Grid Resource Selection Optimization With Guarantee Quality of Service by Hybrid of Genetic and Simulated Annealing Algorithms
When there are a lot of requests for resources in a grid system, it is essential to make a good planning and resources allocation to provide a suitable QOS. As different programs need different amounts of services based on their priorities, there are various methods to provide these requests by choosing suitable allocation to optimize total work of system. In this study, some parameters such as priority, delay, assurance ability and cost are determined to maximize the system's efficiency and properly distribute resources based on services priority. Then an optimizing algorithm will be suggested to select grid resource based on hybrid of genetic and simulated annealing algorithms.