A survey on predicting workloads and optimizing QoS in the cloud computing

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. Then, this paper aims to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In addition, 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.