Dynamic Resource Allocation in Grid Computing Using Fuzzy-genetic Algorithm

The main aspiration of Grid Computing is to aggregate the maximum available idle computing power of the distributed resources, and provide well-organized services to users. An efficient grid resource allocation is very much essential to achieve this aspiration. Researchers had proposed many grid scheduling mechanisms in the past to fulfill this aspiration, but, these are mostly based on traditional techniques. Our proposed new Intelligent Scheduling Algorithm (ISA) uses soft computing techniques such as Fuzzy C-mean clustering, k-mean clustering and the Genetic Algorithm for effective job and resource management which in turn facilitates efficient scheduling. In our approach, the Fuzzy C-mean algorithm (overlapping clustering technique) is employed for resource classification, k-mean clustering for job categorization and the Extended Genetic Algorithm (EGA) for mapping a given job to the most suitable resource.