A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm

• Published in: IET communications Vol. 15; no. 18; pp. 2342 - 2353
• Main Authors: Hashemi, Masoud, Javaheri, Danial, Sabbagh, Parisa, Arandian, Behdad, Abnoosian, Karlo
• Format: Journal Article
• Language: English
• Published: Stevenage John Wiley & Sons, Inc 01.11.2021; Wiley
• Subjects: Algorithms; Animal populations; Cloud computing; Computer engineering; Energy consumption; Energy efficiency; Entropy; Genetic algorithms; Heuristic; Heuristic methods; improved grey wolf optimization algorithm; Internet software; Optimisation techniques; Optimization; Optimization algorithms; Resource utilization; Software; Traveling salesman problem; Virtual environments; virtual machines allocation; Workloads
• ISSN: 1751-8628; 1751-8636
• DOI: 10.1049/cmu2.12274

Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP‐hard, meta‐heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy‐aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works.