skip to main content
survey

A Taxonomy and Survey of Power Models and Power Modeling for Cloud Servers

Published: 28 September 2020 Publication History

Abstract

Due to the increasing demand of cloud resources, the ever-increasing number and scale of cloud data centers make their massive power consumption a prominent issue today. Evidence reveals that the behaviors of cloud servers make the major impact on data centers’ power consumption. Although extensive research can be found in this context, a systematic review of the models and modeling methods for the entire hierarchy (from underlying hardware components to the upper-layer applications) of the cloud server is still missing, which is supposed to cover the relevant studies on physical and virtual cloud server instances, server components, and cloud applications. In this article, we summarize a broad range of relevant studies from three perspectives: power data acquisition, power models, and power modeling methods for cloud servers (including bare-metal, virtual machine (VM), and container instances). We present a comprehensive taxonomy on the collection methods of server-level power data, the existing mainstream power models at multiple levels from hardware to software and application, and commonly used methods for modeling power consumption including classical regression analysis and emerging methods like reinforcement learning. Throughout the work, we introduce a variety of models and methods, illustrating their implementation, usability, and applicability while discussing the limitations of existing approaches and possible ways of improvement. Apart from reviewing existing studies on server power models and modeling methods, we further figure out several open challenges and possible research directions, such as the study on modeling the power consumption of lightweight virtual units like unikernel and the necessity of further explorations toward empowering server power estimation/prediction with machine learning. As power monitoring is drawing increasing attention from cloud service providers (CSPs), this survey provides useful guidelines on server power modeling and can be inspiring for further research on energy-efficient data centers.

References

[1]
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. arXiv:1006.0308.
[2]
Miyuru Dayarathna, Yonggang Wen, and Rui Fan. 2017. Data center energy consumption modeling: A survey. IEEE Communications Surveys 8 Tutorials 18, 1 (2017), 732--794.
[3]
Aman Kansal, Zhao Feng, Liu Jie, Nupur Kothari, and Arka A. Bhattacharya. 2010. Virtual machine power metering and provisioning. In Proceedings of the ACM Symposium on Cloud Computing.
[4]
Jayant Baliga, Robert W. A. Ayre, Kerry Hinton, and Rodney S. Tucker. 2010. Green cloud computing: Balancing energy in processing, storage, and transport. Proceedings of the IEEE 99, 1 (2010), 149--167.
[5]
Amir Varasteh Hajipour and Maziar Goudarzi. 2017. Server consolidation techniques in virtualized data centers: A survey. IEEE Systems Journal 11, 2 (2017), 772--783.
[6]
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. Applied Energy 222 (2018), 329--342.
[7]
Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, and Albert Zomaya. 2011. A taxonomy and survey of energy-efficient data centers and cloud computing systems. In Advances in Computers. Vol. 82. Elsevier, 47--111.
[8]
Zheng Li, Selome Tesfatsion, Saeed Bastani, Ahmed Ali-Eldin, Erik Elmroth, Maria Kihl, and Rajiv Ranjan. 2017. A survey on modeling energy consumption of cloud applications: Deconstruction, state of the art, and trade-off debates. IEEE Transactions on Sustainable Computing 2, 3 (2017), 255--274.
[9]
Huawei Technologies Co. n.d. FusionServer iBMC. Retrieved July 24, 2020 from https://e.huawei.com/en/material/onLineView?materialid=8a3ba90f16654be49cd91d8b526f6b44.
[10]
Weiwei Lin, Haoyu Wang, Yufeng Zhang, Deyu Qi, James Z. Wang, and Victor Chang. 2018. A cloud server energy consumption measurement system for heterogeneous cloud environments. Information Sciences 468 (2018), 47--62.
[11]
James W. Smith and Ian Sommerville. 2011. Workload classification 8 software energy measurement for efficient scheduling on private cloud platforms. arXiv:1105.2584.
[12]
Ata E. Husain Bohra and Vipin Chaudhary. 2010. VMeter: Power modelling for virtualized clouds. In Proceedings of the IEEE International Symposium on Parallel 8 Distributed Processing.
[13]
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. Software: Practice and Experience 41 (2011), 23--50.
[14]
Chung Hsing Hsu and Stephen W. Poole. 2011. Power signature analysis of the SPECpower_ssj2008 benchmark. In Proceedings of the IEEE International Symposium on Performance Analysis of Systems 8 Software.
