Evaluation of Hybridization of GA and TS Algorithms for Independent Batch Scheduling in Computational Grids

Abstract

Computing efficiently a planning of incoming jobs to available machines in the Grid system is a main requirement for optimized system performance. One version of the problem is that of independent batch scheduling in which jobs are assumed independent and are scheduled in batches aiming to minimize the make span and flow time. Given the hardness of the problem, heuristics are used to find high quality solutions for practical purposes of designing efficient Grid schedulers. In this paper we present a study on the performance of two algorithms for the problem: Genetic Algorithms (GAs) and Tabu Search (TS), and two hybridizations of them, namely, the GA(TS) and GA-TS which differ in the way the main control and cooperation among GA and TS are implemented. The hierarchic and simultaneous optimization modes are considered for the bi-objective scheduling problem. The evaluation is done using different grid scenarios generated by a grid simulator. The computational results showed that the hybrid algorithms outperforms both the GA and TS for make span parameter but not for the flow time parameter.