Study On Task Scheduling Algorithm In Grid Computing

Grid computing is based on power grid. It organizes distributed computers as a"virtual super computer"by network. Every computer is called a node, and all the nodes form a"Grid". The target of grid is to make the users feel the use of grid is as convenient as using power grid. Grid computing provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities. It provides an ideal platform to solve large-scale computing problems in scientific and engineering area.Resources scheduling is a key issue in computational grid. In grid environments, from job submission to result processing, all events about jobs are under the control of job management. One good resources scheduling can effectively improve adjusting and assigning grid tasks, and decrease grid computing total time, Grid computing can perform perfectly. Resources scheduling is great important in grid computing. Because grid environments are large-scale, heterogeneous, dynamic, distributed and autonomous, grid job management is complex and challenging.Based on the principle of genetic algorithms, this paper presented the grid resources scheduling strategy adapted for grid computing environment, which was as as the core strategy of grid resource scheduling technical, to make the resource scheduling more reasonable. There were the main works of this paper:1. This paper researched into the current common resource discovery and management model and put forward a resource classification based grid resources hierarchical model, which will improve the speed of resource finding and updating greatly.2. The grid computing systems are consisted of various kinds of different resources, because these resources not only differ greatly in raw performance and also their load balancing is dynamic, their available computing performances to the users vary greatly, too. So this paper proposes three adaptive scheduling algorithms, such as self-adjusted genetic algorithm, linear transformation genetic algorithm, quantum genetic algorithm.3. This paper analyzed the excellent static and dynamic scheduling algorithms in the world, especially the classical Min-min algorithm and QoS guided Min-min algorithm. Considering the influence of different requirements of task on scheduling algorithm, the author put forward a proper algorithm to balance the charge and increase the throughput efficiently.Finally, the author used GridSim tools to make simulating experiment to test the algorithms presented before. The result of the simulation showed that the improved scheduling algorithm is more efficient.