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Enhancing Fault-Tolerance in Grid Computing through Adaptive Checkpointing and Replication Techniques

This paper explores novel algorithms that enhance fault tolerance in distributed grid environments through adaptive checkpointing and replication heuristics. Utilizing the Dynamic Scheduling in Distributed Environments (DSiDE) simulation framework, the study evaluates the performance of various strategies, including Last Failure Dependent Checkpointing and Load-Dependent Replication. Results demonstrate that adaptive techniques can significantly reduce overhead while maintaining high throughput and reliability. This research provides critical insights and future directions for effective fault-tolerant system design.

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Enhancing Fault-Tolerance in Grid Computing through Adaptive Checkpointing and Replication Techniques

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  1. Adaptive Task Checkpointing and Replication: Toward Efficient Fault-Tolerant Grids Maria Chtepen, Filip H.A. Claeys, Bart Dhoedt, Member, IEEE, Filip De Turck, Member, IEEE, Piet Demeester, Senior Member, IEEE, AND Peter A. Vanrolleghem

  2. Table of Content • Introduction • Adaptive Checkpointing Heuristics • Replication-Based Heuristics • Conclusion and Future Work

  3. Introduction • A novel fault-tolerant algorithm combine • Checkpointing • Replication • Be evaluated • Newly developed grid simulation environment Dynamic Scheduling in Distributed Environments(DSiDE)

  4. Introduction (cont.) • Simulation • Run employing workload • System parameters • From several large-scale parallel production systems’ logs • Using the discrete event grid simulator DSiDE

  5. Introduction (cont.) • Comparable throughput and fault tolerance • Static checkpointing with optimal parameters • Replication with optimal parameters

  6. Adaptive Checkpointing Heuristics • The Checkpointing Model • Limites • Runtime overhead (C) • Network latency (L) • Recovery delay (R) • Concentrates on the reduction of the checkpointing runtime overhead

  7. Adaptive Checkpointing Heuristics(cont.) • Problem Assuming the execution time can be exactly determined in advance • Simulation The upper bounds of the algorithms performance, with respect to this parameter

  8. Adaptive Checkpointing Heuristics (cont.) • Last Failure Dependent Checkpointing (LastFailureCP) • Goal • To reduce the overhead

  9. Adaptive Checkpointing Heuristics (cont.) • Mean Failure Dependent Checkpointing (MeanFailureCP) • Only considers checkpoint omissions • Modify the checkpointing interval based on the runtime information • The remaining job execution time • The average failure interval of the resource

  10. Adaptive Checkpointing Heuristics (cont.) • DSiDE Simulation Environment • Goal Validate • Architecture • DExec • DGen • Each DSiDE event has a time stamp • Provide a priori or at runtime • Support several types of dynamic system modifications

  11. Adaptive Checkpointing Heuristics (cont.) • The DSiDE simulator architecture

  12. Adaptive Checkpointing Heuristics (cont.) • The resource performed useful computations • Total grid availability • DSiDE provides a set of events to specify network links and routes

  13. Adaptive Checkpointing Heuristics (cont.) • Simulation Result • To compare the performance • Checkpointing heuristics • Realistic workload • System failure model

  14. Adaptive Checkpointing Heuristics (cont.) • Submit’s time • 80% (7 a.m. ~ 9 p.m.) • 20% (9 p.m. ~ 7 a.m.)

  15. Adaptive Checkpointing Heuristics (cont.) • Execution time • More than 80% of percent of all submitted jobs have medium execution times • 1 hour to 6 hours

  16. Adaptive Checkpointing Heuristics (cont.) • I decreases and longer jobs can get processed • Increase in job runtime is in effect • The results • The results achieved with PeriodicCP are partially improved by LastFailureCP due to omission of redundant checkpoints • The technique provides the best results for short checkpointing intervals • The effectiveness of LastFailureCP strongly depends on failure periodically

  17. Adaptive Checkpointing Heuristics (cont.) • Failures occur quite periodically • Can easily be predicted by the algorithm • LastFailureCP will perform similar to PeriodicCP • The fully dynamic scheme of MeanFailureCP proves to be the most effective • Selective increase in checkpointing keeps the number of processed jobs and the average execution time of MeanFailureCP more or less constant • PeriodicCP and LastFailureCP algorithms, the performance drops considerably

  18. Replication-based Heuristics • Load-Dependent Replication (LoadDependentRep) • Providing fault tolerance in distributed environments through replication • Idle resources can be utilized to run job copies without significantly delaying the execution of the original job

  19. Replication-based Heuristics (cont.) • The algorithm requires a number of parameters to be provided in advance • Minimum number of job copies (Repmin) • Maximum number of job copies (Repmax) • The CPU limit (CL)

  20. Replication-based Heuristics (cont.) • The outcome of the comparison determines the choice for the next job to be scheduled • CA >= CL (Less than Repmax) • 0 < CA < CL (Less than Repmin) • CA = 0 (Skip the current scheduling round) • When one of the job duplicates finishes, other replicas are automatically canceled

  21. Replication-based Heuristics (cont.) • Failure Detection and Load Dependent Replication (FailureDependentRep) • Increase the fault tolerance of the previously discussed LoadDependentRep heuristic • Offer a higher level of fault tolerance compared to solely replication-based strategies • Not ensure job execution

  22. Replication-based Heuristics (cont.) • Adaptive Checkpoint and Replication-Based Fault Tolerance (CombinedFT) • Dynamically switches between both techniques based on runtime information on system load • Checkpointing mode • Replication mode

  23. Replication-based Heuristics (cont.) • Checkpointing mode • CPU availability is low (CA < CL) • Combined FT rolls back • The earlier distributed active job replicas (ARj) • Starts job checkpointing • ARj > 0 • ARj = 0 & CA > 0 • ARj = 0 & CA = 0 & ∃i: ARi > 1 • ARj = 0 & CA = 0 & ¬∃i: ARi > 1

  24. Replication-based Heuristics (cont.) • Replication mode • Either the system load decreases • Enough resources restore from failure (CA≧CL) • All jobs with less than Repmax replicas are considered for submission to the available resources • Assign to the fastest resource connected to a grid site S with the maximum SpeedS • The smallest number of identical replicas

  25. Replication-based Heuristics (cont.) • Simulation Results • Approaches • Unconditional RL(1) • Unconditional RL(2) • Unconditional RL(3) • LoadDependentRL(1, 3, 40) • FailureDependentRL(1, 3, 40) • MeanFailureCP • CombinedFT

  26. Replication-based Heuristics (cont.)

  27. Replication-based Heuristics (cont.)

  28. Conclusion and Future Work • Fault tolerance forms an important problem • Job checkpointing • Replication • Evaluate in the DSiDE grid simulator • The runtime overhead characteristic to periodic checkpointing can be reduced

  29. Conclusion and Future Work (cont.) • Advantage • When the distributed system properties are not known in advance, both techniques can best be applied • Future Work • Scheduling methods will be considered

  30. Thank you for your attention

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