Federations among sets of Cloud Providers (CPs), whereby a set of CPs agree to mutually use their own resources to run the VMs of other CPs, are considered a promising solution to the problem of reducing the energy cost. In this paper, we address the problem of federation formation for a set of CPs, whose solution is necessary to exploit the potential of cloud federations for the reduction of the energy bill. We devise a distributed algorithm, based on cooperative game theory, that allows a set of CPs to cooperatively set up their federations in such a way that their individual profit is increased with respect to the case in which they work in isolation, and we show that, by using our algorithm and the proposed CPs' utility function, they are able to self-organize into Nash-stable federations and, by means of iterated executions, to adapt themselves to environmental changes. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.
To the best of our knowledge, very little work has been carried out to jointly tackle the problem of dynamically forming stable cloud federations for energy-aware resource provisioning. Indeed, much of the existing work only focuses on a single aspect of the problem. In @cite_29 , the design and implementation of a VM scheduler for a federation of CPs is presented. The scheduler, in addition to manage resources that are local to each CP, is able to decide when to rent resources from other CPs, when to lease own idle resources to other CPs, and when to turn on or off local physical resources. Unlike our work, this work does not consider the problem of forming stable CP federations. In @cite_42 , a cooperative game-theoretic model for federation formation and VM management is proposed. In this work, the federation formation among CPs is analyzed using the concept of network games, but the energy minimization problem is not considered.
@cite_29 @cite_42
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Resource provisioning in Cloud providers is a challenge because of the high variability of load over time. On the one hand, the providers can serve most of the requests owning only a restricted amount of resources, but this forces to reject customers during peak hours. On the other hand, valley hours incur in under-utilization of the resources, which forces the providers to increase their prices to be profitable. Federation overcomes these limitations and allows providers to dynamically outsource resources to others in response to demand variations. Furthermore, it allows providers with underused resources to rent them to other providers. Both techniques make the provider getting more profit when used adequately. Federation of Cloud providers requires having a clear understanding of the consequences of each decision. In this paper, we present a characterization of providers operating in a federated Cloud which helps to choose the most convenient decision depending on the environment conditions. These include when to outsource to other providers, rent free resources to other providers (i.e., insourcing), or turn off unused nodes to save power. We characterize these decisions as a function of several parameters and implement a federated provider that uses this characterization to exploit federation. Finally, we evaluate the profitability of using these techniques using the data from a real provider. In cloud computing, organizations can form cooperation to share the available resource to reduce the cost. This is referred to as the multi-organization cloud computing environment. In this paper, we address the issues of virtual machine management and cooperation formation in such an environment. First, for the cooperative organizations, an optimization model is formulated and solved for the optimal virtual machine allocation so that the total cost is minimized. Then, the cost management based on cooperative game theory is applied to obtain the fair share of the cost. Second, the cooperation formation among organizations is analyzed using the network game. With the dynamic cooperation formation, the stable cooperation structure is obtained. Both cooperative virtual machine management and cooperation formation are intertwined, in which the proposed optimization and game models can be used to obtain the solution of the rational organizations to minimize their own costs.