International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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A Survey on Predicting Workloads and Optimising QoS in the Cloud Computing

  • Omar F. Aloufi (Information Systems department, College of Computer Science and Engineering. Taibah University) ;
  • Karim Djemame (School of Computing, University of Leeds) ;
  • Faisal Saeed (Information Systems department, College of Computer Science and Engineering. Taibah University) ;
  • Fahad Ghabban (Information Systems department, College of Computer Science and Engineering. Taibah University)
  • Received : 2024.02.05
  • Published : 2024.02.29
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Abstract

This paper presents the concept and characteristics of cloud computing, and it addresses how cloud computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one's workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. This paper then looks at the primary goal of this project, which is to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In this survey, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies and those that used in this field. The paper concludes with a summary of the literature on predicting workloads and optimising QoS in the cloud computing.

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References

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