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Introduction to Data Center Computing

Learn about handling big data, distributed storage, computation, and recent systems from real papers. Dive into key concepts such as Brewer’s CAP theorem, Google File System, Dynamo, MapReduce, and Dryad. Understand scheduling, performance, and data locality.

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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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