QoS Analysis for Cloud-Based IoT Data Using Multicriteria-Based Optimization Approach

L Jayakumar; R Jothi Chitra; J Sivasankari; S Vidhya; Laura Alimzhanova; Gulnur Kazbekova; Bakhytzhan Kulambayev; Alma Kostangeldinova; S Devi; Dawit Mamiru Teressa

doi:10.1155/2022/7255913

QoS Analysis for Cloud-Based IoT Data Using Multicriteria-Based Optimization Approach

Comput Intell Neurosci. 2022 Sep 7:2022:7255913. doi: 10.1155/2022/7255913. eCollection 2022.

Authors

Affiliations

¹ Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India.
² Department of Electronics and Communication Engineering, Velammal Institute of Technology, Chennai, Tamilnadu, India.
³ Department of Electronics and Communication Engineering, Ultra College of Engineering and Technology, Madurai, Tamilnadu, India.
⁴ Department of Information Technology, Saveetha Engineering College Thandalam, Chennai, Tamilnadu, India.
⁵ Al-Farabi Kazakh National University, Almaty, Kazakhstan.
⁶ Head of the Department of Computer Sciences, C. T. S Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan, Kazakhstan.
⁷ International Information Technology University, Almaty, Kazakhstan.
⁸ Kokshetau University Named Af Sh Ualijhanov, Kokshetau, Kazakhstan.
⁹ Department of Computer Science Engineering, Mother Terasa College of Engineering and Technology, Pudukkottai, Tamil Nadu, India.
¹⁰ Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.

Abstract

This work explains why and how QoS modeling has been used within a multicriteria optimization approach. The parameters and metrics defined are intended to provide a broader and, at the same time, more precise analysis of the issues highlighted in the work dedicated to placement algorithms in the cloud. In order to find the optimal solution to a placement problem which is impractical in polynomial time, as in more particular cases, meta-heuristics more or less approaching the optimal solution are used in order to obtain a satisfactory solution. First, a model by a genetic algorithm is proposed. This genetic algorithm dedicated to the problem of placing virtual machines in the cloud has been implemented in two different versions. The former only considers elementary services, while the latter uses compound services. These two versions of the genetic algorithm are presented, and also, two greedy algorithms, round-robin and best-fit sorted, were used in order to allow a comparison with the genetic algorithm. The characteristics of these two algorithms are presented.

MeSH terms

Algorithms*
Cloud Computing*