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  • Standards-Based Data Center Structured Cabling System Design 3/20/06 Copyright (c) 2006 Ortronics/Legrand. All rights reserved. JS
  • Map reducecloudtech

    1. 1. Cloud Computing Systems Lin Gu Hong Kong University of Science and Technology Sept. 14, 2011
    2. 2. How to effectively compute in a datacenter? Is MapReduce the best answer to computation in the cloud? What is the limitation of MapReduce? How to provide general-purpose parallel processing in DCs?
    3. 3. • MapReduce—parallel computing for Web-scale data processing • Fundamental component in Google’s technological architecture – Why didn’t Google use parallel Fortran, MPI, …? • Followed by many technology firms The MapReduce Approach Program Execution on Web-Scale DataProgram Execution on Web-Scale Data
    4. 4. MapReduce Old ideas can be fabulous, too! ( = Lisp “Lost In Silly Parentheses”) ? • Map and Fold – Map: do something to all elements in a list – Fold: aggregate elements of a list • Used in functional programming languages such as Lisp
    5. 5. • Map is a higher-order function: apply an op to all elements in a list – Result is a new list • Parallelizable f f f f f MapReduce (map (lambda (x) (* x x)) '(1 2 3 4 5)) → '(1 4 9 16 25)
    6. 6. • Reduce is also a higher-order function • Like “fold”: aggregate elements of a list – Accumulator set to initial value – Function applied to list element and the accumulator – Result stored in the accumulator – Repeated for every item in the list – Result is the final value in the accumulator f f f f f final result Initial value (fold + 0 '(1 2 3 4 5)) → 15 (fold * 1 '(1 2 3 4 5)) → 120 The MapReduce Approach Program Execution on Web-Scale DataProgram Execution on Web-Scale Data
    7. 7. Massive parallel processing made simple • Example: word count • Map: parse a document and generate <word, 1> pairs • Reduce: receive all pairs for a specific word, and count (sum) // D is a document for each word w in D output <w, 1> Map Reduce Reduce for key w: count = 0 for each input item count = count + 1 output <w, count> The MapReduce Approach Program Execution on Web-Scale DataProgram Execution on Web-Scale Data
    8. 8. Design Context • Big data, but simple dependence – Relatively easy to partition data • Supported by a distributed system – Distributed OS services across thousands of commodity PCs (e.g., GFS) • First users are search oriented – Crawl, index, search Designed years ago, still working today, growing adoptions
    9. 9. Single Master node Worker threads Worker threads Workflow Single master, numerous worker threads
    10. 10. Workflow • 1. The MapReduce library in the user program first splits the input files into M pieces of typically 16 megabytes to 64 megabytes (MB) per piece. It then starts up many copies of the program on a cluster of machines. • 2. One of the copies of the program is the master. The rest are workers that are assigned work by the master. There are M map tasks and R reduce tasks to assign. The master picks idle workers and assigns each one a map task or a reduce task.
    11. 11. Workflow • 3. A worker who is assigned a map task reads the contents of the corresponding input split. It parses key/value pairs out of the input data and passes each pair to the user-defined Map function. The intermediate key/value pairs produced by the Map function are buffered in memory. • 4. Periodically, the buffered pairs are written to local disk, partitioned into R regions by the partitioning function. The locations of these buffered pairs on the local disk are passed back to the master, who is responsible for forwarding these locations to the reduce workers.
    12. 12. Workflow • 5. When a reduce worker is notified by the master about these locations, it uses RPCs to read the buffered data from the local disks of the map workers. When a reduce worker has read all intermediate data, it sorts it by the intermediate keys so that all occurrences of the same key are grouped together. • 6. The reduce worker iterates over the sorted intermediate data and for each unique intermediate key encountered, it passes the key and the corresponding set of intermediate values to the Reduce function. The output of the Reduce function is appended to a final output file for this reduce partition. • 7. When all map tasks and reduce tasks have been completed, the MapReduce returns back to the user code.
    13. 13. Programming • How to write a MapReduce programto –Generate inverted indices? –Sort? • How to express more sophisticated logic? • What if some workers (slaves) or the master fails?
