Leila Ismail
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Data centers are large-scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT), and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware-level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power-measurement techniques, and error-calculation formulas on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power-measurement techniques, and error formulas, with the aim of achieving an objective comparison. We use different server architectures to assess the impact of heterogeneity on the models’ comparison. The performance analysis of these models is elaborated in the article.
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- Wikipedia. Branch (computer science) - Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Branch_(computer_science).Google Scholar
- Wikipedia. CPU cache - Wikipedia. Retrieved from https://en.wikipedia.org/wiki/CPU_cache.Google Scholar
- Wikipedia. Interrupt - Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Interrupt.Google Scholar
- Wikipedia. Advanced Video Coding - Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Advanced_Video_Coding.Google Scholar
- Ubuntu. Stress. Retrieved form http://manpages.ubuntu.com/manpages/bionic/man1/stress-ng.1.html.Google Scholar
- Florian octo Forster. Collectd -- The system statistics collection daemon. Retrieved from https://collectd.org/.Google Scholar
- iPerf forum. iPerf. Retrieved from https://iperf.fr/iperf-download.php.Google Scholar
- MPlayer team. MPlayer - The Movie Player. Retrieved from http://www.mplayerhq.hu/design7/news.html.Google Scholar
- Ismail Alan, Engin Arslan, and Tevfik Kosar. 2014. Energy-aware data transfer tuning. In Proceedings of the 14th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid’14). 626--634. DOI:https://doi.org/10.1109/CCGrid.2014.117Google ScholarDigital Library
- Kopytov Alexey. 2019. GitHub - akopytov/sysbench: Scriptable database and system performance benchmark. Retrieved from https://github.com/akopytov/sysbench.Google Scholar
- Ehsan Arianyan, Hassan Taheri, and Saeed Sharifian. 2015. Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers. Comput. Electric. Eng. 47 (2015), 222--240. DOI:https://doi.org/10.1016/j.compeleceng.2015.05.006Google ScholarDigital Library
- Zahra Bagheri and Kamran Zamanifar. 2014. Enhancing energy efficiency in resource allocation for real-time cloud services. In Proceedings of the 7th International Symposium on Telecommunications, (IST’14). 701--706. DOI:https://doi.org/10.1109/ISTEL.2014.7000793Google ScholarCross Ref
- Jenny A. Baglivo. 2005. Mathematica Laboratories for Mathematical Statistics: Emphasizing Simulation and Computer Intensive Methods. Vol. 14. Siam.Google Scholar
- Anton Beloglazov, Jemal Abawajy, and Rajkumar Buyya. 2012. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Fut. Gen. Comput. Syst. 28, 5 (2012), 755--768. DOI:https://doi.org/10.1016/j.future.2011.04.017Google ScholarDigital Library
- Anton Beloglazov and Rajkumar Buyya. 2012. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exper. 24, 13 (2012), 1397--1420. DOI:https://doi.org/10.1002/cpe.1867 arxiv:1006.0308.Google ScholarDigital Library
- J. L. Berral, Í. Goiri, R. Nou, F. Julià, J. Guitart, R. Gavaldà, and J. Torres. 2010. Towards energy-aware scheduling in data centers using machine learning. In Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking, Vol. 2. 215--224. DOI:https://doi.org/10.1145/1791314.1791349Google Scholar
- J. L. Berral, R. Gavalda, and J. Torres. 2011. Adaptive scheduling on power-aware managed data-centers using machine learning. In Proceedings of the IEEE/ACM 12th International Conference on Grid Computing. 66--73. DOI:https://doi.org/10.1109/Grid.2011.18Google Scholar
