Enhancement of Cloud Computing Environment Using Machine Learning Algorithms MLCE

Abstract

Cloud computing is an evolving and high-demand research field at the forefront of technological advancements. It aims to provide software resources and operates 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 as expected, 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 accomplish tasks while considering the rapid depletion of resources has become an urgent concern. Researchers are actively working to develop optimal strategies tailored to the cloud environment. Machine learning plays a critical role in these endeavors, serving as a key component in various cloud computing platforms. This study presents a comprehensive literature review of current research papers that employ machine learning algorithms to propose strategies for optimizing cloud computing environments. Additionally, the survey provides authors with invaluable resources by extensively exploring a diverse range of machine learning techniques and their applications in the field of cloud computing. By examining these areas, researchers aim to enhance their understanding of efficient resource allocation and scheduling, addressing the challenges posed by resource scarcity while meeting SLA obligations. Index Terms— Cloud computing, Resource allocation Prediction, Energy-efficient resource management, Task Scheduling, Machine Learning.