عنوان مقاله [English]
نویسنده [English]چکیده [English]
Over the past two decades, PC speeds have increased from a few instructions per second to several million instructions per second. The tremendous speed of today's networks as well as the increasing need for high-performance systems has made researchers interested in parallel and distributed computing. The rapid growth of distributed systems has led to a variety of problems. The most important problem that has been addressed by many researchers is the task allocation in such environments in order to obtain effective system efficiency. The task allocation problem is, except in a few specific cases, an NP-complete problem; so, heuristic methods are used to achieve suboptimal solutions in the desired time. Although different methods have been used in research, finding an effective and efficient method for this problem is still needed and desirable. This study used a parallel genetic algorithm to find the optimal solution for allocating a graph of tasks to the processors in a distributed system. The results showed that the proposed algorithm can provide optimal or near-optimal allocations for problems of different sizes. Also, the proposed method was able to solve problems of large and medium-size in a much faster time than traditional genetic algorithm with super linear speedup.