- Christian Bienia. 2011. Benchmarking Modern Multiprocessors. Ph.D. Dissertation. Department of Computer Science, Princeton University.Google ScholarDigital Library
- William Lloyd Bircher and Lizy K. John. 2011. Complete system power estimation using processor performance events. IEEE Trans. Comput. 61, 4 (2011), 563--577.Google ScholarDigital Library
- Ata E. Husain Bohra and Vipin Chaudhary. 2010. VMeter power modelling for virtualized clouds. In Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum (IPDPSW'10), 19-23 April, 2010, Atlanta, GA, USA. IEEE. DOI:https://doi.org/10.1109/IPDPSW.2010.5470907Google Scholar
- Pat Bohrer, Elmootazbellah N. Elnozahy, Tom Keller, Michael Kistler, Charles Lefurgy, Chandler McDowell, and Ram Rajamony. 2002. The Case for Power Management in Web Servers. In Power Aware Computing. Series in Computer Science, R. Graybill and R. Melhem (Eds.). Springer, Boston, MA, 261--289. DOI:https://doi.org/10.1007/978-1-4757-6217-4_14Google Scholar
- Rajkumar Buyya, Anton Beloglazov, and Jemal Abawajy. 2010. Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges. In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications. 1--12. DOI:https://doi.org/10.1002/cpe.1867 arxiv:1006.0308.Google Scholar
- Rajkumar Buyya and Amir Vahid Dastjerdi. 2016. Internet of Things: Principles and Paradigms (1st ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA.Google Scholar
- Rajkumar Buyya, Christian Vecchiola, and S. Thamarai Selvi. 2013. Mastering Cloud Computing: Foundations and Applications Programming, 1st edition. 469 pages. DOI:https://doi.org/10.1016/B978-0-12-411454-8.00001-2Google Scholar
- Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose, and Rajkumar Buyya. 2011. CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exper. 41, 1 (Jan. 2011), 23--50. DOI:https://doi.org/10.1002/spe.995Google ScholarDigital Library
- Mauro Canuto, Raimon Bosch, Mario Macias, and Jordi Guitart. 2016. A methodology for full-system power modeling in heterogeneous data centers. In Proceedings of the 9th International Conference on Utility and Cloud Computing. ACM, 20--29.Google ScholarDigital Library
- Howard Cheung, Shengwei Wang, Chaoqun Zhuang, and Jiefan Gu. 2018. A simplified power consumption model of information technology (IT) equipment in data centers for energy system real-time dynamic simulation. Appl. Energy 222 (2018), 329--342. DOI:https://doi.org/10.1016/j.apenergy.2018.03.138Google ScholarCross Ref
- Mohammed Rashid Chowdhury, Mohammad Raihan Mahmud, and Rashedur M. Rahman. 2015. Implementation and performance analysis of various VM placement strategies in CloudSim. J. Cloud Comput. 4, 1 (2015), 1--21. DOI:https://doi.org/10.1186/s13677-015-0045-5Google ScholarDigital Library
- Standard Performance Evaluation Corporation. 2019. Dell Inc. PowerEdge R7425 (AMD EPYC 7601 2.20 GHz). Retrieved from https://www.spec.org/power_ssj2008/results/res2019q1/power_ssj2008-20190212-00876.html.Google Scholar
- Standard Performance Evaluation Corporation. 2019. Lenovo Global Technology ThinkSystem SR150. Retrieved from https://www.spec.org/power_ssj2008/results/res2019q1/power_ssj2008-20181225-00874.html.Google Scholar
- Standard Performance Evaluation Corporation. 2019. SPECpower. Retrieved from https://www.spec.org/power_ssj2008/results/.Google Scholar
- Natural Resources Defense Council. 2015. America’s Data Centers Consuming and Wasting Growing Amounts of Energy. Retrieved from https://www.nrdc.org/resources/americas-data-centers-consuming-and-wasting-growing-amounts-energy.Google Scholar
