A Literature Review and Taxonomy on Workload Prediction in Cloud Data Center

Abstract

Resource management is one of the most challenging task in the cloud data center. These challenges have raised from the dynamic nature and high uncertainty in the cloud environment. Moreover, allocating resources over time may lead the sub-optimal execution environment due to significant up and drop in the workload that have some time dependent patterns. Therefore, it requires some time-sensitive techniques for optimising the resources utilization in cloud data center. In this paper, we discuss the workload prediction techniques that forecast the workload in the cloud environment and the value of predicted workload guides for optimising the resources. Furthermore, we present the workload taxonomy which is classified into (i) workload predictor and (ii) model fitting. In addition, we provide an extensive discussion on the workload predictors and further classified into temporal and non-temporal.