Title: Job scheduling in computational grid based on an improved cuckoo search method

Authors: Tarun Kumar Ghosh; Sanjoy Das; Subhabrata Barman; Rajmohan Goswami

Addresses: Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia, India ' Department of Engineering and Technological Studies, Kalyani University, Kalyani, India ' Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia, India ' Department of Computer Applications, Pailan College of Management and Technology, Kolkata, India

Abstract: Grid computing is a high performance distributed computing platform to solve complex and large-scale scientific problems. It consists of heterogeneous computing resources connected by a network across dynamic and geographically distributed organisations to create a distributed high performance computing infrastructure. Job scheduling in computational Grid is known as NP-complete problem owing to the problem complexity and intractable nature of the problem. Such a problem could be solved using heuristic algorithms. These types of algorithms have the ability to find a near optimal solution in reasonable time rather than the optimal solution in a very long processing time. The primary objective of the scheduling is to minimise the makespan of the system. In this paper, an Improved Cuckoo Search (ICS) optimisation method has been proposed for scheduling user-jobs to available resources so that various performance metrics are optimised. Here, our attention has been focused on the improvement of computational Grid performance in terms of makespan and completion time. The study reveals that the proposed ICS algorithm provides better results in comparison with Cuckoo Search (CS) and Simulated Annealing (SA).

Keywords: grid computing; job scheduling; makespan; cuckoo search; completion time; metaheuristics.

DOI: 10.1504/IJCAT.2017.082864

International Journal of Computer Applications in Technology, 2017 Vol.55 No.2, pp.138 - 146

Received: 17 Feb 2016
Accepted: 05 Apr 2016

Published online: 01 Mar 2017 *

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