This document summarizes a presentation about harnessing the power of cloud computing for grid computing. It discusses how RightScale provides automated management of grid computing workloads in the cloud, allowing users to easily deploy and control large numbers of servers. Demos show how RightScale enables graceful scaling of server arrays, automated queue handling, and analyzing results to quantify economic benefits like cost savings and increased agility compared to on-premise grid solutions.
18. Customers on the Cloud Customers from Web 2.0 to Enterprise From to RightScale Grid Solution Pack Grid Worker Grid Worker Grid Worker Grid Worker Grid Worker Grid Worker Grid Worker
19. 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 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 Web 2.0 – Transcoding images to render video on demand • Processing time was reduced from hours on internal resources to minutes 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
25. Grid Computing managed by RightScale Grid Computing Managed by RightScale RightScale Fast and easy provisioning Preconfigured components and solutions Automation and systems management Reduced administration Control and visibility User and cost control
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27. Multiple AWS Regions and Availability ZonesRightScale Professional Services and Support
29. RightScale Grid ArchitectureAutomated server scaling, operational remediation, server cost optimization RightScale Management Interface SQS Output Queue SQS Input Queue SQS Error Queue Your code Amazon S3 Worker Daemon Amazon S3 Your application or next batch process job consumer Batch jobs from Your job producer application Scalable cloud servers using RightScale Server Templates RightScale Amazon Cloud Infrastructure Customer code
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31. Daemon Results Handling S3 Buckets Elasticity daemon IN OUT LOG Upload Output Files Upload log files Delete /tmp and /in files If worker returns success Queue Results in results_queue Post results to http server Else queue results to the error queue Queue statistics in Audit Queue Delete Message from input queue /local Worker code /tmp /in /out /log Results Queue {Results} {Results} Consumer } { Result Error Queue {Results} {Results} Consumer LEGEND { } Audit Queue {Audit} {Audit} Consumer Data Structure AWS Instance
41. Agility Example: Cost-neutral Equation Total processing time: 500 hours 10,000 jobs Output data 2 server cloud Total processing time: 1 hour 10,000 jobs Output data 1000 server cloud 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)
42. Example Cost Savings –1/4 The Price In-house Grid – with commercial grid software Cloud-based Grid Computing