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Virtual machine power metering and provisioning
Abstract
Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's real-world applications such as Windows Live Messenger. The results show that not only does VM power metering allows virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization.
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Cited by
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Dayarathna, M., Wen, Y., & Fan, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2481183 · Cited by 1,165
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Shojafar, M., Cordeschi, N., & Baccarelli, E. (2019) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2016.2551747 · Cited by 339
Artificial neural network-based virtual machine allocation in cloud computing
Shalu, & Singh, D. (2021) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.1080/09720529.2021.1878626 · Cited by 28
Rapid and accurate energy models through calibration with IPMI and RAPL
Kavanagh, R., & Djemame, K. (2019) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.5124 · Cited by 23
On the Carbon Footprint Optimization in an InterCloud Environment
Justafort, V. D., Beaubrun, R., & Pierre, S. (2018) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2015.2511768 · Cited by 10
Metadata
- Authors
- Kansal, A., Zhao, F., Liu, J., Kothari, N., & Bhattacharya, A. A.
- Venue
- Proceedings of the 1st ACM symposium on Cloud computing
- Publisher
- ACM
- Published
- 2010-06-10
- Pages
- 39-50
- OpenAlex
- W2109546606
- Cited by
- 556
- References
- 0
- FWCI
- 39.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kansal, A., Zhao, F., Liu, J., Kothari, N., & Bhattacharya, A. A. (2010). Virtual machine power metering and provisioning. Proceedings of the 1st ACM symposium on Cloud computing. https://doi.org/10.1145/1807128.1807136
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