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Power Consumption Models for Multi-Tenant Server Infrastructures

Authors:

Matteo Ferroni,

Andrea Damiani,

Rolando Brondolin,

Juan A. Colmenares,

Steven Hofmeyr,

John D. Kubiatowicz,

Marco D. SantambrogioAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 14, Issue 4

Article No.: 38, Pages 1 - 22

https://doi.org/10.1145/3148965

Published: 14 November 2017 Publication History

Abstract

Multi-tenant virtualized infrastructures allow cloud providers to minimize costs through workload consolidation. One of the largest costs is power consumption, which is challenging to understand in heterogeneous environments. We propose a power modeling methodology that tackles this complexity using a divide-and-conquer approach. Our results outperform previous research work, achieving a relative error of 2% on average and under 4% in almost all cases. Models are portable across similar architectures, enabling predictions of power consumption before migrating a tenant to a different hardware platform. Moreover, we show the models allow us to evaluate colocations of tenants to reduce overall consumption.

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References

[1]

Apache Cassandra. Retrieved from http://cassandra.apache.org/.

[2]

Benchmark Suite for Apache Spark. Retrieved from https://github.com/SparkTC/spark-bench.

[3]

FFmpeg. Retrieved from https://ffmpeg.org.

[4]

MySQL Benchmark Tool. Retrieved from https://dev.mysql.com/downloads/benchmarks.html.

[5]

Redis. Retrieved from http://redis.io/topics/benchmarks.

[6]

Watts Up? Plug Load Meters. Retrieved from https://www.wattsupmeters.com/secure/products.php?pn=0.

[7]

2007. IOzone Filesystem Benchmark. Retrieved from http://www.iozone.org.

[8]

Bilge Acun, Phil Miller, and Laxmikant V. Kale. 2016. Variation among processors under turbo boost in HPC systems. In Proceedings of the 2016 International Conference on Supercomputing. Article 6, 12 pages.

Digital Library

[9]

Ishtiaq Ali and Natarajan Meghanathan. 2011. Virtual machines and networks-installation, performance study, advantages and virtualization options. arXiv:1105.0061 (2011).

[10]

Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy H. Katz, Andrew Konwinski, Gunho Lee, David A. Patterson, Ariel Rabkin, Ion Stoica et al. 2009. Above the Clouds: A Berkeley View of Cloud Computing. Technical Report.

[11]

Paul Barham, Boris Dragovic, Keir Fraser, Steven Hand, Tim Harris, Alex Ho, Rolf Neugebauer, Ian Pratt, and Andrew Warfield. 2003. Xen and the art of virtualization. In Proceedings of the 19th ACM Symposium on Operating Systems Principles. 164--177.

Digital Library

[12]

Frank Bellosa. 2000. The benefits of event-driven energy accounting in power-sensitive systems. In Proceedings of the 9th ACM SIGOPS European Workshop. Beyond the PC: New Challenges for the Operating System. 37--42.

Digital Library

[13]

Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, Albert Zomaya, and others. 2011. A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv. Comput. 82, 2 (2011), 47--111.

[14]

Ramon Bertran, Yolanda Becerra, David Carrera, Vicenç Beltran, Marc Gonzàlez, Xavier Martorell, Nacho Navarro, Jordi Torres, and Eduard Ayguadé. 2012. Energy accounting for shared virtualized environments under DVFS using PMC-based power models. Future Gen. Comput. Syst. 28, 2 (2012), 457--468.

Digital Library

[15]

W. Lloyd Bircher and Lizy K. John. 2007. Complete system power estimation: A trickle-down approach based on performance events. In Proceedings of the IEEE International Symposium on Performance Analysis of Systems 8 Software. 158--168.

[16]

William Lloyd Bircher, Madhavi Valluri, Jason Law, and Lizy K. John. 2005. Runtime identification of microprocessor energy saving opportunities. In Proceedings of the 2005 International Symposium on Low Power Electronics and Design. 275--280.

Digital Library

[17]

Manoranjan Dash and Huan Liu. 1997. Feature selection for classification. Intell. Data Anal. 1, 3 (1997), 131--156.

