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

2. Related Works

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).