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Providing Resiliency to Load Variations in Distributed Stream Processing

This study explores techniques to provide resiliency to load variations in distributed stream processing networks, optimizing operator distribution and network bandwidth usage. It includes algorithms, experiments, and comparisons with existing methods.

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Providing Resiliency to Load Variations in Distributed Stream Processing

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  1. Providing Resiliency to Load Variationsin Distributed Stream Processing • Ying Xing, Jeong-Hyon Hwang, Ugur Cetintemel, Stan Zdonik • Brown University

  2. Financial Data Streams Surveillance Network Monitoring Click Stream Analysis Traffic Monitoring Sensor Network Stream Processing Monitoring Apps

  3. Distributed Stream Processing

  4. Roadmap • Problem Statement • Linear Load Model • Feasible Set • The Algorithm • Extensions • Lower Bound of Input Rates • Non-linear Load Model • Network Bandwidth / Communication Overhead • Experimental Results • Related Work • Conclusions

  5. r1 r1 r1 Feasible Set r2 r2 Problem Statement Operator Distribution Input Rate Space • Goal • Find an operator distribution with the largest feasible set size r2 feasible infeasible r1

  6. Linear Load Model • rj - input rate of input j (tuples/sec) • ck - processing cost of operator ok (CPU cycles/tuple) • l(ok) - the processingload of operator ok (CPU cycles/sec) • sk - selectivity of operator ok ( [# output tuples] / [# of input tuples] ) o1 o2 o3 o4

  7. o1 o2 o1 o2 o3 o4 o4 o3 r2 r2 0 0 r1 r1 Example Feasible Sets o1 o2 o3 o4 r2 0 r1

  8. r2 0 r1 “Ideal” Feasible Set • Theorem 1. Feasible Set is maximized when load coefficients of each input are perfectly balanced over all nodes (relative to their capacities) r2 o1 o2 o3 o4 0 r1

  9. Resilient Operator Distribution Algorithm • Compute the Ideal Feasible Set • Sort Operators based on Load Coefficients • For each operator, determine the destination server r2 r1 0 Ideal Feasible Set

  10. Result: R.O.D. vs Load Balancing 10 nodes 5 input streams

  11. Result: Latency of a Network Monitoring Query

  12. Extension:Network Bandwidth & Comm. Overhead • Network Bandwidth • Comm. Overhead

  13. r1 o1 ou … r2 om ou+1 … Extension: Nonlinear Load Model • Add an artificial variable r1 r2 om o1 ou ou+1 … …

  14. r2 r2 0 0 r1 r1 Extension: Lower Bound of Input Rates • Use the lower bound instead of the origin

  15. Related Work • Traditional Distributed Systems • Load balancing and load sharing [Shivaratri92] [Diekmann97] • Parallel query processing [DeWitt92] • Graph partitioning [Walshaw97] [Schloegel00] • Stream Processing Systems • Load management • Flux [Shah03] – data partitioning based parallel continuous query processing • Medusa [Balazinska04] – federated distributed stream processing

  16. Conclusion • Distributed Stream Processing • Resilient Operator Distribution • Maximize feasible set size • Performance • Much better than conventional load distribution algorithms

  17. Backup Slides

  18. Computation Complexity • Computation time is determined by • n –number of nodes • m–number of operators • d–number of system input streams • k– number of samples in load time series • Static operator distribution • Dynamic operator distribution

  19. Heuristics • Heuristic #1 • Choose the case where feasibility boundaries are close on each axis • Heuristic #2 • Choose the case where all the feasibility boundaries are far from the orgin. r2 r2 0 0 r1 r1 r2 r2 0 0 r1 r1

  20. Resilient vs. Optimal 2 nodes 4 input streams

  21. Varying Bandwidth Constraints • Resilient vs. Connected-Load-Balancing

  22. Varying Data Communication CPU Overhead • Resilient vs. Connected-Load-Balancing

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