[15]
Christos K. Filelis-Papadopoulos, Konstantinos M. Giannoutakis, George A. Gravvanis, and Dimitrios Tzovaras. 2017. Large-scale simulation of a self-organizing self-management cloud computing framework. Journal of Supercomputing 3 (2017), 1--21.
[16]
Shuaiwen Leon Song, Kevin Barker, and Darren Kerbyson. 2013. Unified performance and power modeling of scientific workloads. In Proceedings of the 1st International Workshop on Energy Efficient Supercomputing. 1--8.
[17]
Bogdan Marius Tudor and Yong Meng Teo. 2013. On understanding the energy consumption of ARM-based multicore servers. In Proceedings of the ACM SIGMETRICS/International Conference on Measurement and Modeling of Computer Systems. 267--278.
[18]
E. N. Mootaz Elnozahy, Michael Kistler, and Ramakrishnan Rajamony. 2002. Energy-efficient server clusters. In Proceedings of the International Workshop on Power-Aware Computer Systems., 179--197.
[19]
Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. 2007. Power provisioning for a warehouse-sized computer. ACM Sigarch Computer Architecture News 35, 2 (2007), 13--23.
[20]
Suzanne Rivoire, Parthasarathy Ranganathan, and Christos Kozyrakis. 2008. A comparison of high-level full-system power models. In Proceedings of the Workshop on Power Aware Computing 8 Systems.
[21]
Luiz André Barroso and Urs Hölzle. 2009. The datacenter as a computer: An introduction to the design of warehouse-scale machines. Synthesis Lectures on Computer Architecture 4, 1 (2009), 1--108.
[22]
Robert Basmadjian, Nasir Ali, Florian Niedermeier, Hermann De Meer, and Giovanni Giuliani. 2011. A methodology to predict the power consumption of servers in data centres. In Proceedings of the ACM SIGCOMM International Conference on Energy-Efficient Computing 8 Networking.
[23]
Liang Luo, W. U. Wen-Jun, and Fei Zhang. 2014. Energy modeling based on cloud data center. Journal of Software 7 (2014), 1371--1387.
[24]
Robert Basmadjian and Hermann De Meer. 2012. Evaluating and modeling power consumption of multi-core processors. In Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing, and Communication Meet. Article 12, 10 pages.
[25]
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 8 Analysis.
[26]
Bharan Giridhar, Michael Cieslak, Deepankar Duggal, Ronald Dreslinski, Hsing Min Chen, Robert Patti, Betina Hold, Chaitali Chakrabarti, Trevor Mudge, and David Blaauw. 2013. Exploring DRAM organizations for energy-efficient and resilient exascale memories. In Proceedings of the International Conference on High Performance Computing, Networking, Storage, and Analysis. 1--12.
[27]
Patricia Arroba, Jose L. Risco-Martin, Marina Zapater, Jose M. Moya, Jose L. Ayala, and Katzalin Olcoz. 2014. Server power modeling for run-time energy optimization of cloud computing facilities. Energy Procedia 62 (2014), 401--410.
[28]
Yan Zhang, Sudhanva Gurumurthi, and Mircea R. Stan. 2007. SODA: Sensitivity based optimization of disk architecture. In Proceedings of the 44th Annual Design Automation Conference. 865--870.
[29]
Miriam Allalouf, Yuriy Arbitman, Michael Factor, Ronen I. Kat, Kalman Z. Meth, and Dalit Naor. 2009. Storage modeling for power estimation. In Proceedings of SYSTOR 2009: the Israeli Experimental Systems Conference. Article 3, 10 pages.
[30]
Anshul Gandhi, Mor Harchol-Balter, Rajarshi Das, and Charles Lefurgy. 2009. Optimal power allocation in server farms. In Proceedings of the 11th International Joint Conference on Measurement 8 Modeling of Computer Systems.
[31]
Maruti Gupta and Suresh Singh. 2003. Greening of the Internet. ACM SIGCOMM Computer Communication Review 33, 4 (2003), 19--26.
[32]
Suresh Gupta and Maruti Singh. 2007. Dynamic ethernet link shutdown for energy conservation on Ethernet links. In Proceedings of the 2007 IEEE International Conference on Communications. IEEE, Los Alamitos, CA, 6156--6161.
[33]
Maruti Gupta and Suresh Singh. 2007. Using low-power modes for energy conservation in Ethernet LANs. In Proceedings of the IEEE International Conference on Computer Communications (IEEE INFOCOM’07).
