Resource Allocation on a Hybrid Cloud for Smart Grids

Alan Briones, Ramon Martín de Pozuelo, Joan Navarro, Agustín Zaballos


The use of hybrid clouds enables companies to cover their demands of IT resources saving costs and gaining flexibility in the deployment of infrastructures by paying under demand these resources. However, considering a scenario with various services to be allocated in more than one cloud, it is necessary to find the distribution of services that minimizes the overall operating costs. This paper researches on the resource allocation methodology to be applied in a multi-cloud scenario based on the findings derived from the framework used for the FINESCE project. The purpose of this work is to define a methodology to assist on the hybrid cloud selection and configuration in the Smart Grid for both generic and highly-constrained scenarios in terms of latency and availability. Specifically, the presented method is aimed to determine which is the best cloud to allocate a resource by (1) optimizing the system with the information of the network and (2) minimizing the occurrence of collapsed or underused virtual machines. Also, to assess the performance of this method and any alternative proposals, a general set of metrics has been defined. These metrics have been refined taking into account the expertise of FINESCE partners in order to shape Smart Grid clouds and reduce the complexity of computation. Finally, using the data extracted from the FINESCE testbed, a decision tree is used to come up with the best resource allocation scheme.


Cloud Computing; Decision Tree; Hybrid Cloud; Resource Allocation; Smart Grids; Virtual Machines

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