An unstated assumption is that clouds are scalable. But are they? Stick thousands upon thousands of machines together and there are a lot of potential bottlenecks just waiting to choke off your scalability supply. And if the cloud is scalable what are the chances that your application is really linearly scalable? At 10 machines all may be well. Even at 50 machines the seas look calm. But at 100, 200, or 500 machines all hell might break loose. How do you know? You know through real life testing. These kinds of tests are brutally hard and complicated. who wants to do all the incredibly precise and difficult work of producing cloud scalability tests? GridDynamics has stepped up to the challenge and has just released their Cloud Performance Reports.

1. Victoria Livschitz, CEO Grid Dynamics [email_address] September 17 th , 2008 Using Grid Technologies on the Cloud for High Scalability A Practitioner Report for Cloud User Group

4. Exploring Scalability thru Benchmarking Benchmark Cloud Vendor Middleware Application 1. Test scalability of EC2 on the simplest map-reduce problem Public commercial cloud, EC2 Amazon GridGain Monte-Carlo 2. Test scalability of data-driven HPC applications, similar to those used in practice Public commercial cloud, EC2 Amazon GigaSpaces Risk Management 3. Explore performance implications of data “in the cloud” vs. “outside the cloud” Incubator compute cloud for academic use, CompFin Microsoft Windows HPC Server, Velocity Data-intensive Analytics

5. Benchmark #1: Scalability of Simple Map/Reduce Application on EC2

16. Benchmark #2: Scalability of Data-Driven Risk Management Application on EC2

18. Architecture Workers take tasks, perform calculations, write results back User uses ec2-gdc-tools to manage grid Master writes tasks into data grid and waits for results… Amazon EC2 Grid Compute Grid Data Grid Grid Console Service Grid Manager Master

21. Benchmark #3: Performance implications of data “in the cloud” vs. “outside the cloud” for data-intensive analytics applications

23. Architecture: CompFin

24. Architecture: Anticipated Bottlenecks

25. Architecture: CompFin + Velocity

28. Performance results

29. Performance results

32. Victoria Livschitz [email_address] Thank You!