[34]
Chamara Gunaratne, Ken Christensen, and Bruce Nordman. 2010. Managing energy consumption costs in desktop PCs and LAN switches with proxying, split TCP connections, and scaling of link speed. International Journal of Network Management 15, 5 (2010), 297--310.
[35]
F. Blanquicet and K. Christensen. 2008. Managing energy use in a network with a new SNMP Power State MIB. In Proceedings of the IEEE Conference on Local Computer Networks.
[36]
Robert Basmadjian, Hermann De Meer, Ricardo Lent, and Giovanni Giuliani. 2012. Cloud computing and its interest in saving energy: The use case of a private cloud. Journal of Cloud Computing Advances Systems 8 Applications 1, 1 (2012), 5.
[37]
Yanfei Li, Wang Ying, Yin Bo, and Guan Lu. 2012. An online power metering model for cloud environment. In Proceedings of the IEEE International Symposium on Network Computing 8 Applications.
[38]
Daniel Versick, Ingolf Wabmann, and Djamshid Tavangarian. 2013. Power consumption estimation of CPU and peripheral components in virtual machines. ACM SIGAPP Applied Computing Review 13, 3 (2013), 17--25.
[39]
Ingolf Wabmann, Daniel Versick, and Djamshid Tavangarian. 2013. Energy consumption estimation of virtual machines. In Proceedings of the ACM Symposium on Applied Computing.
[40]
Xiao Peng, Zhigang Hu, Dongbo Liu, Guofeng Yan, and Xilong Qu. 2013. Virtual machine power measuring technique with bounded error in cloud environments. Journal of Network 8 Computer Applications 36, 2 (2013), 818--828.
[41]
Xiao Peng and Zhao Sai. 2013. A low-cost power measuring technique for virtual machine in cloud environments. International Journal of Grid 8 Distributed Computing 6, 3 (2013), 69--80.
[42]
Wentai Wu, Weiwei Lin, and Zhiping Peng. 2016. An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment. Soft Computing 21 (2016), 5755--5764.
[43]
Dong Ki Kang, Gyu Beom Choi, Seong Hwan Kim, Il Sun Hwang, and Chan Hyun Youn. 2017. Workload-aware resource management for energy efficient heterogeneous docker containers. In Proceedings of the 2010 Region 10 Conference.
[44]
Sareh Fotuhi Piraghaj, Amir Vahid Dastjerdi, Rodrigo N. Calheiros, and Rajkumar Buyya. 2016. A framework and algorithm for energy efficient container consolidation in cloud data centers. In Proceedings of the IEEE International Conference on Data Science 8 Data Intensive Systems.
[45]
Phung James, Young Choon Lee, and Albert Y. Zomaya. 2019. Lightweight power monitoring framework for virtualized computing environments. IEEE Transactions on Computers 69, 1 (2019), 14--25.
[46]
Guillaume Fieni, Romain Rouvoy, and Lionel Seinturier. 2020. SmartWatts: Self-calibrating software-defined power meter for containers. arXiv:2001.02505.
[47]
Senay Semu Tadesse, Francesco Malandrino, and Carla Fabiana Chiasserini. 2017. Energy consumption measurements in Docker. In Proceedings of the IEEE Computer Software 8 Applications Conference, Vol. 2.
[48]
James William Smith, Ali Khajehhosseini, Jonathan Stuart Ward, and Ian Sommerville. 2012. CloudMonitor: Profiling power usage. In Proceedings of the IEEE International Conference on Cloud Computing.
[49]
Shinan Wang, Youhuizi Li, Weisong Shi, Lingjun Fan, and Abhishek Agrawal. 2013. Safari: Function-level power analysis using automatic instrumentation. In Proceedings of the International Conference on Energy Aware Computing.
[50]
Ricardo Koller, Akshat Verma, and Anindya Neogi. 2010. WattApp: An application aware power meter for shared data centers. In Proceedings of the 7th International Conference on Autonomic Computing. 31--40.
[51]
Feifei Chen, John Grundy, and Yun Yang. 2013. Experimental analysis of task-based energy consumption in cloud computing systems. In Proceedings of the ACM/SPEC International Conference on Performance Engineering. 295--306.
[52]
Marc Gamell, Ivan Rodero, Manish Parashar, and Stephen Poole. 2013. Exploring energy and performance behaviors of data-intensive scientific workflows on systems with deep memory hierarchies. In Proceedings of the International Conference on High Performance Computing.
