Open Access

ARTICLE

Intelligent Model for Predicting the Quality of Services Violation

Muhammad Adnan Khan^1,2, Asma Kanwal³, Sagheer Abbas³, Faheem Khan⁴, T. Whangbo^4,*

1 Pattern Recognition and Machine Learning Lab., Department of Software, Gachon University, Seongnam, Gyeonggido, 13120, Korea
2 Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University Lahore Campus, Lahore, 54000, Pakistan
3 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
4 Department of Computer Engineering, Gachon University, Seongnam, 13557, Korea

* Corresponding Author: T. Whangbo. Email:

Computers, Materials & Continua 2022, 71(2), 3607-3619. https://doi.org/10.32604/cmc.2022.023480

Received 09 September 2021; Accepted 15 October 2021; Issue published 07 December 2021

Abstract

Cloud computing is providing IT services to its customer based on Service level agreements (SLAs). It is important for cloud service providers to provide reliable Quality of service (QoS) and to maintain SLAs accountability. Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it. Cloud users’ major concerns; the factors for service reliability are based on response time, accessibility, availability, and speed. In this paper, we, therefore, experiment with the parallel mutant-Particle swarm optimization (PSO) for the detection and predictions of QoS violations in terms of response time, speed, accessibility, and availability. This paper also compares Simple-PSO and Parallel Mutant-PSO. In simulation results, it is observed that the proposed Parallel Mutant-PSO solution for cloud QoS violation prediction achieves 94% accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.

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Citations