A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm. Issue 18 (14th September 2021)

• Record Type: Journal Article
• Authors: Hashemi, Masoud; Javaheri, Danial; Sabbagh, Parisa; Arandian, Behdad; Abnoosian, Karlo
• Is Part Of: IET communications. Volume 15:Issue 18(2021)
• Page Start: 2342
• Page End: 2353
• Publication Date: 2021-09-14
• Subjects: cloud computing -- improved grey wolf optimization algorithm -- virtual machines allocation -- Optimisation techniques -- Computer engineering -- Internet software; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/cmu2.12274
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract: 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.