A thermal-aware energy-efficient virtual machine placement algorithm based on fuzzy controlled binary gravitational search algorithm (FC-BGSA)

• Published in: Cluster computing Vol. 25; no. 2; pp. 1015 - 1033
• Main Authors: Aghasi, Ali, Jamshidi, Kamal, Bohlooli, Ali
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Adaptation; Business models; Cloud computing; Computer centers; Computer Communication Networks; Computer Science; Cooling; Datasets; Energy consumption; Energy distribution; Energy efficiency; Fuzzy control; Fuzzy logic; Genetic algorithms; Heat generation; Heterogeneity; Heuristic; Heuristic methods; Infrastructure; Migration; Operating Systems; Optimization algorithms; Placement; Power consumption; Processor Architectures; Search algorithms; Software services; Virtual environments; Workloads; United States > US; Cloud computing; Fuzzy control; Cooling energy; Metaheuristic algorithm; Temperature management; Virtual machine placement
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-021-03476-0

The remarkable growth of cloud computing applications has caused many data centers to encounter unprecedented power consumption and heat generation. Cloud providers share their computational infrastructure through virtualization technology. The scheduler component decides which physical machine hosts the requested virtual machine. This process is virtual machine placement (VMP) which, affects the power distribution, and thereby the energy consumption of the data centers. Due to the heterogeneity and multidimensionality of resources, this task is not trivial, and many studies have tried to address this problem using different methods. However, the majority of such studies fail to consider the cooling energy, which accounts for almost 30% of the energy consumption in a data center. In this paper, we propose a metaheuristic approach based on the binary version of gravitational search algorithm to simultaneously minimize the computational and cooling energy in the VMP problem. In addition, we suggest a self-adaptive mechanism based on fuzzy logic to control the behavior of the algorithms in terms of exploitation and exploration. The simulation results illustrate that the proposed algorithm reduced energy consumption by 26% in the PlanetLab Dataset and 30% in the Google cluster dataset relative to the average of compared algorithms. The results also indicate that the proposed algorithm provides a much more thermally reliable operation.