1. Cloud Computing Ed Lazowska Bill & Melinda Gates Chair in Computer Science & Engineering University of Washington August 2010

2. Personal computing Office applications Databases and storage Email Math and science Web browser

3. Personal computing Office applications Math and science Web browser Email Databases and storage

7. Math and science Email Databases and storage Office applications Personal computing Web browser

9. Consider … z Sharing z Backup z Software updates z Demands on the operating system z Business models!

11. Amazon Elastic Compute Cloud (EC2) z $0.68 per hour for y 8 cores of 3 GHz 64-bit Intel or AMD y 7 GB memory y 1.69 TB scratch storage z Need it 24x7 for a year? y $3900 z $0.085 per hour for y 1 core of 1.2 GHz 32-bit Intel or AMD (1/20 th the above) y 1.7 GB memory y 160 GB scratch storage z Need it 24x7 for a year? y $490

12. z This includes y Purchase + replacement y Housing y Power y Operation y Reliability y Security y Instantaneous expansion and contraction

13. Slide courtesy of Werner Vogels

16. z Many hundreds of machines are involved in a single Google search request (remember, the web is 400+TB) y There are multiple clusters (of thousands of computers each) all over the world y DNS routes your search to a nearby cluster Isn’t this just timesharing?

17. y A cluster consists of Google Web Servers, Index Servers, Doc Servers, and various other servers (ads, spell checking, etc.) y These are cheap standalone computers, rack-mounted, connected by commodity networking gear

18. y Within the cluster, load-balancing routes your search to a lightly-loaded Google Web Server (GWS), which will coordinate the search and response y The index is partitioned into “shards.” Each shard indexes a subset of the docs (web pages). Each shard is replicated, and can be searched by multiple computers – “index servers” y The GWS routes your search to one index server associated with each shard, through another load-balancer y When the dust has settled, the result is an ID for every doc satisfying your search, rank-ordered by relevance

19. y The docs, too, are partitioned into “shards” – the partitioning is a hash on the doc ID. Each shard contains the full text of a subset of the docs. Each shard can be searched by multiple computers – “doc servers” y The GWS sends appropriate doc IDs to one doc server associated with each relevant shard y When the dust has settled, the result is a URL, a title, and a summary for every relevant doc

20. y Meanwhile, the ad server has done its thing, the spell checker has done its thing, etc. y The GWS builds an HTTP response to your search and ships it off z Many hundreds of computers have enabled you to search 400+TB of web in ~100 ms.

21. z Enormous volumes of data z Extreme parallelism z The cheapest imaginable components y Failures occur all the time y You couldn’t afford to prevent this in hardware z Software makes it y Fault-Tolerant y Highly Available y Recoverable y Consistent y Scalable y Predictable y Secure