig 1. Push Architecture Pattern

Elastic Compute Capability Reference Architecture –Pull Architecture The Pull architecture is often seen as an application-level architecture Also known as the Producer-Consumer design pattern Requests are sent to a queue In contrast to the Push architecture, it does not forward the request (hence less suitable for web applications) Compute nodes polls the queue periodically for jobs Requests are processed one at a time Polling frequently can induce overhead Easier to implement fail-safe mechanism Compute nodes need NOT inform the queue in case of failure Typical fail-safe mechanism involves a queue (e.g., AWS SQS or Azure Queue) that employs a lock attached with a timer. A message is locked when polled by a node. In case of a node failure, the message lock expires and return the message back to the queue. 60

Elastic Compute Capability Reference Architecture 61 Fig 2. Pull Architecture Pattern

Using Cloud for Business Continuity Two main usages of cloud for Business Continuity: Provides highly available systems for day-to-day business Serves as a technology platform to implement disaster recovery Some definitions: Business Continuity: “Activity performed by an organisation to ensure that critical business functions will be available to customers, suppliers, regulators and other entities…” Disaster Recovery: “A small subset of business continuity. The process, policies and procedures related to preparing for recovery or continuation of technology infrastructure critical to an organisation after a natural or human-induced disaster” Fault Tolerance: “The property that enables a system to continue operating properly, possibly at a reduced quality level…” 62

Building Highly Reliable Systems with Cloud Must address potential failures at two levels: Hardware/Infrastructure To prevent Single-Point-of-Failure (SPOF) by adding redundancy in all hardware components (i.e., redundant disks, redundant network devices, redundant power supply, etc.) NOT all cloud providers provide enterprise grade availability. Check your SLA!! Application Prepare fail-over system to take over in case of a failure Database replicates to minimise downtime and loss of data Replicate to geographically different location (e.g., to avoid natural disasters such as floods) 63

Case Study: Building Reliable System using EC2 Highly replicated architecture of cloud makes them great as foundations for business continuity solutions Globally distributed nature further enhances the disaster recovery capability of cloud Availability limitations means need to be realistic about Hot vs Warm vs Cold standby options 64

Case Study: Building Reliable System using EC2 (Contd) Data backup in AWS Amazon S3 is best for off-site data backup Stores large binary files Designed to provide 99.999999999% durability Objects are redundantly stored in multiple facilities in a Region Back up using EBS Uses a regular file system Takes image (or snapshot) of the partition VM Import Allows for easy replication from on-premise to cloud Not trivial to replicate various configuration such as network configuration and disk drives 65

10 Things You Didn’t Know About Cloud Platforms: Azure, GAE and AWS Dr. Anna Liu, Dr. Hiroshi Wada, Kevin Lee National ICT Australia

The 10 Things are... How long does it take for data in cloud to become consistent Limitation and quotas How unpredictable/variable is the cloud? Distributed transaction support in Cloud Pricing variations over time and space Sticky session support The new matrix of roles and responsibilities for cloud providers, consumers and system integrators Secure connections to the cloud Time to getting a new instance Auto-scaling is not all magic

69 The Reality of Eventual Consistency in Amazon SimpleDB The probability to read updated data in SimpleDB in US West An application reads data X (ms) after it has written data Eventual Consistent Consistent Read ,[object Object]

This pattern is consistent regardless of the time of day,[object Object]

71 Other Commercial NoSQL Databases Google App Engine Offers eventual consistent read and consistent read Behavior of eventual consistent read is completely different from Amazon’s In GAE, both types of reads behave exactly same unless data centers have a failure(s) Windows Azure Offers no options for read Always consistent Reference: H Wada, A Fekete, L Zhao, K Lee, A Liu, “Data Consistency Properties And the Trade-offs in Commercial Cloud Storage: The Consumers’ Perspective”, CiDR 2011. http://www.cidrdb.org/cidr2011/Papers/CIDR11_Paper15.pdf

Performance Unpredictability in Cloud Performance unpredictability is one of the major obstacles Performance variance of a MapReduce job for a 50-node EC2 cluster and a 50-node local cluster Examples (time as performance metric) Repeatability of results for researchers Time critical tasks for enterprises

Benchmark Results in EC2 The COV of large instance is higher than the small. However, both are at least by an order magnitude less stable than on a physical cluster. The COV of S3 Access may be influenced by other traffic on the network, showing this experiment just for completeness. Reference - Schad, Jo ̈rg, Jens Dittrich, and Jorge-Arnulfo Quiané-Ruiz. 2010. Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance. In Proceedings of the 36th international conference on Very large data bases. Vol. 3. 1. Singapore, Singapore: VLDB Endowment.

