Genetic Algorithm for Independent Job Scheduling in Grid Computing

Abstract

Grid computing refers to the infrastructure which connects geographically distributed computers ownedby various organizations allowing their resources, such as computational power and storage capabilities, to beshared, selected, and aggregated. Job scheduling is the problem of mapping a set of jobs to a set of resources.It is considered one of the main steps to e ciently utilise the maximum capabilities of grid computing systems.The problem under question has been highlighted as an NP-complete problem and hence meta-heuristic methodsrepresent good candidates to address it. In this paper, a genetic algorithm with a new mutation procedure tosolve the problem of independent job scheduling in grid computing is presented. A known static benchmark forthe problem is used to evaluate the proposed method in terms of minimizing the makespan by carrying out anumber of experiments. The obtained results show that the proposed algorithm performs better than some knownalgorithms taken from the literature.

Hybrid meta-heuristic algorithms for independent job scheduling in grid computing

“…The two proposed hybrid methods,ACO-VNS and GA-VNS, were compared against selected algorithms from the literature. In particular, the following algorithms were selected for comparison: min-min algorithm [9], Genetic Algorithm (GA) [17], Simulated Annealing (SA) [17], Particle Swarm Optimisation (PSO) [17], Differential Evolution algorithm (DE) [18], Two-Phase Variable Neighbourhood Search (TPVNS) [16] and Genetic Algorithm (MGA) [34]. All the competing algorithms were implemented sequentially.…”

“…Several selection techniques are available in the literature. In this study, the N-Tournament method suggested in [10] [34] has been used with N=4. In tournament selection, several tournaments are run among a few individuals which have been selected randomly from the population.…”

“…Although this causes a premature stagnation of the population, the use of the Steady State Strategy produces a fast convergence (minimization) of the objective function [? ] [10] [34]. This characteristic serves our goals since we are interested in minimizing the makespan in a relatively short time.…”

“…The GA has been applied successfully for various complex optimization problems that share the same characteristics with the job scheduling problem in grid computing [34]. This…”

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

“…Authors in [24] proposed a novel genetic algorithm (Im-GA) based independent task scheduling technique to optimize four conflicting objectives, makespan, load balancing, resource utilization, and speed up ratio using a novel mutation technique. Authors in [25] also proposed a GA based scheduling algorithm for scheduling Bag-of-tasks in computational grids using modified mutation strategy to improve the makespan. The proposed GA obtained best makespan over many sequential task scheduling methods, Authors in [26] proposed six multiobjective genetic algorithms using single and multiple population schemes for solving the scheduling problem of batch of independent tasks in Computational Grid (CG).…”