Maximizing Reliability with Task Scheduling In a Computational Grid Using GA

Abstract

Grid is a service aggregation of both the information and the computational resources. Scheduling becomes a challenging job in such a complex and dynamic environment as both the application and the computational resources are heterogeneous. The problem is further complicated by the fact that these resources may fail at any point of time. Thus a scheduling strategy which schedules the job based on the failure possibility of the grid constituents becomes very important for the reliable execution of the job. Genetic algorithm has evolved as an effective search tool to solve the optimization problems consisting of large search space. It is a type of evolutionary search strategies applied on the search space and is based on the principle of "survival of the fittest". The model addresses the important issue of scheduling to provide the most reliable environment for the job execution by scheduling the job, ensuring maximum reliability to the job execution using Genetic Algorithm. The model presents a realistic picture of the grid by scheduling the job based on the reliability of the computational resources, networking resources and application (job) along with the pre-assigned workload on the nodes in order to provide the most reliable environment to the job. Simulation study proves the effectiveness of the model by comparing the performance of the model with another similar model.