Distributed Transactions in Cloud There is now a range of Cloud Database types NOSQL (Azure Table, GAE Datastore, Amazon SimpleDB...) Much more ‘shardable’ architecture; No joins, not full ACID support SQL (Azure SQL, Amazon RDS, Oracle on EC2...) Variable distributed transactional support compared to their traditional RDBMS counterpart Experience with porting PetShop Challenge with porting the data access layer Some JDO interface not supported by App Engine, eg. ‘Join query’ No distributed transaction support in Azure SQL atm 79

Pricing fluctuates over space and time On demand pricing (hourly, per GB, per ‘000 requests) Reserved instances (1 or 3 year term + unit cost) Spot pricing (typically cheaper in US-East!) Similar pricing schemes observed for GAE and Azure 81

Sticky Session Support Autoscaling alone does not guarantee that clients of the same session will always contact the same instance Clients cannot perform a series of connected operations Amazon ELB supports Session Affinity Session affinity allows mapping to be created at the ELB Limitations Session affinity cannot handle HTTPS Autoscaling down an instance with a live session MS Azure advocates stateless sessions If you must – store session state in eg table storage Design issue - Server to remember conversation context? Or for client to remind it every time? How long should it ‘stick’? Too long: compromise server ability to distribute load

Customers’ Responsibility in IaaS Cloud Application Patching App Data Backup Application Monitoring Application Installation/Configuration OS/Application Security (e.g., Active Directory) Billing (Cost Center Charging) Antivirus OS Backup OS Monitoring OS Patching OS/Middleware Installation/Configuration Customers’ Responsibility Infrastructure Configuration (VPN, VMs, Disk, …) Access Control to IaaS Infrastructure Monitoring (CPU, Disk, Net, …) Usage Report and Basic Billing Amazon EC2 (IaaS providers)

Secure Connection to the Cloud 87

Performance Implications Low Security Option – max throughput 5.6MB/sec High Security Option - connection throughput is 4MB/sec Performance hit due to encryption, decryption and firewall Other interesting observations: VPC only available US East-1 and EU-west1 in single availability zone only S3 not working well with VPC yet (very slow), EBS is a workaround MS Azure VPN support next year Google Secure Connector

Time to Getting a New Instance Typically takes minutes to create an instance from its image on EC2 Trick to “create” instances quicker Create a pool of instances in advance, and stop (hibernate) them all Pay no instance cost but need to pay for storage cost (for stopped instances) Revive stopped instances if new instances are needed

Autoscaling is Not All Magic Amazon EC2 “… your application can automatically scale itself up and down depending on its needs.” Windows Azure “Optimizd for scale-out applications-designed so that developers can easily build scale-out applications…” Google App Engine “No matter how many users you have or how much data your application stores, App Engine can scale to meet your needs”

Autoscaling is Not All Magical (contd)

Virtual Machine ‘Stolen Time’ Using traditional system resource monitoring tools in cloud Measuring system performance within a virtual instance (using tools such as vmstat and top) can give misleading information Example: An EC2 instance (e.g. m1.small with 1 EC2 compute unit) does not go above around 40% CPU load as observed from vmstat Certain percentage (around 50-60%) appears on vmstat as ‘st’ “st – Time stolen from a virtual machine” (from vmstat manpage) Does it mean I am not getting what I paid for? No, not really Amazon instances are measured by EC2 compute units “One EC2 compute Unit provides the equivalent CPU capacity of a 1.0-1.2GHz 2007 Opteron or 2007 Xeon process” Monitoring system performance in cloud Use Cloud monitoring tools such as CloudWatch and RightScale

Limitation of Virtual Private Cloud (VPC) VPC hosts are logically detached from (but physically attached to) the Amazon network No direct connection to and from S3 via the Amazon local network Connection via internet only What happen if we need to transfer data from S3 to a VPC host? E.g. If we ship a removable media to Amazon, it would be uploaded to S3. How do we transfer the data to a VPC host? Option 1: Direct transfer from S3 to VPC host Traffic routes through the remote side and comes back (High latency) Option 2: Transfer to EBS and mount EBS to VPC host Traffic routes through local network (Low latency)

