OPTIMIZED QUERY FORWARDING FOR RESOURCE DISCOVERY IN UNSTRUCTURED PEER-TO-PEER GRIDS

One of the challenges of resource discovery in unstructured peer-to-peer grid systems is minimizing network traffic. The network traffic arises by query messages that are broadcasted to other peers in order to find the appropriate resources. Blind search methods that are employed in such systems do not work well because every specific query generates high query traffic, which quickly overwhelms the network. Informed search methods usually use recorded history of previous queries to decide where the new queries should be sent. Such methods can reduce network traffic but do not consider the path length. In this study, a method was proposed in which both the path length and network traffic are considered. This approach reduces the hop numbers and prevents massive flooding of query messages. To do this, it selects optimum neighbor peer(s) in order to optimize query forwarding. The proposed approach uses statistical tables that are obtained from recorded history of previous queries. Then a genetic algorithm is applied to these statistical tables to find the optimum neighbor peer(s). The proposed approach showed that query forwarding through the optimum neighbor peer(s) has a greater probability of finding a requested resource with lower hop numbers. This method was compared with random walk and flooding approaches. It was observed that the network traffic remarkably decreased in comparison to a flooding approach, whereas it was similar to the results obtained by a random walk method. Moreover, this method provided a higher success rate in comparison to the random walk approach, whereas it was similar to the results obtained by a flooding method.