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Introduction to Data Center Computing

Explore techniques for handling big data, distributed storage, and distributed computation in data centers. Focus on recent papers describing real systems.

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Introduction to Data Center Computing

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  1. Introduction to Data Center Computing Derek Murray

  2. What we’ll cover • Techniques for handling “big data” • Distributed storage • Distributed computation • Focus on recent papers describing real systems

  3. Example: web search Crawling Indexing Querying WWW

  4. A system architecture? Network Computers Storage

  5. Data Center architecture Core switch Rack switch Rack switch Rack switch Server Server Server Server Server Server Server Server Server

  6. Distributed storage • High volume of data • High volume of read/writerequests • Fault tolerance

  7. Brewer’s CAP theorem

  8. The Google file system GFS Master Client Chunk server Chunk server Chunk server

  9. Dynamo Client

  10. Distributed computation • Parallel distributed processing • Single Program, Multiple Data (SPMD) • Fault tolerance • Applications

  11. Task farming Master Worker Worker Worker Storage

  12. MapReduce Input Map Shuffle Reduce Output

  13. Dryad • Arbitrary task graph • Vertices and channels • Topological ordering

  14. DryadLINQ • Language Integrated Query (LINQ) vartable = PartitionedTable.Get<int>(“…”); varresult = from x in table selectx * x; intsumSquares = result.Sum();

  15. Scheduling issues • Heterogeneous performance • Sharing a cluster fairly • Data locality

  16. References • Storage • Ghemawatet al., “The Google File System”, Proceedings of SOSP 2003 • DeCandiaet al., “Dynamo: Amazon’s Highly-Available Key-value Store”, Proceedings of SOSP 2007 • Burrows, “The Chubby lock service for loosely-coupled distributed systems”, Proceedings of OSDI 2006 • Computation • Dean and Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters”, Proceedings of OSDI 2004 • Isardet al., “Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks”, Proceedings of EuroSys 2007 • Yu et al., “DryadLINQ: A System for General-Purpose Distributed Data-Parallel Computing Using a High-Level Language”, Proceedings of OSDI 2008 • Olstonet al., “Pig Latin: A Not-So-Foreign Language for Data Processing”, Proceedings of SIGMOD 2008 • Scheduling • Zahariaet al., “Improving MapReduce Performance in Heterogeneous Environments”, Proceedings of OSDI 2008 • Isardet al., “Quincy: Fair Scheduling for Distributed Computing Clusters”, Proceedings of SOSP 2009 • Zahariaet al., “Delay Scheduling: A Simple Technique for Achieving Locality and Fairness in Cluster Scheduling”, Proceedings of EuroSys 2010

  17. Conclusions • Data centers achieve high performance with commodity parts • Efficient storage requires application-specific trade-offs • Data-parallelism simplifies distributed computation on the data

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