Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

Abstract

The grid environment is a dynamic, heterogeneous, and changeable computing system that distributes various services amongst different clients. To attain the benefits of collaborative resource sharing in Grid computing, a novel and proficient grid resource management system (RMS) is essential. Therefore, detection of an appropriate resource for the presented task is a difficult task. Several scientists have presented algorithms for mapping tasks to the resource. Few of them focus on fault tolerance, user fulfillment, and load balancing. With this motivation, this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm (DHOA), called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid platform. The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer. It also derives an objective function with candidate solution (schedule) as input and the outcome is the makespan value denoting the quality of the candidate solution. The simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.