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Smart Redundancy for Distributed Computation

Smart Redundancy for Distributed Computation. Yuriy Brun University of Washington. George Edwards Blue Cell Software, LLC. Jae young Bang University of Southern California. Nenad Medvidovic University of Southern California. Distributed Computation Architectures.

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Smart Redundancy for Distributed Computation

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  1. Smart Redundancy forDistributed Computation Yuriy Brun University of Washington George Edwards Blue Cell Software, LLC Jae young Bang University of Southern California Nenad Medvidovic University of Southern California

  2. Distributed Computation Architectures • Solve large computational problems and/or process large data sets • Provide a platform and API for applications • Transparently parallelize computation across a pool of computers • Examples: • Clouds • Grids • Volunteer computing

  3. DCA Applications • Highly parallelizable problems • Find the 10100th digit of π • Factor 22011 – 1 • Driven by: • Basic research • Pharmaceutical applications • Web analytics • …

  4. Volunteer Computing • Attempts to leverage the more than 1 billion (mostly idle) machines on the Internet • Volunteers install a client • When idle, the client requests work from a server and send back results • Aids projects that have limited funding but large public appeal

  5. Dealing with Faults • Context: • Volunteers fail and maliciously return false results • Volunteers are not accountable • Malicious volunteers may collude • Well-formed but incorrect results are hard to detect • The reliability of volunteers is difficult to estimate • Solution: • Redundancy and voting

  6. System Model • A task server subdivides computations into tasks • The task server replicates each task into multiple identical jobs • The task server assigns each job to a node in the node pool • Nodes perform work, send results, and rejoin the pool • New volunteer nodes may join the pool while other nodes may leave

  7. k-vote Traditional Redundancy (TR) • Performs k independent executions of each task • Takes a vote on the correctness of the result • Requires expending a factor of k resources or suffering a factor of k slowdown in performance • Example • k = 19 • r = 0.7

  8. Insights • Redundant computations need not be simultaneous • DCAs can dynamically adjust the level of redundancy based onrun-time information • k-vote traditional redundancy wastes computations • Example • 19 independent computations (k = 19) • 70% node reliability (r = 0.7) • (0.7)10 ≈ 2.8% of the time, the first 10 of them will return the correct result • The last 9 results are irrelevant

  9. k-vote Progressive Redundancy (PR) • Distributes jobs in waves • In each wave, distributes the minimum jobs needed to produce a consensus (assuming all agree) • Repeats until a consensus is reached • Example • k = 19 • r = 0.7

  10. Insights • The confidence level associated with a result can be computed • k-vote progressive redundancy produces results with varying confidence • Example • k = 19, r = 0.7 • If the vote is 10-0, confidence level ≈ 99.98% • If the vote is 10-9, confidence level = 70%

  11. Iterative Redundancy (IR) • Distributes jobs in waves • In each wave, distributes the minimum jobs required to achieve a desired confidence level • Repeats until desired confidence level is reached • Example • d = 4 • r = 0.7

  12. Algorithm Comparison • System reliability approaches 1 exponentially for TR, PR, and IR • IR produces the same reliabilityat a lower cost • Or, equivalently, higher reliability at the same cost • IR is optimal with respect to cost • Guaranteed to use the minimum computation needed to achieve desired system reliability System Reliability Cost Factor System Reliability Cost Factor

  13. Algorithm Comparison • PR and IR perform best when the reliability of the node pool is high Ratio Improvement OverTraditional Recovery Node Reliability

  14. Adaptive Behavior • IR maintains a constant system reliability as node reliability fluctuates • Injects redundancy where it is needed • “Unlucky” situations • Removes redundancy where it is unnecessary NodeReliability Time Cost Factor Time SystemReliability Time

  15. Node Reliability Estimation • Incorrectly estimating node reliability does not affect the performance of IR System Reliability Cost Factor

  16. Conclusions • Iterative redundancy automatically replicates computation with optimal efficiency • Iterative redundancy can be used when: • A computation can be broken down into independent tasks • Computation is performed by a pool of independent processing resources • Task deployment decisions can be made at runtime • The reliability of resources in the pool is unknown

  17. For More Information • To appear in ICDCS 2011: • Smart Redundancy for Distributed Computationby Yuriy Brun, George Edwards, Jae young Bang and Nenad Medvidovic • http://www.cs.washington.edu/homes/brun/pubs/pubs/Brun11icdcs.pdf

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