A High-Fidelity Temperature Distribution Forecasting System for Data Centers

Chen, Jinzhu; Tan, Rui; Wang, Yu; Xing, Guoliang; Wang, Xiaorui; Wang, Xiao Dong; Punch, Bill; Colbry, Dirk

doi:10.1109/rtss.2012.73

Cited by 45 publications

(22 citation statements)

References 10 publications

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“…In [29], CFD has been successfully applied for geospatial risk assessment of wind channels in urban area with high accuracy. CFD modeling has also been used in sensor placement problem [30] and temperature forecasting [31] in data center environment. CFD models are built to capture extra hot spot scenarios.…”

Section: Related Workmentioning

confidence: 99%

Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

Xing

et al. 2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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Abstract-We collaborate with environmental scientists to study the hydrodynamics and water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the complex urban landform created by surrounding buildings. In this work, we study an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir with a limited number of wind sensors. Unlike existing sensor placement solutions that assume Gaussian process of target phenomena, this study measures the wind which inherently exhibits strong nonGaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons which follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind in the presence of surrounding buildings. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir surface in real time. 10 wind sensors are finally deployed around or on the water surface of an urban reservoir. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction approach provides accurate wind measurement which outperforms the state-of-the-art Gaussian model based or interpolation based approaches.

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Section: Related Workmentioning

confidence: 99%

Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

Xing

et al. 2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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“…A challenge in the design of any predictive control system is how to cope with prediction errors [10] [13]. LetT l,k denote the predicted inlet temperature for the l-th server at the prediction horizon k. We assume that the prediction error (i.e.,T l,k − T l,k ) follows the normal distribution N (µ l,k , σ 2 l,k ), which will be empirically verified in Section V-B.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…PTEC integrates a real-time data-driven temperature prediction algorithm [10] that predicts server inlet temperatures based on cyber and physical status of the data center. This section briefly reviews the algorithm.…”

Section: B Real-time Temperature Predictionmentioning

confidence: 99%

PTEC: A System for Predictive Thermal and Energy Control in Data Centers

Chen

Tan

Xing

et al. 2014

2014 IEEE Real-Time Systems Symposium

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Abstract-Current data centers often adopt conservative and static settings for cooling and air circulation systems, leading to excessive energy consumption. This paper presents the design and evaluation of PTEC -a system for predictive thermal and energy control in data centers. PTEC leverages the server built-in sensors and monitoring utilities, as well as a wireless sensor network, to monitor both the cyber and physical status of a data center. By predicting the temperature evolution of a data center in real time, PTEC finds the temperature setpoints, the cold air supply rates, and the speeds of server internal fans to minimize the expected total energy consumption of cooling and circulation systems. Moreover, PTEC enforces the upper bounds on server inlet temperatures and their temporal variations to prevent server overheating and reduce server hardware failure rate. We evaluated PTEC on a hardware testbed consisting of 15 servers and a total of 23 temperature and power sensors, as well as through Computational Fluid Dynamics (CFD) simulations based on real data traces collected from a data center with 229 servers. The experimental results show that PTEC can reduce the cooling and circulation energy consumption by more than 30%, compared with baseline thermal control strategies.

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“…Similar systems include [20]. We develop our own testbed where we convert the wired building management systems into wireless without changing upper layer building operational protocols [21]; 2) there are studies on physical modeling of the building thermal systems [22][23] [24]; with an aim to better understand cyber-physical co-designs and 3) there are algorithms on wise and automatical device turning-off to save electricity [25] [26], assisted by fine-grained data collection and/or thermal modeling, inference on human presence [27], or human participatory sensing/voting for thermal comfort [28].…”

Section: Related Workmentioning

confidence: 99%

Minimizing Building Electricity Costs in a Dynamic Power Market: Algorithms and Impact on Energy Conservation

Pan

Wang

Cao

et al. 2013

2013 IEEE 34th Real-Time Systems Symposium

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Abstract-Energy is a global concern and the electricity bills nowadays are leading to unprecedented costs. Electricity price is market-based and dynamic. In this paper, we investigate how to cut the electricity bills of commercial buildings in a dynamic power market. The building thermal systems (e.g., air-conditioning), which dominate electricity bills, has a special property of thermal storage, i.e., the energy will not immediately dissipate from thermal air/water. Intuitively, with storage, the energy can be "stored" in the thermal system, making it possible to purchase electricity in low price and use it at appropriate time. The building thermal supply and electricity purchasing surely depends on human activities that the building should support such as class and meeting schedules. To minimize electricity bills, we develop a holistic planning of electricity purchasing schedule with thermal storage management, and appropriate room assignment schedules for classes/meetings usage.The computing algorithms require inputs of physical modeling on energy consumption. We develop wireless sensing systems to collect fine-grained data which are used to assist the cross-disciplinary physical modeling. We conduct validation through real experiments. We formulate an optimization problem and show that it is NP-complete. Our primary focus is to minimize electricity bills, which matches the incentives of the commercial buildings. We show that this does not coincide with energy conservation. We further investigate the relationship of minimization of electricity bills and minimization of energy consumption. We develop algorithms for our problem and our evaluation shows that we can achieve a 40% cost reduction.

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A High-Fidelity Temperature Distribution Forecasting System for Data Centers

Cited by 45 publications

References 10 publications

Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

PTEC: A System for Predictive Thermal and Energy Control in Data Centers

Minimizing Building Electricity Costs in a Dynamic Power Market: Algorithms and Impact on Energy Conservation

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