Tuning struggle strategy in genetic algorithms for scheduling in computational grids

Title:

Tuning struggle strategy in genetic algorithms for scheduling in computational grids

Creator:

Xhafa, Fatos, Duran , Bernat , Abraham , Ajith, and Dahal, Keshav

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:34b67ab9-42a2-456e-9b5f-0548d5972f4c
uuid:34b67ab9-42a2-456e-9b5f-0548d5972f4c

Subject:

Genetic algorithms, scheduling, grid computing, struggle strategy, similarity measure, and tuning

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Job Scheduling in Computational Grids is gaining importance due to the need for efficient large-scale Grid-enabled applications. Among different optimization techniques designed for the problem, Genetic Algorithm (GA) is a popular class of solution methods. As GAs are high level algorithms, specific algorithms can be designed by choosing the genetic operators as well as the evolutionary strategies such as Steady State GAs and Struggle GAs. In this paper we focus on Struggle GAs and their tuning for scheduling of independent jobs in computational grids. Our results showed that a careful hash implementation for computing the similarity of solutions was able to alleviate the computational burden of Struggle GA and perform better than standard similarity measures. This is particularly interesting for the scheduling problem in Grid systems, which due to changeability over time, has demanding time restrictions on the computation of planning the jobs to resources.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2008 Volume:18 | Number:3

Harvested from:

CDK

Metadata only:

false