An efficient resource discovery framework for pure unstructured peer-to-peer systems
In this paper, we propose an efficient resource discovery framework allowing pure unstructured peer-to-peer systems to respond to requests at run time with a high success rate while preserving the local autonomy of member machines. There are five units in the proposed framework that respectively gather information about the status of resources, make decisions, detect the states of member machines, discover resources to respond to requests in normal and dynamic conditions, and balance the load of local machines. Efficient resource discovery is achieved by the deployment of a newly introduced mechanism that is placed on every machine allowing it to figure out its states before and after accepting other machines requests for its resources using a state model and deciding whether to accept or reject those requests. This state model accurately estimates the machines state based on the resources and processes of the machine before and after accepting the request. We have experimentally compared the proposed mechanism with random, learning-based, and state-based search mechanisms with regard to the number of missed requests, network bandwidth due to transferred messages, number of associated machines in a discovery operation, time required to process information in discovery operation, processing time in machines, and the number of faults per request. The results show significant improvement of some of these parameters, specially network bandwidth and the number of missed requests in a dynamic condition, under our framework.
resource discovery pure unstructured peer-to-peer systems state-based search learning