Digital Library

[18]

Christina Delimitrou and Christos Kozyrakis. 2013. Paragon: QoS-aware Scheduling for Heterogeneous Datacenters. In Proceedings of the 18th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 77--88.

Digital Library

[19]

Christina Delimitrou and Christos Kozyrakis. 2014. Quasar: Resource-efficient and QoS-aware cluster management. In Proceedings of the 19th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 127--144.

Digital Library

[20]

Gaurav Dhiman, Kresimir Mihic, and Tajana Rosing. 2010. A system for online power prediction in virtualized environments using gaussian mixture models. In Proceedings of the 47th ACM/IEEE Design Automation Conference. 807--812.

Digital Library

[21]

NASA Advanced Supercomputing Division. 2016. NAS Parallel Benchmarks (NPB). Retrieved from http://www.nas.nasa.gov/publications/npb.html.

[22]

Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. 2007. Power provisioning for a warehouse-sized computer. In ACM SIGARCH Computer Architecture News, Vol. 35. 13--23.

Digital Library

[23]

Matteo Ferroni, Juan A. Colmenares, Steven Hofmeyr, John D. Kubiatowicz, and Marco D. Santambrogio. 2016. Enabling power-awareness for the Xen hypervisor. In Proceedings of the 2016 Embedded Operating System Workshop.

[24]

Matteo Ferroni, Andrea Corna, Andrea Damiani, Rolando Brondolin, John D. Kubiatowicz, Donatella Sciuto, and Marco D. Santambrogio. Accepted to appear. MARC: A resource consumption modelling service for self-aware autonomous agents. Trans. Auton. Adapt. Syst. (Accepted to appear).

Digital Library

[25]

Chonglin Gu, Pengzhou Shi, Shuai Shi, Hejiao Huang, and Xiaohua Jia. 2015. A tree regression-based approach for VM power metering. IEEE Access 3 (2015), 610--621.

[26]

Gernot Heiser. 2008. The role of virtualization in embedded systems. In Proceedings of the 1st Workshop on Isolation and Integration in Embedded Systems. ACM, 11--16.

Digital Library

[27]

Canturk Isci and Margaret Martonosi. 2003. Runtime power monitoring in high-end processors: Methodology and empirical data. In Proceedings of the 36th Annual IEEE/ACM International Symposium on Microarchitecture. 93.

Digital Library

[28]

Aman Kansal, Feng Zhao, Jie Liu, Nupur Kothari, and Arka A. Bhattacharya. 2010. Virtual machine power metering and provisioning. In Proceedings of the 1st ACM Symposium on Cloud Computing. ACM, 39--50.

Digital Library

[29]

Igor Kononenko. 1994. Estimating attributes: Analysis and extensions of RELIEF. In Machine Learning: ECML-94. Springer, 171--182.

Digital Library

[30]

Rakesh Kumar and Shilpi Charu. 2015. Comparison between cloud computing, grid computing, cluster computing and virtualization. Int. J. Modern Comput. Sci. Appl. (IJMCSA) 3, 1 (January 2015).

[31]

K-J Lee and Kevin Skadron. 2005. Using performance counters for runtime temperature sensing in high-performance processors. In Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium.

Digital Library

[32]

Tao Li and Lizy Kurian John. 2003. Run-time modeling and estimation of operating system power consumption. In ACM SIGMETRICS Performance Evaluation Review, Vol. 31. ACM, 160--171.

Digital Library

[33]

David Lo, Liqun Cheng, Rama Govindaraju, Parthasarathy Ranganathan, and Christos Kozyrakis. 2015. Heracles: Improving resource efficiency at scale. In ACM SIGARCH Computer Architecture News, Vol. 43. 450--462.

Digital Library

[34]

Mark F. Mergen, Volkmar Uhlig, Orran Krieger, and Jimi Xenidis. 2006. Virtualization for high-performance computing. ACM SIGOPS Operat. Syst. Rev. 40, 2 (2006), 8--11.

Digital Library

[35]

Philip J. Mucci. 2007. Cachebench. Retrieved from http://www.weblearn.hs-bremen.de/risse/RST/WS06/x86_SUN/Sourcen/LLCBench/www/cachebench.html.