- Leandro Fontoura Cupertino, Georges Da Costa, and Jean-Marc Pierson. 2015. Towards a generic power estimator. Comput. Sci.-Res. Dev. 30, 2 (2015), 145--153.Google ScholarDigital Library
- Georges Da Costa and Helmut Hlavacs. 2010. Methodology of measurement for energy consumption of applications. In Proceedings of the IEEE/ACM International Workshop on Grid Computing. 290--297. DOI:https://doi.org/10.1109/GRID.2010.5697987Google ScholarCross Ref
- Xiangming Dai, Jason Min Wang, and Brahim Bensaou. 2016. Energy-efficient virtual machines scheduling in multi-tenant data centers. IEEE Trans. Cloud Comput. 4, 2 (2016), 210--221. DOI:https://doi.org/10.1109/TCC.2015.2481401Google ScholarCross Ref
- John D. Davis, Suzanne Rivoire, Moises Goldszmidt, and Ehsan K. Ardestani. 2012. CHAOS: Composable highly accurate OS-based power models. In Proceedings of the IEEE International Symposium on Workload Characterization (IISWC’12). 153--163. DOI:https://doi.org/10.1109/IISWC.2012.6402920Google Scholar
- M. Dayarathna, Y. Wen, and R. Fan. 2016. Data center energy consumption modeling: A survey. IEEE Commun. Surv. Tutor. 18, 1 (2016), 732--794. DOI:https://doi.org/10.1109/COMST.2015.2481183Google ScholarDigital Library
- 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 Design Automation Conference (DAC'10), 13-18 June, 2010, Anaheim California. Association for Computing Machinery, New York, NY, USA, 807--812. DOI:https://doi.org/10.1145/1837274.1837478Google ScholarDigital Library
- Dimitris Economou, Suzanne Rivoire, Christos Kozyrakis, and Partha Ranganathan. 2006. Full-system power analysis and modeling for server environments. In Proceedings of the Workshop on Modeling, Benchmarking and Simulation (MoBS’06). 3 (2006), 807--812.Google Scholar
- Elmootazbellah N. Elnozahy, Michael Kistler, and Ramakrishnan Rajamony. 2003. Energy-efficient server clusters. Proceedings of the 2nd International Workshop on Power Aware Computing Systems. 179--197. DOI:https://doi.org/10.1017/CBO9781107415324.004Google ScholarCross Ref
- Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. 2007. Power provisioning for a warehouse-sized computer. ACM SIGARCH Computer Archit. News 35, 2 (2007), 13. DOI:https://doi.org/10.1145/1273440.1250665Google ScholarDigital Library
- Fahimeh Farahnakian, Pasi Liljeberg, and Juha Plosila. 2014. Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In Proceedings of the 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. 500--507. DOI:https://doi.org/10.1109/PDP.2014.109Google ScholarDigital Library
- Center for Machine Learning and Intelligent Systems. 2019. UCI Machine Learning Repository. Retrieved from https://archive.ics.uci.edu/ml/index.php.Google Scholar
- The R foundation. 2019. R: The R Project for Statistical Computing. Retrieved from https://www.r-project.org/.Google Scholar
- Carlucci Gaetano. 2018. CPULoadGenerator. Retrieved from https://github.com/GaetanoCarlucci/CPULoadGenerator.Google Scholar
- Gentoo Foundation, Inc. 2018. Sysbench. Retrieved from https://wiki.gentoo.org/wiki/Sysbench.Google Scholar
- Daniel Gmach, Jerry Rolia, Ludmila Cherkasova, and Alfons Kemper. 2009. Resource pool management: Reactive versus proactive or let’s be friends. Comput. Netw. 53, 17 (2009), 2905--2922. DOI:https://doi.org/10.1016/j.comnet.2009.08.011Google ScholarDigital Library
- Chen Gong, He Wenbo, Liu Jie, Nath Suman, Rigas Leonidas, Xiao Lin, and Zhao Feng. 2008. Energy-aware server provisioning and load dispatching for connection-intensive internet services. In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI’08). 337--350. DOI:https://doi.org/10.1109/INFCOM.2012.6195719Google Scholar
