Computer
Mohammed E. Seno; Ban N. Dhannoon; Omer K. Jasim Mohammad
Abstract
Cloud computing is an evolving and high-demand research field at theforefront of technological advancements. It aims to provide software resources andoperates based on service-oriented delivery. Within the infrastructure as a service (IaaS)framework, the cloud offers end customers access to crucial infrastructure ...
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Cloud computing is an evolving and high-demand research field at theforefront of technological advancements. It aims to provide software resources andoperates based on service-oriented delivery. Within the infrastructure as a service (IaaS)framework, the cloud offers end customers access to crucial infrastructure resources,including CPU, bandwidth, and memory. When a cloud system fails to deliver asexpected, it is referred to as an event, signifying a deviation from the anticipated service.To meet their service-level agreement (SLA) obligations, cloud service providers (CSPs)must ensure continuous access to fault-tolerant, on-demand resources for their clients,particularly during outages. Consequently, finding the most efficient ways to accomplishtasks while considering the rapid depletion of resources has become an urgent concern.Researchers are actively working to develop optimal strategies tailored to the cloudenvironment. Machine learning plays a critical role in these endeavors, serving as a keycomponent in various cloud computing platforms. This study presents a comprehensiveliterature review of current research papers that employ machine learning algorithms topropose strategies for optimizing cloud computing environments. Additionally, the surveyprovides authors with invaluable resources by extensively exploring a diverse range ofmachine learning techniques and their applications in the field of cloud computing. Byexamining these areas, researchers aim to enhance their understanding of efficientresource allocation and scheduling, addressing the challenges posed by resource scarcitywhile meeting SLA obligations.
