Novel energy-aware approach to resource allocation in cloud computing
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2021-12
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Abstract
Abstract. In this paper, we address the issue of resource allocation in a Cloud Computing environment. Since the need for cloud
resources has led to the rapid growth of data centers and the waste of idle resources, high-power consumption has emerged.
Therefore, we develop an approach that reduces energy consumption. Decision-making for adequate tasks and virtual machines
(VMs) with their consolidation minimizes this latter. The aim of the proposed approach is energy efficiency. It consists of two
processes; the first one allows the mapping of user tasks to VMs. Whereas, the second process consists of mapping virtual
machines to the best location (physical machines). This paper focuses on this latter to develop a model by using a deep neural
network and the ELECTRE methods supported by the K-nearest neighbor classifier. The experiments show that our model
can produce promising results compared to other works of literature. This model also presents good scalability to improve the
learning, allowing, thus, to achieve our objectives.