Static Batch Mode Heuristic Algorithm for Mapping Independent Tasks in Computational Grid

Grid computing plays an important role in solving large-scale computational problems in a high performance computing environment. Scheduling of tasks to efficient and best suitable resource is one of the most challenging phase in grid computing systems. Grid environment reveals several challenges in efficient scheduling of complex applications because of its heterogeneity, dynamic behavior and shared resources. Scheduling of independent tasks in grid computing is dealt by a number of heuristic algorithms. This study proposes a new heuristic algorithm for mapping independent tasks in a grid environment to be assigned optimally among the available machines in a grid computing system. Due to the multi-objective nature of the grid scheduling problem, several performance measures and optimization criteria can be assumed to determine the quality of a given schedule. The metrics used here include makespan and resource utilization. This algorithm provides effective resource utilization by reducing machine idle time and minimizes makespan. This algorithm also balances load among the grid resources and produce high resource utilization with low computational complexity. The proposed algorithm is compared with other popular heuristics for performance measures.