TY  - EJOU
AU  - Khan, Muhammad Adnan 
AU  - Kanwal, Asma 
AU  - Abbas, Sagheer 
AU  - Khan, Faheem 
AU  - Whangbo, T. 

TI  - Intelligent Model for Predicting the Quality of Services Violation
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 71
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Accountability; particle swarm optimization; mutant particle swarm optimization; quality of service; service level agreement

DO  - 10.32604/cmc.2022.023480