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In this study, we have proposed a novel perspective for grid scheduling which aims at decreasing the makespan of the submitted jobs and increasing the utilization of resources involved. Grid scheduling is mapping jobs to grid resources at specific time intervals. Efficient scheduling is crucial to achieve excellent performance through grid computation. Meta-heuristics techniques are used, as grid scheduling is an NP-complete problem. Literature proposes genetic algorithm based heuristics and swarm based optimizations for grid scheduling. This study aims at using meta-heuristics techniques for the scheduling problem to reduce the Make span of task submitted to grid. Artificial Bee Colony (ABC) is selected for optimizing the scheduling due to its simplicity, flexibility and robustness. We have proposed Cluster Heterogeneous Min-Min Artificial Bee Colony (CHMM-ABC) and also a Hybrid ABC algorithm with reactive tabu search for efficient grid scheduling. Also the relationships between initial population and ABCs final outcome have been investigated in this study. Simulation confirms the efficiency of the suggested new approach. The proposed method reaches low makespan in the first run as initial swarm is created by the new CHEFT and Min-Min algorithm with RTS. Simulation reveals a make span decrease of 9.87 % to 13.32 % achieved by the new RTSABC compared to classic ABC.

Improved Grid Scheduling Using Hybrid Heuristic Algorithms with Enhanced Initial Solutions