A Systematic Review of Advanced Machine Learning Algorithms for Optimizing Quality of Service Parameters in Cloud Computing Environments

Abstract

Cloud computing has revolutionized the IT industry by providing scalable, on-demand resources. Ensuring optimal Quality of Service (QoS) parameters, such as availability, reliability, and response time, is crucial for maintaining user satisfaction and system efficiency. Advanced Machine Learning (ML) algorithms have emerged as powerful tools for optimizing these QoS parameters in cloud environments. This systematic review aims to synthesize the latest research on the application of ML algorithms in optimizing QoS in cloud computing. We review various ML techniques, including supervised learning, unsupervised learning, reinforcement learning, and deep learning, highlighting their specific applications and benefits in the cloud context. Key areas of focus include resource allocation, load balancing, fault tolerance, and energy efficiency. The review identifies significant contributions from recent studies, categorizing them based on the ML approach and the QoS parameters addressed. Supervised learning algorithms, such as regression and classification, are widely used for predictive maintenance and performance prediction. Unsupervised learning techniques, like clustering, aid in anomaly detection and workload pattern recognition. Reinforcement learning has shown promise in dynamic resource management by learning optimal policies through interaction with the environment. Deep learning approaches, particularly neural networks, are leveraged for complex pattern recognition and predictive analytics. The review also discusses the challenges in integrating ML algorithms into cloud environments, including data privacy, scalability, and computational overhead. Future research directions are proposed, emphasizing the need for hybrid models, real-time adaptation, and more robust evaluation metrics. By providing a comprehensive overview of the state-of-the-art ML applications for QoS optimization, this review aims to guide researchers and practitioners in developing more efficient and resilient cloud computing systems.