Research Specifications

Load balancing is an important part of grid scheduling systems which aims to utilize dynamic and decentralized grid resources without any overcapacity happening at any resources. Load balancing is being considered as an important phase for distributed computing environments. In large scale distributed systems such as grid/cloud computing, it is necessary to use a distributed load balancing system in job scheduling system. Distributed load balancing was identified as a major concern to allow grid computing to scale up the job scheduling. Many algorithms had been proposed for finding the solution of load balancing problem in these fields. But very few algorithms are proposed for distributed load balancing in grid computing environments. In this paper, we have developed an adaptable artificial bee colony (ABC) algorithm for dynamic dividing and scheduling jobs via decentralized schedulers. The proposed method significantly enhances the load balance on the resources. Besides, this method does not need primary information about the capacity and power of the resources which is compatible with the variable and heterogeneous nature of decentralized scheduling. The proposed method is compared with the cuckoo based scheduling algorithm and OSL decentralized scheduling method. The results of the simulations revealed that the proposed method is significantly better than the OSL method. It also outperforms the cuckoo based scheduling algorithm in most of simulations.