Grid Computing Environment, Research And Implementation, Based On Adaptive Genetic Simulated Annealing Algorithm Sgsa Task Scheduling

Grid is an integrated computing and resources environment, It is able to absorb variety of computing resources, and convert them into a widely, available, reliable, standard economic computing power. Grid computing is extensively applied on large scientific computation and research projects, become the traditional Internet and the third Web Internet wave in the world, Grid computing has been applied in many countries and region, and played a significant role in scientific research, information sharing, application conformity and so on.Grid task scheduling problem is the research and application of a key issue that must be addressed. Scheduling goal is to submit the task the user optimal scheduling, and try to improve the overall throughput of the grid system. task scheduling of Grid computing is an NP complete problem.In recent years, people made a lot of inspiring intelligent algorithms such as neural networks, simulated annealing, tabu search, genetic algorithm, ant algorithm, which are firmly established as sharp tools on NP problem and task scheduling .Self-Adaptive genetic simulated annealing algorithm is a method optimization and hybrid the genetic algorithm and simulated annealing algorithm , it is the genetic algorithm has a strong grasp of the overall capacity, but also the algorithm using simulated annealing local search ability of stronger populations of genetic algorithm the probability of acceptance, to avoid the mature problem of genetic algorithm.The main of this paper is reflected in the traditional genetic algorithm and simulated annealing algorithm described in the analysis of the traditional genetic algorithm and simulated annealing algorithm, after analyze the advantages and disadvantages of the original algorithm optimization; based on open of genetic algorithms and simulated annealing, organic mix of the two algorithms, and proposed self-adaptive genetic simulated annealing algorithm. After mixing algorithm has strong global search ability, but also has some local search capabilities, to a certain extent, prevent the algorithm into a local optimal solution, the improved algorithm also has certain self-adaptive ability, according to population evolution adjust the circumstances to achieve the objective of improving the overall efficiency of the algorithm. Finally, self-adaptive genetic simulated annealing algorithm is applied to task scheduling, task scheduling problem for the design of the specific circumstances of the corresponding algorithm, and realized.