- Albert Greenberg, James Hamilton, David A. Maltz, and Parveen Patel. 2008. The cost of a cloud: Research problems in data center networks. ACM SIGCOMM Computer Commun. Rev. 39, 1 (2008), 68--73.Google ScholarDigital Library
- Steve Greenberg, Evan Mills, Bill Tschudi, and Lawrence Berkeley. 2006. Best practices for data centers: Lessons learned from benchmarking 22 data centers. In Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings. 76--87. DOI:https://doi.org/10.1016/j.energy.2012.04.037Google Scholar
- Brendan Gregg. 2008. Linux perf Examples. Retrieved from http://www.brendangregg.com/perf.html.Google Scholar
- The Green Grid. 2011. The ROI of Cooling System Energy Efficiency Upgrades - Case Study. Technical Report. The Green Grid, 1--42 pages.Google Scholar
- Marco Guazzone, Cosimo Anglano, and Massimo Canonico. 2012. Exploiting VM migration for the automated power and performance management of green cloud computing systems. In Proceedings of the Energy Efficient Data Centers Conference (E2DC’12). 81--92.Google ScholarDigital Library
- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. 2009. The WEKA data mining software: An update. ACM SIGKDD Explor. Newslett. 11, 1 (2009), 10--18.Google ScholarDigital Library
- Sang Woo Ham, Min Hwi Kim, Byung Nam Choi, and Jae Weon Jeong. 2015. Simplified server model to simulate data center cooling energy consumption. Energy Build. 86 (2015), 328--339. DOI:https://doi.org/10.1016/j.enbuild.2014.10.058Google ScholarCross Ref
- Guangjie Han, Wenhui Que, Gangyong Jia, and Lei Shu. 2016. An efficient virtual machine consolidation scheme for multimedia cloud computing. Sensors (Switz.) 16, 2 (2016), 1--18. DOI:https://doi.org/10.3390/s16020246Google ScholarCross Ref
- Taliver Heath, Ana Paula Centeno, Pradeep George, Luiz Ramos, Yogesh Jaluria, and Ricardo Bianchini. 2006. Mercury and freon: Temperature emulation and management for server systems. In Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems. 106--116. DOI:https://doi.org/10.1145/1168857.1168872Google ScholarDigital Library
- Li Hongyou, Wang Jiangyong, Peng Jian, Wang Junfeng, and Liu Tang. 2013. Energy-aware scheduling scheme using workload-aware consolidation technique in cloud data centres. China Communica. 10, 12 (2013), 114--124. DOI:https://doi.org/10.1109/CC.2013.6723884Google ScholarCross Ref
- T. Horvath and K. Skadron. 2008. Multi-mode energy management for multi-tier server clusters. In Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT’08). 270--279.Google Scholar
- Intel IT Center. 2012. Big Data Analytics. Technical Report. 27 pages. DOI:https://doi.org/10.1007/978-3-319-10665-6Google Scholar
- Stefan Janacek, Kiril Schröder, Gunnar Schomaker, Wolfgang Nebel, Marco Rüschen, and Günter Pistoor. 2012. Modeling and approaching a cost transparent, specific data center power consumption. In Proceedings of the International Conference on Energy Aware Computing (ICEAC’12). DOI:https://doi.org/10.1109/ICEAC.2012.6471012Google ScholarCross Ref
- Mateusz Jarus, Ariel Oleksiak, Tomasz Piontek, and J. Węglarz. 2013. Runtime power usage estimation of HPC servers for various classes of real-life applications. Fut. Gen. Comput. Syst. 36 (2013), 299--310.Google ScholarCross Ref
- Yichao Jin, Yonggang Wen, Qinghua Chen, and Zuqing Zhu. 2013. An empirical investigation of the impact of server virtualization on energy efficiency for green data center. Comput. J. 56, 8 (2013), 977--990. DOI:https://doi.org/10.1093/comjnl/bxt017Google ScholarCross Ref
