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  • Virtual Machine Level Temperature Profiling

    Rights statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Virtual machine level temperature profiling and prediction in cloud datacenters

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Virtual machine level temperature profiling and prediction in cloud datacenters. / Wu, Zhaohui; Li, Xiang; Garraghan, Peter et al.
2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2016. p. 735-736.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Wu, Z, Li, X, Garraghan, P, Jiang, X, Ye, K & Zomaya, AY 2016, Virtual machine level temperature profiling and prediction in cloud datacenters. in 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS). IEEE, pp. 735-736. https://doi.org/10.1109/ICDCS.2016.62

APA

Wu, Z., Li, X., Garraghan, P., Jiang, X., Ye, K., & Zomaya, A. Y. (2016). Virtual machine level temperature profiling and prediction in cloud datacenters. In 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS) (pp. 735-736). IEEE. https://doi.org/10.1109/ICDCS.2016.62

Vancouver

Wu Z, Li X, Garraghan P, Jiang X, Ye K, Zomaya AY. Virtual machine level temperature profiling and prediction in cloud datacenters. In 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS). IEEE. 2016. p. 735-736 doi: 10.1109/ICDCS.2016.62

Author

Wu, Zhaohui ; Li, Xiang ; Garraghan, Peter et al. / Virtual machine level temperature profiling and prediction in cloud datacenters. 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2016. pp. 735-736

Bibtex

@inproceedings{b369ab2855764d7b8ef749fb7a5582fa,
title = "Virtual machine level temperature profiling and prediction in cloud datacenters",
abstract = "Temperature prediction can enhance datacenter thermal management towards minimizing cooling power draw. Traditional approaches achieve this through analyzing task-temperature profiles or resistor-capacitor circuit models to predict CPU temperature. However, they are unable to capture task resource heterogeneity within multi-tenant environments and make predictions under dynamic scenarios such as virtual machine migration, which is one of the main characteristics of Cloud computing. This paper proposes virtual machine level temperature prediction in Cloud datacenters. Experiments show that the mean squared error of stable CPU temperature prediction is within 1.10, and dynamic CPU temperature prediction can achieve 1.60 in most scenarios.",
keywords = "Temperature, Mathematical model, Predictive models, Calibration, Servers, Cloud computing, Temperature measurement",
author = "Zhaohui Wu and Xiang Li and Peter Garraghan and Xiaohong Jiang and Kejiang Ye and Zomaya, {Albert Y.}",
note = "{\textcopyright} 2016 IEEE. . Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
year = "2016",
month = aug,
day = "11",
doi = "10.1109/ICDCS.2016.62",
language = "English",
pages = "735--736",
booktitle = "2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Virtual machine level temperature profiling and prediction in cloud datacenters

AU - Wu, Zhaohui

AU - Li, Xiang

AU - Garraghan, Peter

AU - Jiang, Xiaohong

AU - Ye, Kejiang

AU - Zomaya, Albert Y.

N1 - © 2016 IEEE. . Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2016/8/11

Y1 - 2016/8/11

N2 - Temperature prediction can enhance datacenter thermal management towards minimizing cooling power draw. Traditional approaches achieve this through analyzing task-temperature profiles or resistor-capacitor circuit models to predict CPU temperature. However, they are unable to capture task resource heterogeneity within multi-tenant environments and make predictions under dynamic scenarios such as virtual machine migration, which is one of the main characteristics of Cloud computing. This paper proposes virtual machine level temperature prediction in Cloud datacenters. Experiments show that the mean squared error of stable CPU temperature prediction is within 1.10, and dynamic CPU temperature prediction can achieve 1.60 in most scenarios.

AB - Temperature prediction can enhance datacenter thermal management towards minimizing cooling power draw. Traditional approaches achieve this through analyzing task-temperature profiles or resistor-capacitor circuit models to predict CPU temperature. However, they are unable to capture task resource heterogeneity within multi-tenant environments and make predictions under dynamic scenarios such as virtual machine migration, which is one of the main characteristics of Cloud computing. This paper proposes virtual machine level temperature prediction in Cloud datacenters. Experiments show that the mean squared error of stable CPU temperature prediction is within 1.10, and dynamic CPU temperature prediction can achieve 1.60 in most scenarios.

KW - Temperature

KW - Mathematical model

KW - Predictive models

KW - Calibration

KW - Servers

KW - Cloud computing

KW - Temperature measurement

U2 - 10.1109/ICDCS.2016.62

DO - 10.1109/ICDCS.2016.62

M3 - Conference contribution/Paper

SP - 735

EP - 736

BT - 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS)

PB - IEEE

ER -