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Harness the Power of theCloud for Grid Computing

June 29, 2010

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Your Panel TodayPresenting:• Michael Crandell, CEO• Josh Fraser, Business Development• Dave Welch, Sales Engineer

Q&A:• Matthew Small, Account Manager

Please use the questions window to ask questions anytime!

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Agenda• Welcome• RightScale Customer Use Cases• Grid Computing Challenges• Grid Computing in the cloud with RightScale• Demo #1• Automated Management• Demo #2• Quantifying the Benefits• Q&A

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• Cloud Management• Over 20,000 users• 1.2M+ servers

launched• Scaling events:

- 50-3500 servers 3 days- 8000 servers 1 co.- 0 to 35M users/day

• Largest production deployments in the cloud to date

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RightScale Cloud Management Easily Deploy and Control

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Grid WorkerGrid WorkerGrid WorkerGrid Worker

Grid WorkerGrid Worker

Customers on the CloudCustomers from Web 2.0 to Enterprise

From to

RightScale Grid Solution Pack

Grid Worker

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Cloud-based Grid Computing Use Cases

Pharmaceutical Analysis – Researchers expected a protein analysis comparing 2.5 million compounds to take a week of processing on internal servers

• Using hundreds of servers, the job was completed in one day

Web 2.0 – Transcoding images to render video on demand

• Processing time was reduced from hours on internal resources to minutes

Insurance Claims Loss Control – Systems for detecting fraudulent or duplicate claims in batches of millions would have required months of processing time to run and millions of dollars to build in the data center

• Batch runs finished in a few days at significantly lower cost

Financial Data Processing – Back testing environments that analyze data to test new trading strategies

• Trading strategies analyzed faster and more cost-effectively by scaling out servers

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Grid Computing & Batch Processing Challenges

For IT• High capital investment, typically low capacity utilization• Scheduling conflicts, constant provisioning• Specialized architectural and operational skills• Specialized software applications and unique datasets

For End Users• Wait for resources• Limited to resources available in the datacenter

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Resource Utilization

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Grid Computing in the Cloud

Cloud Computing Model

Resources on Demand

Virtually Infinite Resources

Pay as You Go

Grid Computing in the Cloud

Resolves Scheduling Issues

Supports Faster Processing

Matches Costs to Demand

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Grid Computing managed by RightScale

RightScale

Preconfigured components and solutions

Automation and systems management

User and cost control

Grid ComputingManaged by RightScale

Fast and easy provisioning

Reduced administration

Control and visibility

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Grid Computing Solution Pack

Preconfigured Framework for Grid ComputingProven, Best Practices Architecture on Amazon Web Services• Leverages AWS EC2, SQS, and S3• Multiple AWS Regions and Availability Zones

RightScale Professional Services and Support

“Lilly is leveraging RightScale's infrastructure management interface and services against appliance/application stacks in a ‘vending machine’ concept that allows self-service to infrastructure.”

- Dave Powers, Assoc. Information Consultant, Eli Lilly & Co. Top 25 Information Managers 2010

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Highlights

Complements existing internal grid engines (Sun Grid, etc.)TBDTBD

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RightScale Grid ArchitectureAutomated server scaling, operational remediation, server cost optimization

SQS Input Queue SQS Error Queue

SQS Output Queue

Amazon S3Amazon S3

Batch jobs from Your job producer

application

Scalable cloud servers using RightScale Server Templates

Your application or next batch process

job consumer

Worker Daemon

Your code

RightScale Amazon Cloud InfrastructureCustomer code

RightScale Management Interface

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Job CoordinatorInput file Creation and message Queuing

Work Queue

S3 Buckets

IN OUT LOG

{Work_Unit }

Job Producer CodeInputFile

1. Job Producer creates input file(s) 2. Job Producer constructs a work Unit3. Input files are placed in an S3 bucket4. Work_unit is packed into a message5. Message is placed in the SQS work queue.

{MSG}Encoder

(Pack Message)

Data Structure{ }LEGEND

• Easily schedule with your current scheduler (Tivoli, Condor, other?)

