International Conference on Computer and Knowledge Engineering

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A Review on Machine Learning Methods for Workload Prediction in Cloud Computing

Authors :

Mohammad Yekta¹ Hadi Shahriar Shahhoseini²

1- School of Electrical Engineering, Iran University of Science and Technology 2- School of Electrical Engineering, Iran University of Science and Technology

Keywords :

Workload Prediction،Cloud Computing،Resource Provisioning،Machine Learning،Deep Learning

Abstract :

As technology advances, the volume of data and computing requirements has increased significantly. Personal computers are no longer sufficient to meet the computational and storage needs of users' data. This has led to the emergence of cloud computing, a new technology that transfers computational and data-processing tasks from PCs to data centers. Cloud computing offers resources in the form of infrastructure, platforms, and software as services, all accessible to users through the Internet. Resource provisioning is a critical aspect of cloud computing. To effectively allocate resources, accurate prediction of cloud workloads is essential. Workload prediction plays a crucial role in enhancing efficiency, reducing costs, optimizing cloud performance, maintaining a high level of quality of service, and minimizing energy consumption. In this paper, we conduct a comprehensive review of the most significant machine learning algorithms used for workload prediction. The algorithms are categorized and discussed, while also highlighting open issues and challenges that could serve as potential directions for future research.