A Comparative Study on Seven Static Mapping Heuristics for Grid Scheduling Problem

Grid computing is a promising technology for future computing platforms and is expected to provide easier access to remote computational resources that are usually locally limited. Scheduling is one of the core steps to efficiently exploit the capabilities of grid computing (GC) systems. The problem of optimally mapping (defined as matching and scheduling) tasks onto the machines of a grid computing environment has been shown, in general, to be NPcomplete, requiring the development of heuristic techniques. The efficient scheduling of independent tasks in a heterogeneous computing environment is an important problem in domains such as grid computing. Different criteria can be used for evaluating the efficiency of scheduling algorithms, the most important of which are makespan, resource utilization and matching proximity. In this paper we will compare 7 popular heuristics for statically mapping independent tasks onto grid computing systems.