2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS) (2016)
June 27, 2016 to June 30, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDCS.2016.62
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.
Temperature, Mathematical model, Predictive models, Calibration, Servers, Cloud computing, Temperature measurement
Z. Wu, X. Li, P. Garraghan, X. Jiang, K. Ye and A. Y. Zomaya, "Virtual Machine Level Temperature Profiling and Prediction in Cloud Datacenters," 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), Nara, Japan, 2016, pp. 735-736.