A New Heuristic Approach:Min-Mean Algorithm For Scheduling Meta-Tasks On Heterogenous Computing Systems

摘要

Grid computing enables the collection of abundance heterogeneous resources which is geographically distributed is selected and shared for solving a large scale problem - usually to a scientific or technical problem that needs a great number of computer processing cycles or access to large amounts of data. The basic idea of grid computing is to make use of the idle CPU cycles and millions of computer systems distributed across a worldwide network. Job scheduling is a vital and challenging work in heterogeneous computing environment. The problem of mapping meta-tasks to a machine is shown to be NP-complete. The NP-complete problem can be solved only using heuristic approach. In this paper, a new heuristic technique Min-mean algorithm for scheduling meta-tasks in grid computing is presented. The proposed algorithm improves the performance in both makespan and effective utilization of resources by reducing the idle time of the machine. The performance analyses show that the proposed algorithm has a better resource utilization rate, reduced makespan and the reduced idle time of the machine than the other known algorithms.