[53]
Maxime Colmant, Mascha Kurpicz, Pascal Felber, Loic Huertas, Romain Rouvoy, and Anita Sobe. 2015. Process-level power estimation in VM-based systems. In Proceedings of the 10th European Conference on Computer Systems.
[54]
Alessandro Leite, Claude Tadonki, Christine Eisenbeis, and Alba de Melo. 2014. A fine-grained approach for power consumption analysis and prediction. Procedia Computer Science 29 (2014), 2260--2271.
[55]
David Meisner, Christopher M. Sadler, Luiz Andre Barroso, Wolf Dietrich Weber, and Thomas F. Wenisch. 2011. Power management of online data-intensive services. In Proceedings of the International Symposium on Computer Architecture.
[56]
Meikel Poess and Raghunath Othayoth Nambiar. 2010. A power consumption analysis of decision support systems. In Proceedings of the 1st Joint WOSP/SIPEW International Conference on Performance Engineering.
[57]
Nan Zhu, Lei Rao, Xue Liu, Jie Liu, and Haibin Guan. 2011. Taming power peaks in MapReduce clusters. ACM SIGCOMM Computer Communication Review 41, 4 (2011), 416--417.
[58]
Nan Zhu, Lei Rao, Xue Liu, and Jie Liu. 2012. Handling more data with less cost: Taming power peaks in MapReduce clusters. In Proceedings of the Asia-Pacific Workshop on Systems. 1--6.
[59]
Willis Lang and Jignesh M. Patel. 2010. Energy management for MapReduce clusters. Proceedings of the VLDB Endowment 3, 1–2 (2010), 129--139.
[60]
Mohammed El Mehdi Diouri, Olivier Gluck, Laurent Lefevre, and Jean-Christophe Mignot. 2013. Energy estimation for MPI broadcasting algorithms in large scale HPC systems. In Proceedings of the European MPI Users Group Meeting.
[61]
Marc Gamell, Ivan Rodero, Manish Parashar, Janine C. Bennett, Hemanth Kolla, Jacqueline Chen, Peer-Timo Bremer, et al. 2013. Exploring power behaviors and trade-offs of in-situ data analytics. In Proceedings of the International Conference on High Performance Computing, Networking, Storage, and Analysis. 1--12.
[62]
Weiwei Lin, Wentai Wu, Haoyu Wang, James Z. Wang, and Ching Hsien Hsu. 2016. Experimental and quantitative analysis of server power model for cloud data centers. Future Generation Computer Systems 86 (2016), 940--950.
[63]
Zhou Zhou, Jemal H. Abawajy, Fangmin Li, Zhigang Hu, and Keqin Li. 2017. Fine-grained energy consumption model of servers based on task characteristics in cloud data center. IEEE Access PP, 99 (2017), 1.
[64]
John J. Prevost, Kranthi Manoj Nagothu, Brian Kelley, and Jamshidi Mo. 2011. Prediction of cloud data center networks loads using stochastic and neural models. In Proceedings of the International Conference on System of Systems Engineering.
[65]
Yao Lu, John Panneerselvam, Lu Liu, and Yan Wu. 2016. RVLBPNN: A workload forecasting model for smart cloud computing. Scientific Programming 2016 (2016), 1--9.
[66]
Jitendra Kumar and Ashutosh Kumar Singh. 2018. Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Generation Computer Systems 81 (2018), 41--52.
[67]
Luo Liang, Wenjun Wu, W. T. Tsai, Dichen Di, and Zhang Fei. 2013. Simulation of power consumption of cloud data centers. Simulation Modelling Practice 8 Theory 39 (2013), 152--171.
[68]
T. Veni and S. Mary Saira Bhanu. 2016. Prediction model for virtual machine power consumption in cloud environments. Procedia Computer Science 87 (2016), 122--127.
[69]
Humaira Abdul Salam, Franco Davoli, Alessandro Carrega, and Andreas Timm-Giel. 2018. Towards prediction of power consumption of virtual machines for varying loads. In Proceedings of the 2018 28th International Telecommunication Networks and Applications Conference (ITNAC’18). IEEE, Los Alamitos, CA, 1--6.
[70]
Hailong Yang, Zhao Qi, Zhongzhi Luan, and Depei Qian. 2014. iMeter: An integrated VM power model based on performance profiling. Future Generation Computer Systems 36, 3 (2014), 267--286.