    14. 14. Workflow Where is the communication-intensive part? Initial data split into 64MB blocks Computed, results locally stored Master informed of result locations R reducers retrieve Data from mappers Final output written
    15. 15. • Distributed, scalable storage for key-value pairs • Example: Dynamo (Amazon) • Another example may be P2P storage (e.g., Chord) • Key-value store can be a general foundation for more complex data structures • But performance may suffer Data Storage – Key-Value Store
    16. 16. Data Storage – Key-Value Store Dynamo: a decentralized, scalable key-value store – Used in Amazon – Use consistent hashing to distributed data among nodes – Replicated, versioning, load balanced – Easy-to-use interface: put()/get()
    17. 17. • Networked block storage – ND by SUN Microsystems • Remote block storage over Internet – Use S3 as a block device [Brantner] • Block-level remote storage may become slow in networks with long latencies Data Storage – Network Block Device
    18. 18. • PC file systems • Link together all clusters of a file – Directory entry: filename, attributes, date/time, starting cluster, file size • Boot sector (superblock) : file system wide information • File allocation table, root directory, … Data Storage – Traditional File Systems Boot sector FAT 1 FAT 2 (dup) ROOT dir Normal directories and files
    19. 19. • NFS—Network File System [Sandberg] – Designed by SUN Microsystems in the 1980’s • Transparent remote access to files stored remotely – XDR, RPC, VNode, VFS – Mountable file system, synchronous behavior • Stateless server Data Storage – Network File System
    20. 20. NFS organization Client Server Data Storage – Network File System
    21. 21. • A distributed file system at work (GFS) • Single master and numerous slaves communicate with each other • File data unit, “chunk”, is up to 64MB. Chunks are replicated. • “master” is a single point of failure and bottleneck of scalability, the consistency model is difficult to use Data Storage – Google File System (GFS)
    22. 22. 22 E 75656 C A 42342 E B 42521 W C 66354 W D 12352 E F 15677 E E 75656 C A 42342 E B 42521 W C 66354 W D 12352 E F 15677 E CREATE TABLE Parts ( ID VARCHAR, StockNumber INT, Status VARCHAR … ) CREATE TABLE Parts ( ID VARCHAR, StockNumber INT, Status VARCHAR … ) Parallel databaseParallel database ReplicationReplication Indexes and viewsIndexes and views Structured schemaStructured schema A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E Data Storage – Database Designed and used by Yahoo! PNUTS – a relational database service
    23. 23. MapReduce/Hadoop • Around 2004, Google invented MapReduce to parallelize computation of large data sets. It’s been a key component in Google’s technology foundation • Around 2008, Yahoo! developed the open-source variant of MapReduce named Hadoop • After 2008, MapReduce/Hadoop become a key technology component in cloud computing • In 2010, the U.S. conferred the MapReduce patent to Google MapReduce … Hadoop or variants …Hadoop
    24. 24. • MapReduce provides an easy-to-use framework for parallel programming, but is it the most efficient and best solution to program execution in datacenters? • MapReduce has its discontents – DeWitt and Stonebraker: “MapReduce: A major step backwards” – MapReduce is far less sophisticated and efficient than parallel query processing • MapReduce is a parallel processing framework, not a database system, nor a query language – It is possible to use MapReduce to implement some of the parallel query processing functions – What are the real limitations? • Inefficient for general programming (and not designed for that) – Hard to handle data with complex dependence, frequent updates, etc. – High overhead, bursty I/O, difficult to handle long streaming data – Limited opportunity for optimization MapReduce—LimitationsMapReduce—Limitations
    25. 25. Critiques MapReduce: A major step backwards -- David J. DeWitt and Michael Stonebraker (MapReduce) is – A giant step backward in the programming paradigm for large- scale data intensive applications – A sub-optimal implementation, in that it uses brute force instead of indexing – Not novel at all – Missing features – Incompatible with all of the tools DBMS users have come to depend on
    26. 26. • Inefficient for general programming (and not designed for that) – Hard to handle data with complex dependence, frequent updates, etc. – High overhead, bursty I/O • Experience with developing a Hadoop-based distributed compiler – Workload: compile Linux kernel – 4 machines available to Hadoop for parallel compiling – Observation: parallel compiling on 4 nodes with Hadoop can be even slower than sequential compiling on one node MapReduce—LimitationsMapReduce—Limitations