- 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 (SoCC’10). 39. DOI:https://doi.org/10.1145/1807128.1807136Google Scholar
- He Kejing, Li Zhibo, Deng Dongyan, and Chen Yanhua. 2017. Energy-efficient framework for virtual machine consolidation in cloud data centers. China Communications 14, 10 (2017), 1--13. DOI:https://doi.org/10.1109/CC.2017.8107643Google Scholar
- Daniel C. Kilper, Gary Atkinson, Steven K. Korotky, Suresh Goyal, Peter Vetter, Dusan Suvakovic, and Oliver Blume. 2011. Power trends in communication networks. IEEE J. Select. Topics Quant. Electron. 17, 2 (2011), 275--284. DOI:https://doi.org/10.1109/JSTQE.2010.2074187Google ScholarCross Ref
- Ricardo Koller, Akshat Verma, and Anidya Neogi. 2010. WattApp: An application aware power meter for shared data centers. In Proceedings of the International Conference on Autonomic Computing. 10. DOI:https://doi.org/10.1145/1809049.1809055Google ScholarDigital Library
- Alexey Kopytov. 2006. SysBench manual. Retrieved from http://imysql.com/wp-content/uploads/2014/10/sysbench-manual.pdf.Google Scholar
- N. Kord and H. Haghighi. 2013. An energy-efficient approach for virtual machine placement in cloud based data centers. In Proceedings of the 5th Conference on Information and Knowledge Technology (IKT’13). 44--49. DOI:https://doi.org/10.1109/IKT.2013.6620036Google ScholarCross Ref
- Rainer Kress. 1998. Numerical Analysis. Springer, New York, NY.Google Scholar
- Etienne Le Sueur and Gernot Heiser. 2010. Dynamic voltage and frequency scaling: The laws of diminishing returns. In Proceedings of the International Conference on Power Aware Computing and Systems. 1--8.Google Scholar
- Young Choon Lee and Albert Y. Zomaya. 2012. Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60, 2 (2012), 268--280. DOI:https://doi.org/10.1007/s11227-010-0421-3Google ScholarDigital Library
- Tao Li and Lizy Kurian John. 2003. Run-time modeling and estimation of operating system power consumption. ACM SIGMETRICS Perf. Eval. Rev. 31 (2003), 160. DOI:https://doi.org/10.1145/885651.781048Google ScholarDigital Library
- Yuanlong Li, Han Hu, Yonggang Wen, and Jun Zhang. 2016. Learning-based power prediction for data centre operations via deep neural networks. In Proceedings of the 5th International Workshop on Energy Efficient Data Centres (E2DC’16). 1--10. DOI:https://doi.org/10.1145/2940679.2940685Google ScholarDigital Library
- Yanfei Li, Ying Wang, Bo Yin, and Lu Guan. 2012. An online power metering model for cloud environment. In Proceedings of the IEEE 11th International Symposium on Network Computing and Applications (NCA’12). 175--180. DOI:https://doi.org/10.1109/NCA.2012.10Google ScholarDigital Library
- Chia Hung Lien, Ying Wen Bai, and Ming Bo Lin. 2007. Estimation by software for the power consumption of streaming-media servers. IEEE Trans. Instrument. Measur. 56, 5 (2007), 1859--1870. DOI:https://doi.org/10.1109/TIM.2007.904554Google ScholarCross Ref
- Weiwei Lin, Wentai Wu, Haoyu Wang, James Z. Wang, and Ching-Hsien Hsu. 2018. Experimental and quantitative analysis of server power model for cloud data centers. Fut. Gen. Comput. Syst. 86 (2018), 940--950. DOI:https://doi.org/10.1016/j.future.2016.11.034Google ScholarDigital Library
- Haikun Liu, Cheng-Zhong Xu, Hai Jin, Jiayu Gong, and Xiaofei Liao. 2011. Performance and energy modeling for live migration of virtual machines. In Proceedings of the 20th International Symposium on High Performance Distributed Computing (HPDC’11). 171. DOI:https://doi.org/10.1145/1996130.1996154Google ScholarDigital Library
