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

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

3. Grid Computing 3

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) 4 Storage Nodes Storage Nodes Computation Nodes Computation Nodes Job 1 Job 2 Job 3 Job N... File 1 File 2 File 3... File M

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

6. Preliminaries ›Pareto Front ›Genetic Algorithm 6

7. GA for Finding DAJS’ Pareto Front (GA-ParFnt) 7

8. Simulation ›Test-Grid-4-8 8

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

10. Discussion and Analysis ›Scheduling Algorithms 10

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. 11

12. THANK YOU Questions? 12