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

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 know...

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Vydáno v:IET communications Ročník 15; číslo 18; s. 2342 - 2353
Hlavní autoři: Hashemi, Masoud, Javaheri, Danial, Sabbagh, Parisa, Arandian, Behdad, Abnoosian, Karlo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Stevenage John Wiley & Sons, Inc 01.11.2021
Wiley
Témata:
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
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1751-8628
1751-8636
DOI:10.1049/cmu2.12274