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A Pareto Frontier for Optimizing Data Transfer vs. Job Execution in Grids

A Pareto Frontier for Optimizing Data Transfer vs. Job Execution in Grids. Javid Taheri | Postdoctoral Research Fellow. Albert Y. Zomaya | Professor and Director. Centre for Distributed and High Performance Computing School of Information Technologies

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A Pareto Frontier for Optimizing Data Transfer vs. Job Execution in Grids

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  1. A Pareto Frontier for Optimizing Data Transfer vs. Job Execution in Grids Javid Taheri | Postdoctoral Research Fellow Albert Y. Zomaya| Professor and Director Centre for Distributed and High Performance Computing School of Information Technologies The University of Sydney, Sydney, Australia

  2. Introduction to Grid Computing • Problem Statement: Data-Aware Job Scheduling • Preliminaries • Pareto Frontier • Genetic Algorithm (GA) • GA-ParFnt • Simulation and Analysis of Results • Conclusion

  3. Grid Computing

  4. Problem Statement • Data Aware Job Scheduling (DAJS) • (1) the overall execution time of a batch of jobs (NP-Complete) • (2) transfer time of all datafiles to their dependent jobs(NP-Complete) Computation Nodes Storage Nodes File 1 Job 1 File 2 Job 2 File 3 Job 3 ... ... Job N File M

  5. Problem Statement (cont.) SN CN Scheduler SN CN SN CN

  6. Preliminaries • Pareto Front • Genetic Algorithm

  7. GA-ParFnt

  8. Discussion and Analysis • GA-ParFnt’s Behavior and Performance • Scheduling Algorithms • Allocating jobs is much harder than replicating datafiles Test-Grid-8-4 Test-Grid-8-16 Test-Grid-8-8

  9. THANK YOU Questions?

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