Cloud Workload Turning Points Prediction via Cloud Feature-Enhanced Deep Learning

Cloud workload turning point is either a local peak point standing for workload pressure or a local valley point standing for resource waste. Predicting such critical points is important to give warnings to system managers to take precautionary measures aimed at achieving high resource utilization, quality of service (QoS), and profit of the investment. Existing researches mainly focus more on the workload's future point value prediction only, whereas trend-based turning point prediction is not considered. Moreover, one of the most critical challenges during the prediction is the fact that traditional trend prediction methods which succeed in financial and industrial areas, etc., have a weak ability to represent the cloud features, which means that they cannot describe the highly-variable cloud workloads time series. This article introduces a novel cloud workload turning point prediction approach based on cloud feature-enhanced deep learning. First, we establish a turning point prediction model of cloud server workload considering cloud workload features. Then, a cloud feature-enhanced deep learning model is designed for workload turning point prediction. Experiments on the most famous Google cluster demonstrate the effectiveness of our model compared with state-of-the-art models. To the best of our knowledge, this article is the first systematic research on turning point-based trend prediction of cloud workload time series by cloud feature-enhanced deep learning.