Proffering a New Method for Grid Computing Resource Discovery with Improved Genetic Algorithm by Means of Learning Automata Based on Economic Criteria

Grid computing offers an effective way to build high-performance computing systems, allowing users to efficiently access and integrate geographically distributed computers, data and applications. Grid computing and peer-to-peer computing are both hot topics at present. The convergence of the two systems is increasingly visible and OGSA provides a framework for integrating grid and peer to peer. Resource discovery in peer to peer grid computing systems is a fundamental task which provides searching and locating necessary resources for given processes. Resource discovery involves discovery of appropriate resources required by user applications. Otherwise Economic model for Grid environment presented newly. In economic Grid environments, the producers (resource owners) and consumers (resource users) have different goals, objectives, strategies and supply-and-demand patterns. Mechanism based on economic models is an effective approach to solve the problem of grid resources management. In this paper a new method for grid computing resource discovery based on economic criteria by means of improved genetic algorithm with learning automata have been proposed. Theoretical analysis and simulations prove that improved genetic algorithm with learning automata in peer-to-peer grid can improve the performance of resource discovery.