Static Task Allocation in Distributed Systems Using Parallel Genetic Algorithm

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. Task allocation is a key process for distributed systems to achieve effective system efficiency, which, except for a few cases, is an NP-complete problem. Finding an effective and efficient method for this problem is still sought despite various methods used in studies. Experiments and the results of previous research have shown that NP problems are better solved by exploratory methods than other methods. This study used a parallel genetic algorithm (PGA) to find a solution for proper task allocation to processors in a distributed system. The task allocation policy, obtained using PGA, is much faster than traditional genetic algorithms. The results showed that the proposed algorithm can provide optimal or near-optimal allocations for problems of different sizes. The proposed method also solved large- and medium-sized problems much faster than traditional genetic algorithms and with super linear speedup.