International Journal of Computer Science Issues

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Multiprocessors have become powerful computing means for running real-time applications and their high performance depends greatly on parallel and distributed network environment system. Consequently, several methods have been developed to optimally tackle the multiprocessor task scheduling problem which is called NP-hard problem. To address this issue, this paper presents two approaches, Modified List Scheduling Heuristic (MLSH) and hybrid approach composed of Genetic Algorithm (GA) and MLSH for task scheduling in multiprocessor system. Furthermore, this paper proposes three different representations for the chromosomes of genetic algorithm: task list (TL), processor list (PL) and combination of both (TLPLC). Intensive simulation experiments have been conducted on different random and real-world application graphs such as Gauss-Jordan, LU decomposition, Gaussian elimination and Laplace equation solver problems. Comparisons have been done with the most related algorithms like: list scheduling heuristics algorithm LSHs, Bipartite GA (BGA) [1] and Priority based Multi-Chromosome (PMC) [2]. The achieved results show that the proposed approaches significantly surpass the other approaches in terms of task execution time (makespan) and processor efficiency.