A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid

A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid

Tarun Kumar Ghosh (Haldia Institute of Technology, Haldia, India) and Sanjoy Das (Kalyani University, Kalyani, India)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/IJDST.2018040101

Abstract

Scheduling jobs in computational Grids is considered as NP-complete problem owing to the heterogeneity of shared resources. The resources belong to many distributed administrative domains that enforce various management policies. Therefore, the use of meta-heuristics are more appropriate option in obtaining optimal results. In this article, a novel hybrid population-based global optimization algorithm, called the Hybrid Firefly Algorithm and the Differential Evolution (HFA-DE), is proposed by combining the merits of both the Firefly Algorithm and Differential Evolution. The Firefly Algorithm and the Differential Evolution are executed in parallel to support information sharing amongst the population and thus enhance searching efficiency. The proposed HFA-DE algorithm reduces the schedule makespan, processing cost, and improves resource utilization. The HFA-DE is compared with the standard Firefly Algorithm, the Differential Evolution and the Particle Swarm Optimization algorithms on all these parameters. The comparison results exhibit that the proposed algorithm outperforms the other three algorithms.
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The job scheduling problem in Grid computing is a NP-complete problem (Ma et al., 2011). Task of the scheduler is to manage the jobs and resources. The scheduler performs two main functions: first scheduler selects the appropriate computational resource for the job and then assigns the resource to the jobs (Sharma & Mittal, 2013). New techniques, particularly those based in meta-heuristic algorithms, have been proposed to solve the Grid scheduling problem. These sorts of techniques make realistic assumptions based on a priori knowledge of the concerning environment and of the system load characteristics. The most frequently used meta-heuristic algorithms are Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Differential Evolution (DE), Ant Colony Optimization (ACO) and Cuckoo Search Algorithm (CSA).

The GA is a meta-heuristic algorithm that imitates the principle of genetic process in living organisms. The GA is a very popular algorithm to solve various types of combinatorial optimization problems. Job scheduling in computational Grid using GA has been addressed by Buyya et al. (2000); Braun et al. (2001); Zomaya and Teh (2001); Martino and Mililotti (2004); Page and Naughton (2005); Gao et al. (2005); Xhafa et al. (2008) and Aggarwal and Kent (2005). Prakash and Vidyarthi (2015) have proposed a new mechanism to maximize the availability of resources for job scheduling in computational Grid using GA. Enhanced Genetic-based scheduling for Grid computing is proposed in (Kolodziej & Xhafa, 2011).

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