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As web services proliferate, finding a service that can perform a given request becomes more difficult. In some cases, a composition of several services may be required. In semantic web service composition, a repository of services is given in which input and output parameters of each service are annotated with a concept from ontology. Given request is defined by a set of known input concepts and a set of wanted output concepts. Semantic composer should find a sequence of services, called composition that satisfies the wanted output concept. In this paper, a semantic composer based on ant colony optimization method is proposed. This semantic composer is tested using the challenge set of Web Service Challenges (WSC) 2008. The proposed composer can find optimal composition length in each challenge set in a reasonable time. The result of the suggested composition has been compared with the best result reported in WSC 2008 and in most cases; it performs similar or better results than the other systems.

One of the main challenges in peer-to-peer based volunteer computing systems is efficient resource discovery algorithm. Load balancing is a part of resource discovery algorithm and aims to minimize the overall response time of the system. This paper introduces an analytical model based on distributed parallel queues to optimize the average response time of the system in a distributed manner. The proposed resource discovery algorithm consists of two phases. In the first phase, it selects peers in a load-balanced manner based on QoS constraints of request. In the second phase, a proximity-aware feature is applied to select the peer with minimum communication overhead among selected peers in the first phase. Two dispatching strategies are proposed for the load balancing based on stochastic analysis of routing in the distributed parallel queues. These policies adopt probabilistic and deterministic sequences to redirect requests to the capable peers in the system. Simulation results show that the proposed resource discovery algorithm improves the response time of user's requests by a factor of 1.8 under a moderate load.