An Adaptive Multi Agent Service Discovery for Peer to Peer Cloud Services

Cloud computing is evolving into a popular platform that enables on-demand provisioning of computing resources to a growing population of clients. Core to the provisioning of service in the cloud is the discovery of these services in an efficient and timely manner. Centralized and hierarchical approaches to service discovery have exhibited bottlenecks as network load increases and limitation in scalability. Efforts have been made in combining cloud systems and Peer to peer P2P systems to address the problem encountered in the conventional service discovery approaches but not without a new set of challenges ranging from network flooding to poor performance in dynamic networks. This paper presents an efficient and scalable approach for semantic cloud service discovery in a P2P cloud environment. The approach is based on Learning Automata LA and Ant Colony Optimization ACO. The ability of ACO to adapt to changes in real time makes it a better choice in dynamic environments such as cloud. We evaluate this approach against the some existing P2P service discovery approaches, the proposed mechanism showed an improved performance.

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