A Brief Review on Prediction Methods for Cloud Resource Management

The complexity of apps running on cloud platforms is evident in their nature. Every application has distinct needs for processing power and memory at various times. In order to effectively cater to tenants’ varied computing requirements while reducing inefficiencies in resource usage, cloud service providers need to be able to predict workload variations with high accuracy and evaluate how cloud apps use resources. In the recent past, there has been a significant increase in research and engineering efforts in cloud computing. These efforts are focused on developing methods to address important issues such as predicting changes in workloads, assessing resource demands, perceiving quality of service (QoS), and detecting anomalies. By reviewing these algorithms in depth, this paper aims to provide academics a better grasp of the most recent developments in this area.