A transient model for data center thermal prediction

Jonas, Michael; Gilbert, Rose Robin; Ferguson, Joshua; Varsamopoulos, Georgios; Gupta, Sandeep

doi:10.1109/igcc.2012.6322262

Cited by 25 publications

(11 citation statements)

References 13 publications

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“…Workload scheduling would need to account for these issue scenarios and select and schedule the next set load to servers which would remediate this issue. Authors in [32] proposed a fast thermal modeling technique that captures the transient behavior necessary to improve thermal prediction capability using transient thermal model. The model predicts three aspects of thermal behavior: division (spatial distribution): how the heat produced by each computing server is split and circulated into each other server or to each chiller; temporal distribution: how the heat portion of one server to another is distributed over time, which also captures the hysteresis: how long the heat takes to travel from one unit to another.…”

Section: Integrated Compute and Cooling Power Consumption Considerationsmentioning

confidence: 99%

Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds

Kumar¹,

Raghunathan

2016

Journal of Computer and System Sciences

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Please cite this article in press as: M.R. Velayudhan Kumar, S. Raghunathan, Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in Infrastructure clouds, J. Comput. Syst. Sci. (2015), http://dx. Highlights• Metrics to classify servers in a heterogeneous cloud environment based on server's power consumption and performance characteristics.• Adaptive heterogeneous and thermal aware heuristic approaches for the problem of energy and performance efficient dynamic consolidation of VMs. • Accounted for server processor sleep states and state transition time latencies in a heterogeneous cloud environment.• Hybrid heuristics approach, which works with both homogeneous and heterogeneous server cloud environment and improves cloud datacenters' energy efficiency. • Extensive simulation-based evaluation and analysis of the proposed algorithms. AbstractHolistic da specific us consumptio operationa improve en state, state proposed h

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Section: Integrated Compute and Cooling Power Consumption Considerationsmentioning

confidence: 99%

Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds

Kumar¹,

Raghunathan

2016

Journal of Computer and System Sciences

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“…This does not hold in many data centers where the cooling equipment is placed in the rows of the racks [Niemann 2006;Bell 2012] or near the racks [Rasmussen 2011], which generates significant side-to-side airflow. A recent work [Jonas et al 2012] predicts the temperature as a weighted average of contributing temperatures of each heat source, where the model coefficients are determined via offline CFD simulations. Thus, this approach does not leverage the real temperature measurements in a data center to ensure prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Sensor System for High-Fidelity Temperature Distribution Forecasting in Data Centers

Chen

Tan

et al. 2014

ACM Trans. Sen. Netw.

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Data centers have become a critical computing infrastructure in the era of cloud computing. Temperature monitoring and forecasting are essential for preventing server shutdowns because of overheating and improving a data center's energy efficiency. This article presents a novel cyber-physical approach for temperature forecasting in data centers, one that integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing, and real-time data-driven prediction. To ensure forecasting fidelity, we leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and calibrate it using sensor feedback. Both simulated temperature distribution and sensor measurements are then used to train a real-time prediction algorithm. As a result, our approach reduces not only the computational complexity of online temperature modeling and prediction, but also the number of deployed sensors, which enables a portable, noninvasive thermal monitoring solution that does not rely on the infrastructure of a monitored data center. We extensively evaluated the proposed system on a rack of 15 servers and a testbed of five racks and 229 servers in a small-scale production data center. Our results show that our system can predict the temperature evolution of servers with highly dynamic workloads at an average error of 0.52 • C, within a duration up to 10 minutes. Moreover, our approach can reduce the required number of sensors by 67% while maintaining desirable prediction fidelity.

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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: 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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A transient model for data center thermal prediction

Cited by 25 publications

References 13 publications

Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds

Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds

A Sensor System for High-Fidelity Temperature Distribution Forecasting in Data Centers

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

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