A discrete firefly algorithm for scheduling jobs on computational grid

Abstract

Computational grid emerged as a large scale distributed system to offer dynamic coordinated resources sharing and high performance computing. Due to the heterogeneity of grid resources scheduling jobs on computational grids is identified as NP-hard problem. This chapter introduces a job scheduling mechanism based on Discrete Firefly Algorithm (DFA) to map the grid jobs to available resources in order to finish the submitted jobs within a minimum makespan time. The proposed scheduling mechanism uses population based candidate solutions rather than single path solution as in traditional scheduling mechanism such as tabu search and hill climbing, which help avoids trapping in local optimum. We used simulation and real workload traces to evaluate the proposed scheduling mechanism. The simulation results of the proposed DFA scheduling mechanism are compared with Genetic Algorithm and Tabu Search scheduling mechanisms. The obtained results demonstrated that, the proposed DFA can avoid trapping in local optimal solutions and it could be efficiently utilized for scheduling jobs on computational grids. Furthermore, the results have shown that DFA outperforms the other scheduling mechanisms in the case of typical and heavy loads. © 2014 Springer International Publishing Switzerland.