98 How Long You Need to Wait to Get Updated with Eventual Consistent Read? Result of the “5 minutes run” for one week ,[object Object]

t2: the first time to reach 100% of reading updated

t3: the last time to read stale data Mostly updated after 600ms but no guarantee

Let’s Switch Gear… what’s happening in industry? 99

Australian Cloud Adoption Software as a service Enterprise and SME Productivity suites, CRM Telco and SaaS vendor partnership emerging tier 2 System integrator Platform and Infrastructure as a Service SME, startups well on their way Enterprise doing evaluation Government Cloud, Community Cloud Data centre consolidation SOA, shared services Financial industry leadership 100

Some Australian Enterprise Proof of Concepts Internet scale web applications User base from around the world Integration with existing web APIs Transient campaigns Many Mobile devices connecting to cloud Good adoption in utilities industries Development/Test environment Dynamic provisioning of dev/test resources Pay for usage Bursty workload Web apps Large scale data analysis eScience, Financial risk calculations, Government statistical data 101

One example POC detail findings An Example POC Experience 102

103 Proof of Concept Overview Objective reduce IT cost evaluate cloud opportunity and risks Test and Dev environment, as opposed to production Maximise re-applicability of learning experience across other apps Evaluation dimensions Performance, security, feasibility cost and license, flexibility and elasticity integration with existing environment, migration effort disaster recovery and backup, new roles and responsibilities …

Solution Design Rationale POC Solution Design Rationale Standard 3 tier web application, with backend and authentication server integration Location of data tier Maintain as much as dev/test configuration as common as possible PaaS or IaaS Selection of cloud platform for POC Project Management Governance: CIO/Director level sponsorship Project participants: enterprise architect, solution developer, security specialist, commercial specialist NICTA: cloud computing experience and evaluation framework 2 wks POC selection; 6 wks POC; 2 wks consolidate findings 104

105 Architecture of a Hybrid Dev Environment NICTA Corporate Network Internet Remote-desktop to XX.XX.0.* (No direct access to Amazon VPC) Amazon Cloud (US-East Datacenter) IPSec VPN approx 230ms RTT Enterprise Data store Authentication server Business Web application On-Premise Servers Virtual Machines Private Cloud (Isolated Network) Only accessible from NICTA Isolated Network in Amazon

106 Security There is ‘Secure integration to cloud’ solutions emerging Amazon VPC, Google Secure Data Connector, Azure App Fabric, etc Standard IPSec-VPN brings peace of mind to enterprise users One of the strong key enablers for enterprise use Fit in an existing security policy Data masking could increase the cost/effort An automated method is necessary for further cost/effort reduction Secure Software Development Lifecycle Process change required

107 Performance The performance of each component (network, VMs, …) in cloud is comparable to or better than current on-premise components For dev/test environments, suitable for production systems? Do not underestimate the latency in hybrid environments Many of traditional applications and protocols are not optimized for a high-latency/WAN environment E.g., a protocol is too “chatty” and we observed that the network usage never exceeds 0.1% in some cases There are performance improvement opportunities Alternative solution design, Configuration and tuning

108 Cost Many companies use ‘private cloud’; however, current offering is seen to be more expensive and less flexible increasingly Pay-as-you-go options are available unit price is typically ~100 times more costly for storage SLA & management services usually included Cost of keeping data/VMs is larger ,[object Object],[object Object]

Commercial Implications Software Licensing in the cloud? Reuse enterprise license Pay for usage software license model Payment model? enterprise governance model Metering and chargeback Service level agreement? Monitoring and management Contracts Backup, disaster recovery New roles and responsibility? Existing IT outsourcing arrangements 110

POC Experience Summary Cloud Computing has the potential to reduce existing enterprise IT cost There are technical solutions for managing performance, security risks Need some fresh approach to manage: Enterprise architecture and governance Commercial implications such as SLA, new roles and responsibility 111

Other Global Challenges Policy and Procedure Procurement strategy? Pricing strategy? Governance and Control Financial control vs shared model Taxation and legal Federal and state based taxation, sales and payroll tax Compliance and assessment 112