- Liang Luo, Wenjun Wu, W. T. Tsai, Dichen Di, and Fei Zhang. 2013. Simulation of power consumption of cloud data centers. Simul. Model. Pract. Theor. 39 (2013), 152--171. DOI:https://doi.org/10.1016/j.simpat.2013.08.004Google ScholarCross Ref
- Theodosios Makris. 2017. Measuring and Analyzing Energy Consumption of the Data Center. Ph.D. Dissertation. School of Electrical Engineering, Aalto University.Google Scholar
- Vimal Mathew, Ramesh K. Sitaraman, and Prashant Shenoy. 2012. Energy-aware load balancing in content delivery networks. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’12). 954--962. DOI:https://doi.org/10.1109/INFCOM.2012.6195846Google ScholarCross Ref
- John C. McCullough, Yuvraj Agarwal, Jaideep Chandrashekar, Sathyanarayan Kuppuswamy, Alex C. Snoeren, and Rajesh K. Gupta. 2011. Evaluating the effectiveness of model-based power characterization. In Proceedings of the USENIX Annual Technical Conference, Vol. 20.Google Scholar
- David Meisner and Thomas F. Wenisch. 2010. Peak power modeling for data center servers with switched-mode power supplies. In Proceedings of the 16th ACM/IEEE International Symposium on Low Power Electronics and Design. 319--324. DOI:https://doi.org/10.1145/1840845.1840911Google Scholar
- Peter Mell and Timothy Grance. 2011. The NIST definition of cloud computing recommendations of the National Institute of Standards and Technology. Nist Spec. Pub. 145 (2011), 7. DOI:https://doi.org/10.1136/emj.2010.096966 arxiv:2305-0543.Google Scholar
- Bryan Mills, Taieb Znati, Rami Melhem, Kurt B. Ferreira, and Ryan E. Grant. 2014. Energy consumption of resilience mechanisms in large scale systems. In Proceedings of the 22nd Euromicro International Conference on Parallel, Distributed, and Network-based Processing (PDP’14). 528--535. DOI:https://doi.org/10.1109/PDP.2014.111Google Scholar
- Christoph Möbius, Waltenegus Dargie, and Alexander Schill. 2013. Power consumption estimation models for processors, virtual machines, and servers. IEEE Trans. Parallel Distrib. Syst. 25, 6 (2013), 1600--1614.Google ScholarDigital Library
- Mathijs Mortimer. 2018. iperf3 documentation. (2018). https://buildmedia.readthedocs.org/media/pdf/iperf3-python/latest/iperf3-python.pdf.Google Scholar
- Hitoshi Nagasaka, Naoya Maruyama, Akira Nukada, Toshio Endo, and Satoshi Matsuoka. 2010. Statistical power modeling of GPU kernels using performance counters. In Proceedings of the International Conference on Green Computing, 15-18 August, 2010, Chicago, IL, USA. IEEE. DOI:https://doi.org/10.1109/GREENCOMP.2010.5598315Google ScholarDigital Library
- Riddhi Patel, Hitul Patel, and Sanjay Patel. 2015. Quality of service based efficient resource. Int. J. Technol. Res. Eng. 2, 9 (2015), 2008--2013.Google Scholar
- Massoud Pedram and Inkwon Hwang. 2010. Power and performance modeling in a virtualized server system. In Proceedings of the International Conference on Parallel Processing Workshops. 520--526. DOI:https://doi.org/10.1109/ICPPW.2010.76Google ScholarDigital Library
- Steven Pelley, David Meisner, Thomas F. Wenisch, and James W. VanGilder. 2009. Understanding and abstracting total data center power. In Proceedings of the Workshop on Energy-efficient Design, Vol. 11.Google Scholar
- Asfandyar Qureshi, Rick Weber, Hari Balakrishnan, John Guttag, and Bruce Maggs. 2009. Cutting the electric bill for internet-scale systems. ACM SIGCOMM Comput. Commun. Rev. 39, 4 (2009), 123. DOI:https://doi.org/10.1145/1594977.1592584Google ScholarDigital Library
- Ramya Raghavendra, Parthasarathy Ranganathan, Vanish Talwar, Zhikui Wang, and Xiaoyun Zhu. 2008. No “power” struggle: Coordinated multi-level power management for the data center. ACM SIGPLAN Notices 43, 3 (2008), 48--59 pages. DOI:https://doi.org/10.1145/1346281.1346289Google ScholarDigital Library