[71]
Shuai Ye, Ruoyan Zhao, and Xinru Fang. 2019. An ensemble learning method for dialect classification. In IOP Conference Series: Materials Science and Engineering, Vol. 569. IOP Publishing, 052064.
[72]
De-Shuang Huang, Vitoantonio Bevilacqua, and Prashan Premaratne. 2016. Intelligent Computing Theories and Application: 12th International Conference, ICIC 2016, Lanzhou, China, August 2-5, 2016, Proceedings I. Lecture Notes in Computer Science, Vol. 9771. Springer.
[73]
Timothy Harton, Cameron G. Walker, and Michael O’Sullivan. 2016. Towards power consumption modeling for servers at scale. In Proceedings of the IEEE/ACM International Conference on Utility 8 Cloud Computing.
[74]
Balaji Krishnapuram, Mohak Shah, Alex Smola, Charu Aggarwal, Dou Shen, and Rajeev Rastogi. 2016. KDD’16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY.
[75]
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems. 3146--3154.
[76]
Ning Liu, Lin Xue, and Yanzhi Wang. 2017. Data center power management for regulation service using neural network-based power prediction. In Proceedings of the International Symposium on Quality Electronic Design.
[77]
Yuanlong Li, Hu Han, Yonggang Wen, and Jun Zhang. 2016. Learning-based power prediction for data centre operations via deep neural networks. In Proceedings of the International Workshop on Energy Efficient Data Centres.
[78]
Weiwei Lin, Guangxin Wu, Xinyang Wang, and Keqin Li. 2019. An artificial neural network approach to power consumption model construction for servers in cloud data centers. IEEE Transactions on Sustainable Computing. Early Access.
[79]
Nilabja Roy, Abhishek Dubey, and Aniruddha S. Gokhale. 2011. Efficient autoscaling in the cloud using predictive models for workload forecasting. In Proceedings of the IEEE International Conference on Cloud Computing.
[80]
Jitendra Kumar, Rimsha Goomer, and Ashutosh Kumar Singh. 2018. Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters. Procedia Computer Science 125 (2018), 676--682.
[81]
Z. Chen, Y. Zhu, Y. Di, and S. Feng. 2015. Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network. Computational Intelligence and Neuroscience 2015, 10a (2015), 17.
[82]
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 Design Automation Conference.
[83]
Zhu Hao, Huadong Dai, Shazhou Yang, Yuejin Yan, and Bin Lin. 2017. Estimating power consumption of servers using Gaussian mixture model. In Proceedings of the International Symposium on Computing 8 Networking.
[84]
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv:1312.5602.
[85]
Fahimeh Farahnakian, Pasi Liljeberg, and Juha Plosila. 2014. Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In Proceedings of the Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.
[86]
Ning Liu, Li Zhe, Zhiyuan Xu, Jielong Xu, and Yanzhi Wang. 2017. A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning. In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems (ICDCS’17). 372--382.
[87]
Juan C. Salinas Hilburg, Marina Zapater, Jose L. Risco Martin, Jose M. Moya, and Jose L. Ayala. 2015. Using grammatical evolution techniques to model the dynamic power consumption of enterprise servers. In Proceedings of the 9th International Conference on Complex, Intelligent, and Software Intensive Systems.
[88]
Sparsh Mittal and Jeffrey S. Vetter. 2014. A survey of methods for analyzing and improving GPU energy efficiency. ACM Computing Surveys 47, 2 (2014), 1--23.
[89]
Kenneth O’Neal and Philip Brisk. 2018. Predictive modeling for CPU, GPU, and FPGA performance and power consumption: A survey. In Proceedings of the 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI’18). 763--768.
[90]
Mark Gebhart, Daniel R. Johnson, David Tarjan, Stephen W. Keckler, William J. Dally, Erik Lindholm, and Kevin Skadron. 2011. Energy-efficient mechanisms for managing thread context in throughput processors. In Proceedings of the 2011 38th Annual International Symposium on Computer Architecture (ISCA’11). IEEE, Los Alamitos, CA, 235--246.
[91]
Daniel Nikolai Peroni. 2019. Approximate Computing for GPGPU Acceleration. Ph.D. Dissertation. University of California, San Diego.
[92]
Wenjie Liu, Zhihui Du, Xiao Yu, David A. Bader, and Xu Chen. 2011. A waterfall model to achieve energy efficient tasks mapping for large scale GPU clusters. In Proceedings of the IEEE International Symposium on Parallel 8 Distributed Processing Workshops 8 Ph.D. Forum.
