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CDRM: A Cost-effective Dynamic Replication Management Scheme for Cloud Storage Cluster

The IEEE International Conference on Cluster Computing 2010. CDRM: A Cost-effective Dynamic Replication Management Scheme for Cloud Storage Cluster. Qingsong Wei Data Storage Institute, A-STAR, Singapore Bharadwaj Veeravalli , Bozhao Gong National University of Singapore

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CDRM: A Cost-effective Dynamic Replication Management Scheme for Cloud Storage Cluster

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  1. The IEEE International Conference on Cluster Computing 2010 CDRM: A Cost-effective Dynamic Replication Management Scheme for Cloud Storage Cluster Qingsong Wei Data Storage Institute, A-STAR, Singapore Bharadwaj Veeravalli, Bozhao Gong National University of Singapore Lingfang Zeng, Dan Feng Huazhong University of Science & Technology, China

  2. Outline Agenda • Introduction • Problem Statement • Cost-effective Dynamic Replication Management (CDRM) • Evaluation • Conclusion

  3. Outline Node Disk Node Disk Node Disk Node Disk Node Disk Node Disk Disks Disks Disks Disks Disks Disks 1, Introduction HDFS Architecture Clients Meta Data Name Node Network Control Data Blocks Data Nodes

  4. 1. Introduction • In the HDFS, files are striped into date blocks across multiple data nodes to enable parallel access. B1 B2 … Bm Data Striping … Node1 Node2 Noden • However, Block may be unaccessible due to date node unavailable. If one of the blocks is unavailable, so as the whole file. • Failure is normal instead of exception in large scale storage cloud system. Fault tolerance is required in such a system. Page 4 of 19

  5. 1. Introduction Clients 1 1 2 5 2 2 4 Data nodes 3 4 5 4 3 5 • Replication is used in HDFS. • When one data node fails, the data is still accessible from the replicas and storage service need not be interrupted. • Besides fault tolerance, replicas among data nodes can be used to balance workload. Page 5 of 19

  6. 2. Problem Statement • Current replication managements • Treat all data as same: same replica number for all data • Treat all storage nodes as same • Fixed and Static 1 2 3 4 5 5 1 2 3 4 4 5 1 2 3 • High cost & Poor load balance Page 6 of 19

  7. 2. Problem Statement • Replica number is critical to management cost. More replica, more cost. 1 2 1 5 2 2 4 The block 5 is modified 3 5 4 4 3 5 Update to maintain consistency • Because large number of blocks are stored in system, even a small increase of replica number can result in a significant increase of management cost in the overall system. • Then, how many minimal replica should be kept in the system to satisfy availability requirement? Page 7 of 19

  8. 2. Problem Statement • Replica placement influences intra-requestparallelism. Client File (B1, B2, B3) B3 B2 B1 Requests Blocked B2 B3 B1 B1 Data Node1 Data Node2 Data Node3 Sessionmax=2 Sessionfree=0 Sessionmax=3 Sessionfree=2 Sessionmax=3 Sessionfree=1 Page 8 of 19

  9. 2. Problem Statement • Replica placement also influences inter-requestparallelism. Client1 Client2 B3 B2 B1 B1 Requests How to place these replicas among Data nodes clusters in a balance way to improve access parallelism? B2 B1 B3 B1 Data Node1 Data Node2 Data Node3 Sessionmax=2 Sessionfree=0 Sessionmax=3 Sessionfree=1 Sessionmax=3 Sessionfree=0 Page 9 of 19

  10. 3. Cost-effective Dynamic Replication Management • System Model pj : popularity sj : size rj : replica number tj : access latency requirement (p1, s1, r1, t1) (pj, sj, rj, tj) (pM, sM, rM, tM) …… …… B1 Bj BM Total arrival rate: λ λi : req. arr. rate τi : average ser. time fi: failure rate ci: max sessions Node1 … … NodeN Nodei (λ1, τ1, f1, c1) (λi, τi, fi, ci) (λN, τN, fN, cN) • Data has different attributes • Data nodes are different Page 10 of 19

  11. 3. Cost-effective Dynamic Replication Management • Availability • Suppose file F is striped into m blocks {b1 , b2 ,…, bm}. To retrieve whole file F, we must get all the m blocks. • Availability is modeled as function of replica number. • Suppose the expected availability for file F is Aexpect, which defined by users. To satisfy the availability requirement for a given file, we get Minimum replicas can be calculated from above Eq. for a given expected availability. Page 11 of 19

  12. 3. Cost-effective Dynamic Replication Management • Blocking Probability • Blocking probability is used as criterion to place replicas among data nodes to improve load balance . • An data node Siis modeled as M/G/ci system with arrival rate λi and service time τi, and accordingly, the blocking probability of data node Sican be given to be Replica placement policy: replica will be placed into data node with lowest blocking probability to dynamically maintain overall load balancing. Page 12 of 19

  13. 3. Cost-effective Dynamic Replication Management Request to create a file with <Availability, Block Number> 1 Client Name Node Bm … B2 B1 Return replication policy <Bi, Replication factor, DataNode list> 3 Calculate the replication factor and Search the Datanode B+Tree to obtain Datanode list. 2 Flush and replicate blocks to selected Datanodes 4 Data Nodes Replication Pipelining Framework of cost-effective dynamic replication management in HDFS Page 13 of 19

  14. 4. Evaluation • Setup • Our test platform is built on a cluster with one name node and twenty data nodes of commodity computer • The operating system is Red Hat AS4.4 with kernel 2.6.20. • Hadoop version is 0.16.1 and java version is 1.6.0. • AUSPEX file system trace is used • A synthesizer is developed to create workloads with different characteristics, such as data sets of different sizes, varying data rates, and different popularities. These characteristics reflect the differences among various workloads to the cloud storage cluster. Page 14 of 19

  15. 4. Evaluation • Cost effective Availability • Initially, one replica per object. • CDRM only maintain minimal replicas to satisfy availability. • Higher failure rate, more replica required. Dynamic replication with Data node failure rate of 0.1 and 0.2 , Aexpect=0.8 Page 15 of 19

  16. 4. Evaluation • Performance • CDRM vs. HDFS default Replication Management (HDRM) under different popularity and workload intensity. • Performance of CDRM is much better than that of HDRM when popularity is small. • CDRM outperform HDRM under heavy workload. Page 16 of 19 Effect of popularity and access arrival rate, 20 data nodes

  17. 4. Evaluation • Load Balance • The figure shows the difference of system utilization of each data node comparing to the average system utilization of the cluster. • CDRM can dynamically distribute workload among whole cluster. System utilization among data nodes, popularity=10%, λ=0.6 Page 17 of 19

  18. 5. Conclusion Page 18 of 19

  19. Thanks & Question For more questions, please contact Dr. Qingsong Wei by email: WEI_Qingsong@dsi.a-star.edu.sg Page 19 of 19

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