A multi-objective cloud energy optimizer algorithm for federated environments

Energy is a considerable portion of the cost of a cloud data center. In addition to energy, carbon emission tax imposes another cost on data centers, which have different policies in different cities around the world. Most of the cloud data centers are geographically distributed, which can have a huge advantage in reducing such costs. In addition, in recent years, cloud federation, in which multiple cloud providers share their IT infrastructures voluntarily, could play a crucial role in minimizing the energy consumption of cloud data centers. On the other hand, improper VM placement results frequent VM migration, constant turning off/on physical machines, service quality degradation and energy consumption increase. To tackle these weaknesses, we propose a multi-objective algorithm, Called OUR-ACS, to minimize data centers' energy consumption, carbon emission, and the total expenses (energy cost + carbon tax) in a federated environment considering both initial VM placement and VM consolidation approaches. We evaluate the efficiency of our proposed algorithm by using the ClousSim Plus simulator toolkit using realworld datasets. Compared to the competing algorithms, our simulation results indicate that our proposed algorithm could reduce the total energy consumption, carbon emission, and the total costs of multiple cloud data centers by an average of 37.6%, 41%, and 25%, respectively, in a federated environment.(c) 2022 Elsevier Inc. All rights reserved.