Virtual machine level temperature profiling and prediction in cloud datacenters

Wu, Zhaohui and Li, Xiang and Garraghan, Peter and Jiang, Xiaohong and Ye, Kejiang and Zomaya, Albert Y. (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. ISBN 9781509014828

[img]
Preview
PDF (Virtual Machine Level Temperature Profiling)
Virtual_Machine_Level_Temperature_Profiling.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (269kB)

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.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© 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.
Subjects:
ID Code:
82344
Deposited By:
Deposited On:
21 Oct 2016 14:22
Refereed?:
Yes
Published?:
Published
Last Modified:
08 Apr 2020 00:40