Grid computing process improvement through computing resource scheduling using genetic algorithm and Tabu Search integration

Problems of scheduling jobs to some machine (Scheduling Jobs on Multiple Machines / SJMM) is one of the classical scheduling problems which can be found in the computing process, especially when done in a distributed computing. Several methods of solving problems has been developed both exact and heuristic approaches (metaheuristic). Tabu Search as one of a relatively new method of metaheuristic can be an alternative method to obtain the settlement approach to these problems. This method has been applied to combinatorial optimization problems, multi external optimization, and rare event simulation, with results that are optimal solution with a relatively short time. The purpose of this study to develop and implement a Tabu Search method combined with genetic algorithms (Integration Genetic-Tabu Search Algorithm / IGTS) in SJMM problems in computational grid. So that the integration of scheduling algorithms GA and TS can improve processing perpormance Job in grid computing environments. The method used is to include the excess Tabu Search algorithm which formed tabulist to be used in Genetic algorithms. Tabulist used to detect / store data in the process of forming a new population whose job is to detect repeated marriages between same Parent. Results obtained from the algorithm that is designed (IGTS) which serves to determine the allocation of the processing load on the cluster is the increased performance of some value which is quite satisfactory compared with not using tabulist include: makespan = 3.07%, the waiting time = 19.39%, and the number of generations / iterations is smaller.