An Efficient Multi-Cloud Service Composition Using a Distributed Multiagent-Based, Memory-Driven Approach

Abstract

Cloud services are often distributed across several data centers requiring new scalable approaches to efficiently perform searching to reduce the energy and price cost of fulfilling requests. Multiagent-based systems have arisen as a powerful technique for improving distributed processing on a wide scale, which can operate in environments where partial observability is the norm and the cost of prolonged search can be exponential. In this paper, we present a multiagent-based service composition approach, using agent-matchmakers and agent-representatives, for the efficient retrieval of distributed services and propagation of information within the agent network to reduce the amount of brute-force search. Our extensive simulation results indicate that by introducing localized agent-based memory searches, the amount of actions (with their associated energy costs) can be reduced by over 50 percent which results in a lower energy cost per composition request.