Energy-aware cloud workload prediction method using machine learning techniques

The popularity of cloud computing services is increasing significantly, which requires energy-aware workload prediction systems to improve resource efficiency and decrease the power consumption of IT infrastructures. Due to the significant amount of historical data that can be collected in these cloud infrastructures, statistical methods and machine learning-based solutions provide promising results for accurate workload predictions. This paper investigated a publicly available Microsoft Azure trace containing a snapshot of millions of virtual machine workloads from one region to create a cloud workload prediction method. We performed several data preparation and aggregation steps to identify the significant information from the time-series trace data. Based on the processed data, the key contribution of this paper is an evaluation of the ARIMA statistical model and a fine-tuned IBM’s TinyTimeMixer machine learning model to create an accurate workload prediction method.