Resource Selection in Grids Using Contract Net

Kunal Goswami, Arobinda Gupta

Cisco Systems, Bangalore, IndiaDept. of Computer Science & Engineering and School of IT, IIT K

haragpur, India

Reporter： S.Y.Chen

2008-09-26 S.Y. Chen 2

AbstractDifferent 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.

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OutlineIntroductionSystem ModelResource Selection PoliciesExperimental Results.

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IntroductionChoosing the right resource for a user job is an important problem in Grid.

We propose two simple contract net based resource selection policies.

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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.

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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.

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System Model

InitiatorP3

P2

P1

P4

CFP

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System Model (cont.)

InitiatorP3

P2

P1

P4

Propose 4.5/unit

Propose 5.5/unit

Refuse

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System Model (cont.)

InitiatorP3

P2

P1

P4

AcceptProposal

Reject Proposal

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System Model (cont.)

InitiatorP3

P2

P1

P4

InformDone

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Resource Selection PoliciesSome 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.

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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.

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Resource Selection Policies (cont.)K-Time-Optimized

K-Cost-Optimized

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Experimental ResultsUsers ： 10 Resources ： 10 Jobs ： 100The resources have different speeds and cost per unit time of usage as shown below.

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Experimental Results (cont.)Evaluation of the K-Time-Optimized Policy

Success rate for different job lengths

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Experimental Results (cont.)Success rate for the three policies

K = 4Job length is 150,000Arrival rate 0.001 ~ 0.03

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Experimental Results (cont.)Effect of Arrival Rate

K = 4Job length is 150,000Arrival rate 0.002 ~ 0.06

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Experimental Results (cont.)Effect of job length

K = 4Arrival rate = 0.01

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

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

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

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Experimental Results (cont.)Effect of Arrival Rate

K = 4Job length is 100,000Arrival rate 0.002 ~ 0.06

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Experimental Results (cont.)Effect of Job Length

K = 4Arrival rate is 0.01