2011 ThermoCastACyberPhysicalForecas

• (Li et al., 2011) ⇒ Lei Li, Chieh-Jan Mike Liang, Jie Liu, Suman Nath, Andreas Terzis, and Christos Faloutsos. (2011). “ThermoCast: A Cyber-physical Forecasting Model for Datacenters.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020611

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+ThermoCast%3A+A+Cyber-physical+Forecasting+Model+for+Datacenters
• http://dl.acm.org/citation.cfm?id=2020408.2020611&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; cyber physical modeling; data center energy efficiency; experimentation; measurement; measurement techniques; modeling techniques; performance; time series forecasting

Abstract

Efficient thermal management is important in modern data centers as cooling consumes up to 50% of the total energy. Unlike previous work, we consider proactive thermal management, whereby servers can predict potential overheating events due to dynamics in data center configuration and workload, giving operators enough time to react. However, such forecasting is very challenging due to data center scales and complexity. Moreover, such a physical system is influenced by cyber effects, including workload scheduling in servers. We propose ThermoCast, a novel thermal forecasting model to predict the temperatures surrounding the servers in a data center, based on continuous streams of temperature and airflow measurements. Our approach is (a) capable of capturing cyberphysical interactions and automatically learning them from data; (b) computationally and physically scalable to data center scales; (c) able to provide online prediction with real-time sensor measurements. The paper's main contributions are: (i) We provide a systematic approach to integrate physical laws and sensor observations in a data center; (ii) We provide an algorithm that uses sensor data to learn the parameters of a data center's cyber-physical system. In turn, this ability enables us to reduce model complexity compared to full-fledged fluid dynamics models, while maintaining forecast accuracy; (iii) Unlike previous simulation-based studies, we perform experiments in a production data center. Using real data traces, we show that ThermoCast forecasts temperature better than a machine learning approach solely driven by data, and can successfully predict thermal alarms 4.2 minutes ahead of time.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 ThermoCastACyberPhysicalForecas	Christos Faloutsos Lei Li Chieh-Jan Mike Liang Jie Liu Suman Nath Andreas Terzis			ThermoCast: A Cyber-physical Forecasting Model for Datacenters				10.1145/2020408.2020611		2011