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

S.Y. Chen2 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. Chen3 Outline Introduction System Model Resource Selection Policies Experimental Results.

S.Y. Chen4 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. Chen5 Introduction (cont.) The policies increase the number of jobs finishing within budget and deadline while reducing the average turnaround time per job or the average budget spent per job at the same time.

S.Y. Chen6 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. Chen7 System Model

S.Y. Chen8 System Model (cont.)

S.Y. Chen9 System Model (cont.)

S.Y. Chen10 System Model (cont.)

S.Y. Chen11 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. Chen12 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. Chen13 Resource Selection Policies (cont.) K-Time-Optimized K-Cost-Optimized

S.Y. Chen14 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. Chen15 Experimental Results (cont.) Evaluation of the K-Time-Optimized Policy Success rate for different job lengths

S.Y. Chen16 Experimental Results (cont.) Success rate for the three policies K = 4 Job length is 150,000 Arrival rate ~ 0.03

S.Y. Chen17 Experimental Results (cont.) Effect of Arrival Rate K = 4 Job length is 150,000 Arrival rate ~ 0.06

S.Y. Chen18 Experimental Results (cont.) Effect of job length K = 4 Arrival rate = 0.01

S.Y. Chen19 Experimental Results (cont.) Evaluation of the K-Cost-Optimized Policy

S.Y. Chen20 Experimental Results (cont.) The success rate for the three policies.

S.Y. Chen21 Experimental Results (cont.) The average budget spent for the three policies.

S.Y. Chen22 Experimental Results (cont.) Effect of Arrival Rate K = 4 Job length is 100,000 Arrival rate ~ 0.06

S.Y. Chen23 Experimental Results (cont.) Effect of Job Length K = 4 Arrival rate is 0.01