    27. 27. • Proprietary solution developed in an environment with one prevailing application (web search) – The assumptions introduce several important constraints in data and logic – Not a general-purpose parallel execution technology • Design choices in MapReduce – Optimizes for throughput rather than latency – Optimizes for large data set rather than small data structures – Optimizes for coarse-grained parallelism rather than fine- grained Re-thinking MapReduceRe-thinking MapReduce
    28. 28. • A lightweight parallelization framework following the MapReduce paradigm – Implemented in C++ – More than just an efficient implementation of MapReduce – Goal: a lightweight “parallelization” service that programs can invoke during execution • MRlite follows several principles – Memory is media—avoid touching hard drives – Static facility for dynamic utility—use and reuse threads for map tasks MRlite: Lightweight Parallel ProcessingMRlite: Lightweight Parallel Processing
    29. 29. MRlite : Towards Lightweight, Scalable, and General Parallel Processing MRlite clientMRlite client MRlite master scheduler MRlite master scheduler slaveslave slaveslave slaveslave slaveslave applicationapplication Data flow Command flow Linked together with the app, the MRlite client library accepts calls from app and submits jobs to the master Linked together with the app, the MRlite client library accepts calls from app and submits jobs to the master High speed distributed storage, stores intermediate files High speed distributed storage, stores intermediate files The MRlite master accepts jobs from clients and schedules them to execute on slaves The MRlite master accepts jobs from clients and schedules them to execute on slaves Distributed nodes accept tasks from master and execute them Distributed nodes accept tasks from master and execute them
    30. 30. 30 Computing Capability Using MRlite, the parallel compilation jobs, mrcc, is 10Using MRlite, the parallel compilation jobs, mrcc, is 10 times faster than that running on Hadoop!times faster than that running on Hadoop! Z. Ma and L. Gu. The Limitation of MapReduce: a Probing Case and a Lightweight Solution. CLOUD COMPUTING 2010
    31. 31. Network activities under MapReduce/Hadoop workload • Hadoop: open-source implementation of MapReduce • Processing data with 3 servers (20 cores) – 116.8GB input data • Network activities captured with Xen virtual machines Inside MapReduce-Style ComputationInside MapReduce-Style Computation
    32. 32. Workflow Where is the communication-intensive part? Initial data split into 64MB blocks Computed, results locally stored Master informed of result locations R reducers retrieve Data from mappers Final output written
    33. 33. • Packet reception under MapReduce/Hadoop workload – Large data volume – Bursty network traffic • Genrality—widely observed in MapReduce workloads Packet reception on a slave server Inside MapReduceInside MapReduce
    34. 34. Packet reception on the master server Inside MapReduceInside MapReduce
    35. 35. Packet transmission on the master server Inside MapReduceInside MapReduce
    36. 36. Major Components of a Datacenter • Computing hardware (equipment racks) • Power supply and distribution hardware • Cooling hardware and cooling fluid distribution hardware • Network infrastructure • IT Personnel and office equipment Datacenter Networking
    37. 37. Growth Trends in Datacenters • Load on network & servers continues to rapidly grow – Rapid growth: a rough estimate of annual growth rate: enterprise data centers: ~35%, Internet data centers: 50% - 100% – Information access anywhere, anytime, from many devices • Desktops, laptops, PDAs & smart phones, sensor networks, proliferation of broadband • Mainstream servers moving towards higher speed links – 1-GbE to10-GbE in 2008-2009 – 10-GbE to 40-GbE in 2010-2012 • High-speed datacenter-MAN/WAN connectivity – High-speed datacenter syncing for disaster recovery Datacenter Networking
    38. 38. • A large part of the total cost of the DC hardware – Large routers and high-bandwidth switches are very expensive • Relatively unreliable – many components may fail. • Many major operators and companies design their own datacenter networking to save money and improve reliability/scalability/performance. – The topology is often known – The number of nodes is limited – The protocols used in the DC are known • Security is simpler inside the data center, but challenging at the border • We can distribute applications to servers to distribute load and minimize hot spots Datacenter Networking
    39. 39. Networking components (examples) • High Performance & High Density Switches & Routers – Scaling to 512 10GbE ports per chassis – No need for proprietary protocols to scale • Highly scalable DC Border Routers – 3.2 Tbps capacity in a single chassis – 10 Million routes, 1 Million in hardware – 2,000 BGP peers – 2K L3 VPNs, 16K L2 VPNs – High port density for GE and 10GE application connectivity – Security 768 1-GE port Downstream 64 10-GE port Upstream Datacenter Networking
    40. 40. Common data center topology Internet Servers Layer-2 switchAccess Data Center Layer-2/3 switchAggregation Layer-3 routerCore Datacenter Networking