Other Challenges Australian Face The Tyranny of Distance Latency: ~250ms Singapore, ~220ms US west coast, ~5-600ms US east coast, Europe No business case for an Australian Data centre 22 mil population, 12 mil internet users National Broadband Network The rise of oz cloud innovations Strong Privacy Laws Federal Privacy Act APRA – Australian Prudential Regulation Authority EU Safe Harbour <> oz Safe Harbour 113

Other Industry Happenings Specialist cloud New types of System integrators Innovative Scenarios 115

Research Agenda Enterprise Architecture Framework Evaluation, acquisition, effort estimation, project and risk management Software Development Lifecycle Requirement solicitation for cloud, design for interoperable services, MDA/MDD/DSL, testing at massively parallel scale, cloud design patterns Interoperability and Integration Hybrid cloud, integration challenges across clouds Performance Engineering Monitoring and measurement, performance modelling, prediction and analysis, quality of service, SLA and assurance Many more… 116

Cost Effort Estimation for Cloud Migration Cost implication/estimation for cloud migration is especially challenging because: Applications and migration projects vary in terms of: size/complexity, functionality, quality requirements, target deployment platforms... Cloud computing is new and different from traditional software engineering paradigm: different development and deployment models, non-functional characteristics, pricing models... Migration effort/cost estimation is not trivial Little Empirical Data in cloud V Tran, K Lee, A Fekete, A Liu, J Keung, “Size Estimation of Cloud Migration Projects with Cloud Migration Point (CMP)”, 5th Intl Symposium on Empirical Software Engineering and Measurement V Tran, J Keung, A Liu, A Fekete, “Application Migration to Cloud: A Taxonomy of Critical Factors”, ICSE Software Engineering For Cloud Computing Workshop 2011. 117

Adaptive Cloud Middleware Research Evaluating Cloud Performance – Measuring Elasticity Achieving Cloudburst – Integrated monitoring and management Cloud Data Management – Elastic Data Store S Sakr, L Zhao, H Wada, A Liu, “CloudDB AutoAdmin: Towards a Truly Elastic Cloud-Based Data Store”, 9th IEEE Intl Conf on Web Service ICWS 2011. S Islam, J Keung, K Lee, A Liu, “An Empirical Study into Adaptive Resource Provisioning in the Cloud”, IEEE Intl Conf on Utility and Cloud Computing UCC2010. L Zhao, A Liu, J Keung, “Evaluating Cloud Platform Architecture with the CARE Framework”, APSEC 2010. P Brebner, A Liu, “Modeling Cloud Cost and Performance”, Cloud Computing and Virtualisation (CCV 2010) H Wada, A Fekete, L Zhao, K Lee, A Liu, “Data Consistency Properties And the Trade-offs in Commercial Cloud Storage: The Consumers’ Perspective”, CiDR 2011. 118

Elasticity Measure Elasticity is the defining characteristic of cloud Challenge: No existing metrics to measure elasticity Not the same as ‘scalability’ or ‘throughput’ measures Users care about running cost, agility Understanding elasticity “the ability of software to meet changing capacity demands, deploying and releasing relevant necessary resources on-demand” Varying elasticity behaviour across platforms SPEC Standardisation effort

Data Consistency in Cloud Inconsistent views of data is common in cloud Due to the distributed nature and support of massive scalability Understanding data inconsistency is a new and big challenge for software industry What is the exact characteristics? When (not) to use them? How to use them? ,[object Object]

Working on a decision making algorithm involving large number of parameters

CiDR 2011 paper for more details

H Wada, A Fekete, L Zhao, K Lee, A Liu, “Data Consistency Properties And the Trade-offs in Commercial Cloud Storage: The Consumers’ Perspective”, CiDR 2011.120

Cloud Data Management One of the main goals of the next wave of Cloud Computing is to facilitate the job of implementing every application as a distributed, scalable and widely-accessible service on the Web. Recently, a new generation of low-cost, high-performance database software has emerged to challenge dominance of RDBMS named as NoSQL (Not Only SQL). Examples are BigTable, Dynamo, Cassandra, Hbase, HyperTable,… The main features of these systems include: ability to horizontally scale, supporting weaker consistency models, using flexible schemas and data models and supporting simple low-level query interfaces. 121

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