[36]

Murray Rosenblatt et al. 1956. Remarks on some nonparametric estimates of a density function. Ann. Math. Stat. 27, 3 (1956), 832--837.

[37]

Efraim Rotem, Alon Naveh, Avinash Ananthakrishnan, Eliezer Weissmann, and Doron Rajwan. 2012. Power-management architecture of the intel microarchitecture code-named sandy bridge. IEEE Micro 32, 2 (March 2012), 20--27.

Digital Library

[38]

Malte Schwarzkopf, Andy Konwinski, Michael Abd-El-Malek, and John Wilkes. 2013. Omega: Flexible, scalable schedulers for large compute clusters. In Proceedings of the 8th ACM European Conference on Computer Systems. 351--364.

Digital Library

[39]

Amir Ali Semnanian, Jeffrey Pham, Burkhard Englert, and Xiaolong Wu. 2011. Virtualization technology and its impact on computer hardware architecture. In Proceedings of the 8th International Conference on Information Technology: New Generations. 719--724.

Digital Library

[40]

Ibrahim Takouna, Wesam Dawoud, and Christoph Meinel. 2011. Accurate multicore processor power models for power-aware resource management. In Proceedings of the 2011 IEEE 9th International Conference on Dependable, Autonomic, and Secure Computing. 419--426.

Digital Library

[41]

Hailong Yang, Qi Zhao, Zhongzhi Luan, and Depei Qian. 2014. iMeter: An integrated VM power model based on performance profiling. Future Gen. Comput. Syst. 36 (2014), 267--286.

Cited By

Revathi NSumathi G(2021)Multistep temperature prediction for proactive thermal management on chip multiprocessorsThe Journal of Supercomputing10.1007/s11227-020-03611-577:8(8967-8994)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11227-020-03611-5
Brondolin RArnaboldi MSantambrogio M(2020)Power consumption management under a low-level performance constraint in the Xen hypervisorACM SIGBED Review10.1145/3412821.341282817:1(42-48)Online publication date: 27-Jul-2020
https://dl.acm.org/doi/10.1145/3412821.3412828
Imes CHofmeyr SHoffmann H(2018)Energy-efficient Application Resource Scheduling using Machine Learning ClassifiersProceedings of the 47th International Conference on Parallel Processing10.1145/3225058.3225088(1-11)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3225058.3225088
Show More Cited By

Index Terms

Power Consumption Models for Multi-Tenant Server Infrastructures

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Published In

cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 14, Issue 4

December 2017

600 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/3154814

Editor:
Koen De Bosschere
Ghent University

Issue’s Table of Contents

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2017

Accepted: 01 September 2017

Revised: 01 August 2017

Received: 01 March 2017

Published in TACO Volume 14, Issue 4

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Cited By

Revathi NSumathi G(2021)Multistep temperature prediction for proactive thermal management on chip multiprocessorsThe Journal of Supercomputing10.1007/s11227-020-03611-577:8(8967-8994)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11227-020-03611-5
Brondolin RArnaboldi MSantambrogio M(2020)Power consumption management under a low-level performance constraint in the Xen hypervisorACM SIGBED Review10.1145/3412821.341282817:1(42-48)Online publication date: 27-Jul-2020
https://dl.acm.org/doi/10.1145/3412821.3412828
Imes CHofmeyr SHoffmann H(2018)Energy-efficient Application Resource Scheduling using Machine Learning ClassifiersProceedings of the 47th International Conference on Parallel Processing10.1145/3225058.3225088(1-11)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3225058.3225088
Brondolin RArnaboldi MSardelli TNotargiacomo SSantambrogio M(2018)Energy Efficiency for Autonomic Scalable Systems: Research Objectives and Preliminary Results2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)10.1109/RTSI.2018.8548400(1-5)Online publication date: Sep-2018
https://doi.org/10.1109/RTSI.2018.8548400
Brondolin RSardelli TSantambrogio M(2018)DEEP-Mon: Dynamic and Energy Efficient Power Monitoring for Container-Based Infrastructures2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2018.00110(676-684)Online publication date: May-2018
https://doi.org/10.1109/IPDPSW.2018.00110

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