    41. 41. Data center network design goals • High network bandwidth, low latency • Reduce the need for large switches in the core • Simplify the software, push complexity to the edge of the network • Improve reliability • Reduce capital and operating cost Datacenter Networking
    42. 42. Avoid this… Data Center Networking and simplify this…and simplify this…
    43. 43. ?? Can we avoid using high-end switches? • Expensive high-end switches to scale up • Single point of failure and bandwidth bottleneck – Experiences from real systems • One answer: DCell 43 Interconnect
    44. 44. DCell Ideas • #1: Use mini-switches to scale out • #2: Leverage servers to be part of the routing infrastructure – Servers have multiple ports and need to forward packets • #3: Use recursion to scale and build complete graph to increase capacity Interconnect
    45. 45. One approach: switched network with a hypercube interconnect • Leaf switch: 40 1Gbps ports+2 10 Gbps ports. – One switch per rack. – Not replicated (if a switch fails, lose one rack of capacity) • Core switch: 10 10Gbps ports – Form a hypercube • Hypercube – high-dimensional rectangle Data Center Networking
    46. 46. Hypercube properties • Minimum hop count • Even load distribution for all-all communication. • Can route around switch/link failures. • Simple routing: – Outport = f(Dest xor NodeNum) – No routing tables Interconnect
    47. 47. A 16-node (dimension 4) hypercube 0 32 10 0 12 3 3 1 30 0 2 1 5 47 3 2 10 118 1 1 1 1 1 2 2 22 2 0 0 0 0 3 3 3 3 Interconnect
    48. 48. Interconnect How many servers can be connected in this system? 81920 servers with 1Gbps bandwidth Core switch: 10Gbps port x 10 Leaf switch: 1Gbps port x 40 + 10Gbps port x 2.
    49. 49. The Black Box Data Center Networking
    50. 50. Shipping Container as Data Center Module • Data Center Module – Contains network gear, compute, storage, & cooling – Just plug in power, network, & chilled water • Increased cooling efficiency – Water & air flow – Better air flow management • Meet seasonal load requirements Data Center Network
    51. 51. Unit of Data Center Growth • One at a time: – 1 system – Racking & networking: 14 hrs ($1,330) • Rack at a time: – ~40 systems – Install & networking: .75 hrs ($60) • Container at a time: – ~1,000 systems – No packaging to remove – No floor space required – Power, network, & cooling only – Weatherproof & easy to transport • Data center construction takes 24+ months Data Center Network
    52. 52. Multiple-Site Redundancy and Enhanced Performance using load balancing • Handling site failures transparently • Providing best site selection per user • Leveraging both DNS and non-DNS methods for multi-site redundancy • Providing disaster recovery and non-stop operation LB system DNS Datacenter Datacenter Datacenter LB (load balancing) System • The load balancing systems regulate global data center traffic • Incorporates site health, load, user proximity, and service response for user site selection • Provides transparent site failover in case of disaster or service outage Global Data Center Deployment Problems Data Center Network
    53. 53. Challenges and Research Problems Hardware – High-performance, reliable, cost-effective computing infrastructure – Cooling, air cleaning, and energy efficiency [Barraso] Clusters [Fan] Power [Andersen] FAWN [Reghavendra] Power
    54. 54. Challenges and Research Problems System software – Operating systems – Compilers – Database – Execution engines and containers Ghemawat: GFS Chang: Bigtable DeCandia: Dynamo Brantner: DB on S3 Cooper: PNUTS Yu: DryadLINQ Dean: MapReduce Burrows: Chubby Isard: Quincy
    55. 55. Challenges and Research Problems Networking – Interconnect and global network structuring – Traffic engineering Al-Fares: Commodity DC Guo 2008: DCell Guo 2009: BCube
    56. 56. Challenges and Research Problems • Data and programming – Data consistency mechanisms (e.g., replications) – Fault tolerance – Interfaces and semantics • Software engineering • User interface • Application architecture Pike: Sawzall Olston: Pig Latin Buyya: IT services
    57. 57. Resources • [Al-Fares] Al-Fares, M., Loukissas, A., and Vahdat, A. A scalable, commodity data center network architecture. In Proceedings of the ACM SIGCOMM 2008 Conference on Data Communication (Seattle, WA, USA, August 17 - 22, 2008). SIGCOMM '08. 63-74. • [Andersen] David G. Andersen, Jason Franklin, Michael Kaminsky, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan. FAWN: A Fast Array of Wimpy Nodes. SOSP'09. • [Barraso] Luiz Barroso, Jeffrey Dean, Urs Hoelzle, "Web Search for a Planet: The Google Cluster Architecture," IEEE Micro, vol. 23, no. 2, pp. 22-28, Mar./Apr. 2003 • [Brantner] Brantner, M., Florescu, D., Graf, D., Kossmann, D., and Kraska, T. Building a database on S3. In Proceedings of the 2008 ACM SIGMOD international Conference on Management of Data (Vancouver, Canada, June 09 - 12, 2008). SIGMOD '08. 251-264.
