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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.11, No.3, Mar. 2019

An Automated Parameter Tuning Method for Ant Colony Optimization for Scheduling Jobs in Grid Environment

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Author(s)

Ankita, Sudip Kumar Sahana

Index Terms

ACO;bio-inspired;grid;parameter values;parameter tuning;scheduling

Abstract

The grid infrastructure has evolved as the integration and collaboration of multiple computer systems, networks, different databases and other network resources. The problem of scheduling in grid environment is an NP complete problem where conventional approaches like First Come First Serve (FCFS), Shortest Job First (SJF), Round Robin Scheduling algorithm (RR), Backfilling is not preferred because of the unexpectedly high computational cost and time in the worst case. Different algorithms, for example bio-inspired algorithms like Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm and Particle Swarm Optimization (PSO) are there which can be applied for solving NP complete problems. Among these algorithms, ACO is designed specifically to solve minimum cost problems and so it can be easily applied in grid environment to calculate the execution time of different jobs. Algorithms have different parameters and the performance of these algorithms extremely depends on the values of its parameters. In this paper, we have proposed a method to tune the parameters of ACO and discussed how parameter tuning affects the performance of ACO which in turn affects the performance of grid environment when applied for scheduling.

Cite This Paper

Ankita, Sudip Kumar Sahana, "An Automated Parameter Tuning Method for Ant Colony Optimization for Scheduling Jobs in Grid Environment", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.3, pp.11-21, 2019. DOI: 10.5815/ijisa.2019.03.02

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