A survey and classification of the workload forecasting methods in cloud computing

Workload prediction is one of the important parts of proactive resource management and auto-scaling in cloud computing. Accurate prediction of workload in cloud computing is of high importance for improving cloud performance, mitigate energy consumptions, meeting the required quality of service (QoS) level, predicting the energy consumption of data centers (DCs), and improving the cloud service providers' scalability. However, in cloud computing context workload prediction is a challenging issue and various schemes using machine learning, data mining, and mathematical methods to deal with this issue. This scheme presents an extensive literature review of the workload prediction schemes proposed in the literature to improve resource management in the cloud DCs. It first provides the required knowledge regarding the workload prediction context and presents a taxonomy of the workload prediction schemes according to their applied prediction algorithm. Moreover, the main contributions of these schemes are illustrated and their major advantages and limitation are specified. At last, the open research opportunities in the workload prediction field are focused and the concluding remarks are presented.