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Daemon / Worker CodeWork Queue and Work _Unit Processing

Work Queue

Worker daemon

Workercode

AWS Instance

MSGEnv{ }MSG{ }

Result{ }

1. RightScale instantiates server2. Worker Daemon copies a

message3. Unpacks the msg and

downloads files4. Worker code is called with both

the msg_env and original unpacked message

5. Worker does work6. Output files placed in the /out

directory7. Logs are created8. Results returned back to the

Daemon

/local

/tmp /in /out /log

S3 Buckets

IN OUT LOG

{Work_Unit }

Data Structure{ }LEGEND

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Daemon Results Handling

Elasticity daemon

Workercode

AWS Instance

Result{ }

Data Structure{ }LEGEND

1. Upload Output Files2. Upload log files3. Delete /tmp and /in files4. If worker returns success

A. Queue Results in results_queue

B. Post results to http server5. Else queue results to the error

queue6. Queue statistics in Audit Queue7. Delete Message from input

queue

/local

/tmp /in /out /log

S3 Buckets

IN OUT LOG

Results Queue {Results} {Results} Consumer

Error Queue {Results}

Audit Queue {Audit} {Audit}Consumer

{Results}Consumer

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Demo #1• Pre-configured Grid Architecture

• Job Coordinator Plug-ins

• Graceful Provisioning and De-provisioning

• Automated Queue Handling

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Automated Management

Server Array ScalingYour grid

processing code

Worker Daemon

1. ALERTS or ScheduleA. Scale up/down based on monitoring system metricsB. Scale up/down based on set schedule – TOD, DOW

2. QUEUES STATISTICSA. # of backlogged msgs per instanceB. Time msgs have stayed in queue

1) Avg. of the last 10 msgs2) Max length of the last 10 msgs

C. If idle after 55 mins from launch

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Automated Management Key Benefits

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Demo #2• Configuring array automation

• Analyze your results

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Key Economic Benefits

Cost Savings

No CAPEX

Pay only for what is used

SaaS-based management model

Agility

Your job is always first in line

Unlimited resources

Pre-configured, proven architectures

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Amazon’s Public Data Sets

Centralized repository of public data sets AWS is hosting the public data sets at no charge for the community, users pay only for the compute and storage they use for their own applications.Examples:• Economics data, e.g. the 2000 US Census  • Life Sciences data, e.g GenBank

http://aws.amazon.com/publicdatasets/

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Agility Example: Cost-neutral Equation

This graphic compares running the same 10,000 jobs on 2 servers versus 1000 servers. The cost is the same for either scenario in RightScale using

AWS, but the difference in elapsed time is 499 hours.(assuming each server can process 10 jobs/hour)

10,000jobs

10,000jobs

2 server cloud

Total processing time: 500 hours

Total processing time: 1 hour

1000 server cloud

Output data

Output data

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Example Cost Savings – 1/4 The Price

Item Annual Cost

30 dedicated HP ProLiant DL360 G6 servers at $3,879 each, amortized over three years. Each server has two quad-core processors, for a total of 240 cores.

$38,790

Commercial grid middleware at $399/processor with basic support $95,760

Open source Resource Manager software at $199/processor with support contract $47,760

Systems administration*: 1.5 FTE at $150,937/year** $ 226,406 Hosting fees at $1,000/month for 30 machines *** $ 360,000

Total $768,716

Item Annual Cost

10 extra-large cloud servers running for a total of 6,570 hours per month at $.68 per hour. $53,611

RightScale subscription at $1,500/month plus $5,000 annual fee $23,000

Systems administration*: 0.6 FTE at $150,937/year** $90,562

Total $167,173

In-house Grid – with commercial grid software

Cloud-based Grid Computing

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Contact us:• sales@rightscale.com• (866) 720-0208• Twitter: @rightscale

Grid Computing Whitepaper: www.rightscale.com/gridsolution

Webinar Archive: www.rightscale.com/webinars

Q&A - Getting Started

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Thank You!