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Cloud Computing Ed Lazowska Bill & Melinda Gates Chair in Computer Science & Engineering University of Washington August 2010
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Personal computing Office applications Databases and storage Email Math and science Web browser
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Personal computing Office applications Math and science Web browser Email Databases and storage
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Math and science Email Databases and storage Office applications Personal computing Web browser
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Consider … z Sharing z Backup z Software updates z Demands on the operating system z Business models!
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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
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z This includes y Purchase + replacement y Housing y Power y Operation y Reliability y Security y Instantaneous expansion and contraction
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Slide courtesy of Werner Vogels
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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?
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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
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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
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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
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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.
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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
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