- A. A. Rahmanian, G. H. Dastghaibyfard, and H. Tahayori. 2017. Penalty-aware and cost-efficient resource management in cloud data centers. Int. J. Commun. Syst. 30, 8 (2017), e3179. DOI:https://doi.org/10.1002/dac.3179Google ScholarCross Ref
- Patrick Raycroft, Ryan Jansen, Mateusz Jarus, and Paul R. Brenner. 2014. Performance bounded energy efficient virtual machine allocation in the global cloud. Sustain. Comput. Inf. Syst. 4, 1 (2014), 1--9. DOI:https://doi.org/10.1016/j.suscom.2013.07.001Google ScholarCross Ref
- Douglas Reynolds. 2015. Gaussian mixture models. Encycl. Biomet. 741 (2015), 827--832.Google ScholarCross Ref
- Suzanne Rivoire, Parthasarathy Ranganathan, and Christos Kozyrakis. 2008. A comparison of high-level full-system power models. In Proceedings of the Conference on Power Aware Computing and Systems (HotPower’08). 1--5.Google ScholarDigital Library
- Suzanne Marion Rivoire. 2008. Models and Metrics for Energy-Efficient Computer Systems. Ph.D. Dissertation. Stanford University.Google ScholarDigital Library
- Osman Sarood, Akhil Langer, Abhishek Gupta, and Laxmikant Kale. 2014. Maximizing throughput of overprovisioned HPC data centers under a strict power budget. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC ’15). 807--818. DOI:https://doi.org/10.1109/SC.2014.71Google ScholarDigital Library
- Bernhard Scholkopf and Alexander J. Smola. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press.Google ScholarDigital Library
- Neeraj Sharma and Ram Mohana Guddeti. 2016. Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans. Serv. Comput. 1374, c (2016), 1--1. DOI:https://doi.org/10.1109/TSC.2016.2596289Google Scholar
- Donghwa Shin, Jihun Kim, Naehyuck Chang, Jinhang Choi, Sung Woo Chung, and Eui-Young Chung. 2009. Energy-optimal dynamic thermal management for green computing. In Proceedings of the International Conference on Computer-aided Design (ICCAD’09). 652. DOI:https://doi.org/10.1145/1687399.1687520Google ScholarDigital Library
- Richa Sinha, Nidhi Purohit, and Hiteshi Diwanji. 2011. Power aware live migration for data centers in cloud using dynamic threshold. Int. J. Comput. Technol. Applic. 2, 6 (2011), 2041--2046. DOI:https://doi.org/10.1.1.658.4169Google Scholar
- James William Smith, Ali Khajeh-Hosseini, Jonathan Stuart Ward, and Ian Sommerville. 2012. CloudMonitor: Profiling power usage. In Proceedings of the IEEE 5th International Conference on Cloud Computing (CLOUD’12). 3--4.Google ScholarDigital Library
- Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 151--161.Google ScholarDigital Library
- Stanford. 2007. Regularization: Ridge Regression and the LASSO The Bias-Variance Tradeoff. Technical Report. Retrieved from http://statweb.stanford.edu/ owen/courses/305/Rudyregularization.pdf.Google Scholar
- Gang Sun, Vishal Anand, Dan Liao, Chuan Lu, Xiaoning Zhang, and Ning-Hai Bao. 2015. Power-efficient provisioning for online virtual network requests in cloud-based data centers. IEEE Syst. J. 9 (2015), 427--441.Google ScholarCross Ref
- Cheng Jen Tang and Miau Ru Dai. 2011. Dynamic computing resource adjustment for enhancing energy efficiency of cloud service data centers. In Proceedings of the IEEE/SICE International Symposium on System Integration (SII ’11). 1159--1164. DOI:https://doi.org/10.1109/SII.2011.6147613Google ScholarCross Ref
- M. Tang and S. Pan. 2015. A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Proc. Lett. 41, 2 (2015), 211--221. DOI:https://doi.org/10.1007/s11063-014-9339-8Google ScholarDigital Library
