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

This research explores a Pareto Frontier approach to optimize data transfer and job execution in grid computing. Addressing the complexities of Data-Aware Job Scheduling, we utilize a Genetic Algorithm (GA) named GA-ParFnt to identify the Pareto Front for execution time and data transfer time simultaneously. Our simulations on various test grids reveal that traditional scheduling algorithms may not be optimal. The findings suggest that the Pareto Front can be effectively represented by exponential functions, offering insights into more efficient scheduling strategies.

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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 • GA-ParFnt • Pareto Frontier • Genetic Algorithm (GA) • 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 for Finding DAJS’ Pareto Front (GA-ParFnt)

  8. Simulation • Test-Grid-4-8

  9. Discussion and Analysis • The shape of Pareto Front Test-Grid-8-4

  10. Discussion and Analysis • Scheduling Algorithms

  11. Conclusion • GA-ParFnt was effective in finding the Pareto Front of executing jobs vs Transfer time of Datafiles in Grids • Such Pareto Front could be estimated by exponential funcitons • Many scheduling algorithms are not optimal, despite their claim.

  12. THANK YOU Questions?

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