In this paper, we present a novel combinatorial auction (CA) based cloud market model that facilitates dynamic collaboration (DC) among cloud providers (CPs) for providing composite/collaborative cloud services to consumers and hence can address the interoperability and scalability issues for cloud computing. Also to minimize the conflicts that may happen when negotiating among providers in a DC platform, we propose a new auction policy in CA that allows a CP to dynamically collaborate with suitable partner CPs to form a group before joining the auction and to publish their group bids as a single bid to fulfill the service requirements completely. But to find a good combination of CP partners is a NP-hard problem. So we propose a promising multi-objective (MO) optimization model for CP partner selection that not only uses their individual information (INI) but also their past collaborative relationship information (PRI) which is seldom considered in existing approaches. A multi-objective genetic algorithm (MOGA) called MOGA-IC is also developed to solve the model. We implemented our proposed CACM model and the MOGA-IC in a simulated environment and study their economic efficiency and performance with existing model and algorithm. The experimental results show that the proposed MOGA-IC can support satisfactory and high quality partner selection in CACM model.
Mendeley saves you time finding and organizing research
Choose a citation style from the tabs below