A Review of Resource Allocation and Task scheduling for Computational Grids based on Meta-heuristic Function

The current scenario of grid computing faced a problem of job failure and increase of execution time of jobs. The failure of job degraded the performance of grid computing. The failure and increase execution time depend on resource allocation and task scheduling of job in grid computing. The proper sharing of resource of grid computing are related to distributes the system workload based on the processing elements capacity which leads to Minimize the overall job mean response time and maximize the system utilization and throughput at the working mode. The reduction of failure and task scheduling of resource need an optimization of process of allocation and execution of task. Various authors proposed a different optimization technique for process scheduling in grid computing such as ant colony optimization, genetic algorithm and fuzzy logic. These entire optimization factors reduce the rate of failure and execution time of grid. In this paper we discuss various method of grid computing related to resource allocation and task scheduling based on meta-heuristic function.