A DSL-MCDA Model for Energy Consumption-Aware in Cloud Computing
Karima Saidi, Ouassila Hioual, Abderrahim Siam · 2019
Cloud Computing environment provides an infinite number of resources to Cloud users with different utilities such as computing power, storage space, servers and applications. There are various issues in the cloud resource allocation area that still need to be solved. To address some of these issues, we propose in this paper the DSL-MCDA (Deep Supervised Learning - Multi Criteria Decision Analysis) Model for Energy Consumption-Aware in Cloud Computing as a proactive model of energy consumption-aware. The main goal of the proposed model is to reduce the energy consumption since virtual machines (VM) are dynamically consolidated to lesser number of physical machines (PMs) by selecting an optimal resource that satisfies the demands of cloud users, and this based on classification in deep supervised learning and multi-criteria decision analysis (MCDA) methods.