Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters

Lu, Yao; Liu, Lu; Panneerselvam, John; Zhai, Xiaojun; Sun, Xiang; Antonopoulos, Nick

Abstract

Cloud datacenters are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess energy consumption of the datacentre include running servers at low level of workloads and over-provisioning of server resources to the arriving workloads during execution. To this end, predicting the future workload demands and their respective behaviors at the datacenters are being the focus of recent researches in the context of sustainable datacenters. But prediction analytics of cloud workloads suffer various limitations imposed by the dynamic and unclear characteristics of Cloud workloads. This paper proposes a novel forecasting model named K-means based Rand Variable Learning Rate Backpropagation Neural Network (K-RVLBPNN) for predicting the future workload arrival trend, by exploiting the latency sensitivity characteristics of Cloud workloads, based on a combination of improved K-means clustering algorithm and Backpropagation Neural Network (BPNN) algorithm. Experiments conducted on real-world Cloud datasets shows that the proposed model shows better prediction accuracy, outperforming the traditional Hidden Markov Model, Naïve Bayes Classifier, and our earlier RVLBPNN model, respectively.

Citation

Lu, Y., Liu, L., Panneerselvam, J., Zhai, X., Sun, X., & Antonopoulos, N. (2020). Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters. IEEE Transactions on Sustainable Computing, 5(3), 308-318. https://doi.org/10.1109/TSUSC.2019.2905728

• Journal Article Type: Article
• Acceptance Date: Mar 9, 2019
• Online Publication Date: Mar 18, 2019
• Publication Date: 2020-09
• Deposit Date: Jan 26, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 3
• Pages: 308-318
• DOI: https://doi.org/10.1109/TSUSC.2019.2905728
• Public URL: http://researchrepository.napier.ac.uk/Output/2717645