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Title |
A Survey on Predicting Workloads and Optimising QoS in the Cloud Computing |
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Author |
Omar F. Aloufi, Karim Djemame, Faisal Saeed, and Fahad Ghabban |
| Citation |
Vol. 24 No. 2 pp. 59-66 |
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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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Keywords |
Cloud Computing, Optimising Quality of Service, Resource managment. |
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