Design And Realization Of Task Scheduling Algorithm In Grid Computing

Along with the rapid development of science and technology, many scientific computing and complex application system need more and more computation. But for nowadays single computer, even the super computer has no such capacity to handle this computing task. We still need the virtual network super computer consisted of many distributed computers, many kinds of corporate systems, even science instruments or equipments. This new approach about connecting geographically distributed, heterogeneous resources together by the high-speed network to solve the numerically and data intensive problem is named by grid computing.An important aspect in implementing grid computing is the implement of unified description method for the geographically distributed, heterogeneous resource, so that the grid system can give users the virtual unified resource interface and execute the task scheduling by the users on the fittest resource nod dynamically. This paper studies two important subjects: the first one is a grid computing simulating model; the second one is task scheduling algorithm.This paper first outlines the system structure of the grid computing system, the basic principles in constructing heterogeneous resources, the main design features and the applications of grid. This paper also introduces the research background of grid computing and surveys the current status of this upcoming technology.Task scheduling is the key to decide the efficiency of resource and the finishing time of tasks. This paper also gives detail about task scheduling modal and scheduling algorithm. In order to study scheduling algorithm well, we design a grid simulating model consisting of four parts.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 are dynamic, their available computing performances to the users vary greatly, too. So this paper proposes four adaptive scheduling algorithms, such as self-adjusted genetic algorithm, linear transformation genetic algorithm, parallel genetic simulated annealing algorithm, quantum genetic algorithm.Finally, we give some conclusions and outline a few directions for future research.