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

Abstract

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 on point value prediction only, whereas trend-based turning point prediction is not considered. This is partly due to the fact that traditional trend prediction methods have a weak ability to represent the cloud features, which means that they cannot describe the highly-variable cloud workloads time series. This paper introduces a novel cloud workload turning point prediction approach based on cloud feature-enhanced deep learning. Firstly, we establish a turning point prediction model of cloud server workload by taking cloud workload features into consideration. 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 paper is the first systematic research on turning point-based trend prediction of cloud workload time series by cloud feature-enhanced deep learning.