Resource Selection in Grids Using Contract Net

1. Resource Selection in Grids Using Contract Net Kunal Goswami, Arobinda Gupta Cisco Systems, Bangalore, India Dept. of Computer Science & Engineering and School of IT, IIT Kharagpur, India Reporter：S.Y.Chen

2. Abstract • Different market mechanisms have been used to match resources with users in grids. In this paper, we propose two simple contract-net based resource selection policies in grids with heterogeneous resources. A detailed experimental evaluation of the policies shows that they perform better than other commonly used policies for many scenarios. S.Y. Chen

3. Outline • Introduction • System Model • Resource Selection Policies • Experimental Results. S.Y. Chen

4. Introduction • Choosing the right resource for a user job is an important problem in Grid. • We propose two simple contract net based resource selection policies. S.Y. Chen

5. Introduction (cont.) • The policies increase the number of jobs finishing within budget and deadline while reducing the averageturnaround time per job or the average budget spent per job at the same time. S.Y. Chen

6. Introduction (cont.) • Previous works on using contract net for resource selection in grids with heterogeneous resources either only attempted to increase the number of jobs finishing within deadlines, or attempted to reduce the cost of execution of a job. S.Y. Chen

7. System Model S.Y. Chen

8. System Model (cont.) S.Y. Chen

9. System Model (cont.) S.Y. Chen

10. System Model (cont.) S.Y. Chen

11. Resource Selection Policies • Some policies that have been used in prior works in resource selection in grids are random, time optimized and cost-optimized. • In a random policy, a user randomly chooses one resource that can complete the job within the deadline and budget allocated for the job. • It may increase both the execution time and the cost of execution of a job. S.Y. Chen

12. Resource Selection Policies (cont.) • In a time-optimized or cost-optimized policy, the fastest or the cheapest resource is selected respectively. • A pure time-optimized strategy or a pure cost-optimized strategy can cause higher speed or lower cost resources to become overloaded respectively, thereby reducing the success rate. S.Y. Chen

13. Resource Selection Policies (cont.) • K-Time-Optimized • K-Cost-Optimized S.Y. Chen

14. Experimental Results • Users：10 • Resources：10 • Jobs：100 • The resources have different speeds and cost per unit time of usage as shown below. S.Y. Chen

15. Experimental Results (cont.) • Evaluation of the K-Time-Optimized Policy • Success rate for different job lengths S.Y. Chen

16. Experimental Results (cont.) • Success rate for the three policies • K = 4 • Job length is 150,000 • Arrival rate 0.001 ~ 0.03 S.Y. Chen

17. Experimental Results (cont.) • Effect of Arrival Rate • K = 4 • Job length is 150,000 • Arrival rate 0.002 ~ 0.06 S.Y. Chen

18. Experimental Results (cont.) • Effect of job length • K = 4 • Arrival rate = 0.01 S.Y. Chen

19. Experimental Results (cont.) • Evaluation of the K-Cost-Optimized Policy S.Y. Chen

20. Experimental Results (cont.) • The success rate for the three policies. S.Y. Chen

21. Experimental Results (cont.) • The average budget spent for the three policies. S.Y. Chen

22. Experimental Results (cont.) • Effect of Arrival Rate • K = 4 • Job length is 100,000 • Arrival rate 0.002 ~ 0.06 S.Y. Chen

23. Experimental Results (cont.) • Effect of Job Length • K = 4 • Arrival rate is 0.01 S.Y. Chen