Home > Research > Publications & Outputs > Virtual machine level temperature profiling and...

Electronic data

  • 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.

    Accepted author manuscript, 263 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

Virtual machine level temperature profiling and prediction in cloud datacenters

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
Close
NullPointerException

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.

Bibliographic note

© 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.