Predictive Cloud resource management framework for enterprise workloads

AbstractThe study proposes an innovative Predictive Resource Management Framework (PRMF) to overcome the drawbacks of the reactive Cloud resource management approach. Performance of PRMF was compared with that of a reactive approach by deploying a timesheet application on the Cloud. Key metrics of the simulated workload patterns were monitored and analyzed offline using information gain module present in PRMF to determine the key evaluation metric. Subsequently, the best-fit model for the key evaluation metric among Autoregressive Integrated Moving Average (ARIMA) (1⩽p⩽4, 0<d