Presented at SDForum. October 2009

2. Cloud Computing.... Virtualization Grid computing Application hosting Utility computing Platform as a service Infrastructure as a service Software as a service

4. Analogy with the evolution of electrical power From each company generating their own power to a utility

5. Power generation technology

6. Distribution is key

7. When and will this happen? 2000 2005 2010? 2015? 2020? 2025? Utility / Cloud Computing Private DataCenters

8. Clouds reach the tipping point – AWS Bandwidth exceeds Amazon.com

9. Major driver - Web API's

11. History lesson: 1987 Connection Machine 65,536 processors and lots of blinking lights

12. Truly Massive Scale TACC Sun Magnum 3456 Switch TACC Supercomputer center

14. Data Center Level Computing Google’s new data center on the Columbia river, Oregon Thousands and thousands of commodity parts built into a system to essentially serve a single application Power and Cooling major drivers of cost

15. Data + compute -> new opportunities 'Semi-structured' data emerges Mogile, Bigtable, Hypertable ... New 'Analytics' emerge MapReduce, Hadoop ... New 'Compute' New 'Data'

16. More data each and every day

17. How do the new guys compete? Web server app server database server Storage system Web server Web server app server database server Storage system Load balancer distributed memory cache

22. Patterns emerge in architecture - learn from others

24. Automation – scaling from 50 to 3500 servers in 3 days 4/11 4/12 4/13 4/13 4/14 4/15 4/15 4/16 4/17 4/17 4/18 4000 3000 2000 1500 1000 500 50 Animoto case study Servers

25. Map Reduce design pattern Documents Map <word, cnt> Map <word, cnt> Map <word, cnt> hash(word, mod R)‏ Reduce <word, cnt> Reduce <word, cnt> Reduce <word, cnt> Result <apple, 2,045,455> <barn, 254,345> Example: compute word frequency over a large set of documents Map <word, cnt> Result <house, 111,341> <kitchen, 4,678> Result <cat, 1,033,746> <horse, 25,387> Reduce workers: reads and sorts Map output, invoking Reduce() task which sums counts for each word Map workers: reads input, invoking Map() function which finds words and outputs records for reduce workers P 1 P 1 P 1 P 1

28. The Network is YOUR Computer

29. Thank you Lew Tucker Sun Microsystems, Inc