Prediction-based scheduling techniques for cloud data center’s workload: a systematic review

Abstract

A cloud data center provides various facilities such as storage, data accessibility, and running many specific applications on cloud resources. The unpredictable demand for service requests in cloud workloads affects the availability of resources during scheduling. It raises the issues of inaccurate workload prediction, lack of fulfillment in resource demands, load unbalancing, high power consumption due to heavy loads, and problems of under and overutilization of resources. Therefore, an efficient scheduling technique and an accurate forecasting model are needed to overcome these issues. Also, to deal with these challenges and provide optimal solutions, researchers must have a robust knowledge of cloud workloads, their types, issues, existing technologies, their advantages and disadvantages. However, previous research indicates limited systematic review studies exist for cloud workload applications with prediction-based scheduling techniques. Therefore, a survey is required that provides information related to cloud workload. To fulfill this requirement, the current study collects the related articles published in the past years. This paper is a systematic review study of prediction-based scheduling techniques that extract and evaluate data based on five criteria. It includes the datasets of different workload applications, resources, current prediction and scheduling techniques, and their related parameters. The survey is quite useful for academicians who want to select the problem and develop new techniques for issues related to cloud workload applications. It also gives an idea of existing approaches that are already implemented and employed.