- Tektronix. 2003. Digital Storage Oscilloscope. Retrieved from https://www.tek.com/datasheet/digital-storage-oscilloscope.Google Scholar
- Princeton University. 2018. The PARSEC Benchmark Suite. Retrieved from http://parsec.cs.princeton.edu/index.htm.Google Scholar
- Henk Vandenbergh. 2012. Vdbench users guide. October (2012), 1--114. https://www.oracle.com/technetwork/server-storage/vdbench-1901683.pdf.Google Scholar
- Micha vor dem Berge, Georges Da Costa, Andreas Kopecki, Ariel Oleksiak, Jean-Marc Pierson, Tomasz Piontek, Eugen Volk, and Stefan Wesner. 2012. Modeling and simulation of data center energy-efficiency in CoolEmAll. In Proceedings of the International Workshop on Energy Efficient Data Centers. Springer, 25--36.Google ScholarDigital Library
- Di Wang, Chuangang Ren, Sriram Govindan, Anand Sivasubramaniam, Bhuvan Urgaonkar, Aman Kansal, and Kushagra Vaid. 2013. ACE: Abstracting, characterizing and exploiting peaks and valleys in datacenter power consumption. In Proceedings of the ACM SIGMETRICS Performance Evaluation Review, Vol. 41. ACM, 333--334.Google ScholarDigital Library
- Zhikui Wang, Niraj Tolia, and Cullen Bash. 2010. Opportunities and challenges to unify workload, power, and cooling management in data centers. ACM SIGOPS Op. Syst. Rev. 44, 3 (2010), 41. DOI:https://doi.org/10.1145/1842733.1842741Google ScholarDigital Library
- B. D. Wedlock and J. K. Roberge. 1969. Electronic Components and Measurements. Prentice-Hall. 79013618Google Scholar
- Yingyou Wen, Zhi Li, Shuyuan Jin, Chuan Lin, and Zheng Liu. 2017. Energy-efficient virtual resource dynamic integration method in cloud computing. IEEE Access 5 (2017), 12214--12223. DOI:https://doi.org/10.1109/ACCESS.2017.2721548Google ScholarCross Ref
- Business Wire. 2018. Global Data Center Services Market Growth, Trends, and Forecasts 2018--2023: Tier 4 Data Center Type to Have the Highest Share. Retrieved from https://www.businesswire.com/news/home/20180517005800/en/Global-Data-Center-Services-Market-Growth-Trends.Google Scholar
- Michal Witkowski, Ariel Oleksiak, Tomasz Piontek, and J. Węglarz. 2012. Practical power consumption estimation for real life HPC applications. Fut. Gen. Comput. Syst. 29, 1 (2012), 208--217.Google ScholarDigital Library
- Wei Wu, Lingling Jin, Jun Yang, Pu Liu, and Sheldon X.-D. Tan. 2007. Efficient power modeling and software thermal sensing for runtime temperature monitoring. ACM Trans. Des. Automat. Electron. Syst. 12, 3 (2007), 26--es. DOI:https://doi.org/10.1145/1255456.1255462Google ScholarDigital Library
- Hong Xu and Baochun Li. 2014. Reducing electricity demand charge for data centers with partial execution. In Proceedings of the Fifth International Conference on Future Energy Systems (e-Energy'14), June 2014, Cambridge, United Kingdom. Association for Computing Machinery, 51--61. DOI:https://doi.org/10.1145/2602044.2602048Google ScholarDigital Library
- X. Ye, Y. Yin, and L. Lan. 2017. Energy-efficient many-objective virtual machine placement optimization in a cloud computing environment. IEEE Access 5 (2017), 16006--16020. DOI:https://doi.org/10.1109/ACCESS.2017.2733723Google ScholarCross Ref
- Xiao Zhang, Jian Jun Lu, Xiao Qin, and Xiao Nan Zhao. 2013. A high-level energy consumption model for heterogeneous data centers. Simul. Model. Pract. Theor. 39 (2013), 41--55. DOI:https://doi.org/10.1016/j.simpat.2013.05.006Google ScholarCross Ref
- Kuangyu Zheng, Xiaodong Wang, Li Li, and Xiaorui Wang. 2014. Joint power optimization of data center network and servers with correlation analysis. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’14). IEEE, 2598--2606.Google ScholarCross Ref
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