摘要
Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique for minimizing the parallel application time cost of task assignment based on the honeybee mating optimization （HBMO） algorithm. The HBMO approach combines the power of simulated annealing, genetic algorithms, and an effective local search heuristic to find the best possible solution to the problem within an acceptable amount of computation time. The performance of the proposed HBMO algorithm is shown by comparing it with three existing task assignment techniques on a large number of randomly generated problem instances. Experimental results indicate that the proposed HBMO algorithm outperforms the competing algorithms.
Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique for minimizing the parallel application time cost of task assignment based on the honeybee mating optimization （HBMO） algorithm. The HBMO approach combines the power of simulated annealing, genetic algorithms, and an effective local search heuristic to find the best possible solution to the problem within an acceptable amount of computation time. The performance of the proposed HBMO algorithm is shown by comparing it with three existing task assignment techniques on a large number of randomly generated problem instances. Experimental results indicate that the proposed HBMO algorithm outperforms the competing algorithms.
