Name
Title | ||
|---|---|---|
Multi-Granularity Power Prediction for Data Center Operations via Long Short-Term Memory Network |
Abstract | ||
|---|---|---|
The increasing numbers of the applications and requirement of cloud computing have made huge power consumption in data centers, which brings the problems of the high cost and resource waste. This problem attracts significant attention from academia and industry. A critical approach to solve this problem is constructing an intelligent energy management system for data centers. Furthermore, an efficient assessment and prediction module of power consumption in data centers is an essential part of the management system. It facilitates cloud service providers to perform workflow scheduling at the minimal cost and energy efficiency management with the requirement of QoS. Since the assessment and prediction of power consumption correlate, this paper presents a multi-granularity approach for power consumption prediction in data centers, which combines multi-task learning with the LSTM network. We first transfer a multi-granularity power prediction problem into a multi-task regression problem to assess and predict the power consumption of data center system maintenance and scheduling operations. Due to the time requirement for workflow and container scheduling, the prediction interval is 30 seconds. Then we propose an efficient long short-term memory network for the multigranularity prediction. The experimental results show our model outperforms other prediction models on the real datasets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00037 | 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) |
Keywords | DocType | ISBN |
Data center, Time series prediction, Energy efficiency, Power consumption | Conference | 978-1-7281-4329-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||