Bi-objective task assignment in heterogeneous distributed systems using honeybee mating optimization

Abstract

Effective task assignment is critical for achieving high performance in heterogeneous distributed computing systems. However, there is a possibility of processor and network failures and this can have an adverse impact on applications running on such systems. This paper proposes a new technique based on the honeybee mating optimization (HBMO) algorithm for static task assignment in the systems, which takes into account both minimizing the total execution and communication times and maximizing the system reliability simultaneously. The HBMO based approach combines the powers of simulated annealing, genetic algorithms, and an effective local search heuristic to search for the best possible solution to the problem under investigation within a reasonable computing time. We study the performance of the algorithm over a wide range of parameters such as the number of tasks, the number of processors, the ratio of average communication time to average computation time, and task interaction density of applications. The effectiveness and efficiency of our algorithm are manifested by comparing it with recently proposed algorithms from the literature.