1. iMapReduce: A Distributed Computing Framework for Iterative Computation Yanfeng Zhang, Northeastern University, China Qixin Gao, Northeastern University, China Lixin Gao, UMass Amherst Cuirong Wang, Northeastern University, China

2. Iterative Computation Everywhere PageRank Clustering BFS Video suggestion Pattern Recognition

3. Iterative Computation on Massive Data PageRank Clustering BFS Video suggestion Pattern Recognition 100 million videos 700PB human genomics 29.7 billion pages 500TB/yr hospital Google maps 70.5TB

4. Large-Scale Solution MapReduce Programming model –Map: distribute computation workload –Reduce: aggregate the partial results But, no support of iterative processing –Batched processing –Must design a series of jobs, do the same thing many times Task reassignment, data loading, data dumping

5. Outline Introduction Example: PageRank in MapReduce System Design: iMapReduce Evaluation Conclusions

6. PageRank Each node associated with a ranking score R –Evenly to its neighbors with portion d –Retain portion (1-d)

7. PageRank in MapReduce 0 1:2,3 1 1:3 2 1:0,3 3 1:4 4 1:1,2,3 map 2 1/2 3 1/2 reduce 4 1 3 1 1 1/3 2 1/3 3 1/3 0 1/2 3 1/2 1 0.33 3 2.33 0 0.5 2 0.83 4 1 0 p:2,3 1p:3 2 p:0,3 3p:4 4p:1,2,3 0 0.5:2,3 2 0.83:0,3 4 1:1,2,3 1 0.33:3 3 2.33:4 for the next MapReduce job

8. Problems of Iterative Implementation 1. Multiple times job/task initializations 2. Re-shuffling the static data 3. Synchronization barrier between jobs

9. Outline Introduction Example: PageRank System Design: iMapReduce Evaluation Conclusions

10. iMapReduce Solutions 1. Multiple job/task initializations –Iterative processing 2. Re-shuffling the static data –Maintain static data locally 3. Synchronization barrier between jobs –Asynchronous map execution

11. Iterative Processing Reduce  Map –Load/dump once –Persistent map/reduce task –Each reduce task has a correspondent map task –Locally connected

12. Maintain Static Data Locally Iterate state data –Update iteratively Maintain static data –Join with state data at map phase

13. State Data Static Data 0 1 2 1 4 1 1 3 1 map 0map 1 2 1/2 3 1/2 reduce 0reduce 1 4 1 3 1 1 1/3 2 1/3 3 1/3 0 1/2 3 1/2 1 0.33 3 2.33 0 0.5 2 0.83 4 1 for the next iteration 0 2,3 2 0,3 4 1,2,3 1 3 3 4

14. Asynchronous Map Execution Persistent socket reduce -> map Reduce completion directly trigger map Map tasks no need to wait MapReduceiMapReduce time

15. Outline Introduction Example: PageRank System Design: iMapReduce Evaluation Conclusions

16. Experiment Setup Cluster –Local cluster: 4 nodes –Amazon EC2 cluster: 80 small instances Implemented algorithms –Shortest path, PageRank, K-Means Data sets SSSPPageRank

17. Results on Local Cluster PageRank: google webgraph iMapReduce Hadoop Sync. iMapReduce Hadoop ex. initialization

18. Results cont. PageRank: google webgraphPageRank: Berk-Stan webgraph SSSP: Facebook dataset SSSP: DBLP dataset

19. Scale to Large Data Sets On Amazon EC2 cluster Various-size graphs PageRankSSSP

20. Different Factors on Running Time Reduction iMapReduce Shuffling Sync. maps Init time Hadoop

21. Conclusion iMapReduce reduces running time by –Reducing the overhead of job/task initialization –Eliminating the shuffling of static data –Allowing asynchronous map execution Achieve a factor of 1.2x to 5x speedup Suitable for most iterative computations

22. Q & A Thank you!