    58. 58. Resources • [Burrows] Burrows, M. The Chubby lock service for loosely-coupled distributed systems. In Proceedings of the 7th Symposium on Operating Systems Design and Implementation (Seattle, Washington, November 06 - 08, 2006). 335-350. . • [Buyya] Buyya, R. Chee Shin Yeo Venugopal, S. Market-Oriented Cloud Computing. The 10th IEEE International Conference on High Performance Computing and Communications, 2008. HPCC '08. • [Chang] Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A., and Gruber, R. E. Bigtable: a distributed storage system for structured data. In Proceedings of the 7th Symposium on Operating Systems Design and Implementation (Seattle, Washington, November 06 - 08, 2006). 205-218. • [Cooper] Cooper, B. F., Ramakrishnan, R., Srivastava, U., Silberstein, A., Bohannon, P., Jacobsen, H., Puz, N., Weaver, D., and Yerneni, R. PNUTS: Yahoo!'s hosted data serving platform. Proc. VLDB Endow. 1, 2 (Aug. 2008), 1277-1288.
    59. 59. Resources • [Dean] Dean, J. and Ghemawat, S. 2004. MapReduce: simplified data processing on large clusters. In Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation - Volume 6 (San Francisco, CA, December 06 - 08, 2004). • [DeCandia] DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., and Vogels, W. 2007. Dynamo: amazon's highly available key-value store. In Proceedings of Twenty-First ACM SIGOPS Symposium on Operating Systems Principles (Stevenson, Washington, USA, October 14 - 17, 2007). SOSP '07. ACM, New York, NY, 205-220. • [Fan] Fan, X., Weber, W., and Barroso, L. A. Power provisioning for a warehouse-sized computer. In Proceedings of the 34th Annual international Symposium on Computer Architecture (San Diego, California, USA, June 09 - 13, 2007). ISCA '07. 13-23.
    60. 60. Resources • [Ghemawat] Ghemawat, S., Gobioff, H., and Leung, S. 2003. The Google file system. In Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles (Bolton Landing, NY, USA, October 19 - 22, 2003). SOSP '03. ACM, New York, NY, 29-43. • [Guo 2008] Chuanxiong Guo, Haitao Wu, Kun Tan, Lei Shi, Yongguang Zhang, and Songwu Lu, DCell: A Scalable and Fault-Tolerant Network Structure for Data Centers, in ACM SIGCOMM 08. • [Guo 2009] Chuanxiong Guo, Guohan Lu, Dan Li, Xuan Zhang, Haitao Wu, Yunfeng Shi, Chen Tian, Yongguang Zhang, and Songwu Lu, BCube: A High Performance, Server- centric Network Architecture for Modular Data Centers, in ACM SIGCOMM 09. • [Isard] Michael Isard, Vijayan Prabhakaran, Jon Currey, Udi Wieder, Kunal Talwar and Andrew Goldberg. Quincy: Fair Scheduling for Distributed Computing Clusters. SOSP'09.
    61. 61. Resources • [Olston] Olston, C., Reed, B., Srivastava, U., Kumar, R., and Tomkins, A. 2008. Pig Latin: a not-so-foreign language for data processing. In Proceedings of the 2008 ACM SIGMOD international Conference on Management of Data (Vancouver, Canada, June 09 - 12, 2008). SIGMOD '08. 1099-1110. • [Pike] Pike, R., Dorward, S., Griesemer, R., and Quinlan, S. 2005. Interpreting the data: Parallel analysis with Sawzall. Sci. Program. 13, 4 (Oct. 2005), 277-298. • [Reghavendra] Ramya Raghavendra, Parthasarathy Ranganathan, Vanish Talwar, Zhikui Wang, Xiaoyun Zhu. No "Power" Struggles: Coordinated Multi-level Power Management for the Data Center. In Proceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Seattle, WA, March 2008. • [Yu] Y. Yu, M. Isard, D. Fetterly, M. Budiu, Ú. Erlingsson, P. K. Gunda, and J. Currey. DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language. In Proceedings of the 8th Symposium on Operating Systems Design and Implementation (OSDI), December 8-10 2008. tid=5
    62. 62. Thank you! Questions?