Machine Learning Approaches for Load Balancing in Cloud Computing Services

Abstract

As the demand for cloud services increases, optimization of resources becomes essential. Static algorithms are no longer sufficient to solve cloud-related challenges such as imbalanced workload distribution in Virtual Machines or improper resource allocation to cloud users. Thus, the need to explore other rich approaches can greatly improve cloud applications' performance and tackle the above challenges. This research investigates the latest Machine Learning approaches that can tackle the above challenges in cloud environment. A comparison of these approaches included highlighting their strengths and weaknesses to induce a research gap useful for upcoming researchers in the field.