[93]
Minsoo Rhu, Michael Sullivan, Jingwen Leng, and Mattan Erez. 2013. A locality-aware memory hierarchy for energy-efficient GPU architectures. In Proceedings of the IEEE/ACM International Symposium on Microarchitecture.
[94]
Gong Fan, Ju Lei, Deshan Zhang, Mengying Zhao, and Zhiping Jia. 2017. Cooperative DVFS for energy-efficient HEVC decoding on embedded CPU-GPU architecture. In Proceedings of the Design Automation Conference.
[95]
João Guerreiro, Aleksandar Ilic, Nuno Roma, and Pedro Tomás. 2019. DVFS-aware application classification to improve GPGPUs energy efficiency. Parallel Computing 83 (2019), 93--117.
[96]
Andrew Kerr, Gregory F. Diamos, and Sudhakar Yalamanchili. 2010. Modeling GPU-CPU workloads and systems. In Proceedings of the Workshop on General Purpose Processing on Graphics Processing Units.
[97]
John A. Stratton, Nasser Anssari, Christopher Rodrigues, I. Jui Sung, and W. M. Hwu. 2012. Optimization and architecture effects on GPU computing workload performance. In Proceedings of the 2012 Conference on Innovative Parallel Computing (InPar’12).
[98]
Mengchi Zhang, Roland Green, and Timothy G. Rogers. 2018. Characterizing the runtime effects of object-oriented workloads on GPUs. In Proceedings of the IEEE International Symposium on Performance Analysis of Systems 8 Software.
[99]
Wang Yue, Soumyaroop Roy, and Nagarajan Ranganathan. 2012. Run-time power-gating in caches of GPUs for leakage energy savings. In Proceedings of the Design, Automation, and Test in Europe Conference 8 Exhibition.
[100]
Junke Li, Guo Bing, Shen Yan, Deguang Li, Jihe Wang, Yanhui Huang, and Li Qiang. 2015. GPU-memory coordinated energy saving approach based on extreme learning machine. In Proceedings of the IEEE International Symposium on High Performance Computing 8 Communications.
[101]
Bing Li, Mengjie Mao, Xiaoxiao Liu, Tao Liu, Zihao Liu, Wujie Wen, Yiran Chen, et al. 2019. Thread batching for high-performance energy-efficient GPU memory design. arXiv:1906.05922.
[102]
Mohsen Imani and Tajana S. Rosing. 2019. Approximate CPU and GPU Design Using Emerging Memory Technologies. Springer.
[103]
Wu Song, Mei Chao, Jin Hai, and Duoqiang Wang. 2018. Android Unikernel: Gearing mobile code offloading towards edge computing. Future Generation Computer Systems 86 (2018), 694--703.
[104]
Zeyi Tao, Qi Xia, Zijiang Hao, Cheng Li, Lele Ma, Shanhe Yi, and Qun Li. 2019. A survey of virtual machine management in edge computing. Proceedings of the IEEE 107, 8 (2019), 1482--1499.
[105]
Xian Yu, Guangxing Zhang, Zhenyu Li, Wei Liangs, and Gaogang Xie. 2019. Toward generalized neural model for VMs power consumption estimation in data centers. In Proceedings of the 2019 IEEE International Conference on Communications (ICC’19).
[106]
Muhammad Qamar Raza and Abbas Khosravi. 2015. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable 8 Sustainable Energy Reviews 50 (2015), 1352--1372.
[107]
Yunbo Li, Anne-Cecile Orgerie, Ivan Rodero, Betsegaw Lemma Amersho, Manish Parashar, and Jean Marc Menaud. 2017. End-to-end energy models for edge cloud-based IoT platforms: Application to data stream analysis in IoT. Future Generation Computer Systems 87 (2017), 667--678.
[108]
Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. Fog computing and its role in the Internet of Things. In Proceedings of the 1st edition of the MCC Workshop on Mobile Cloud Computing.
[109]
Fatemeh Jalali, Kerry Hinton, Robert S. Ayre, Tansu Alpcan, and Rodney S. Tucker. 2016. Fog computing may help to save energy in cloud computing. IEEE Journal on Selected Areas in Communications 34, 5 (2016), 1728--1739.
[110]
Siming Wang, Xumin Huang, Liu Yi, and Yu Rong. 2016. CachinMobile: An energy-efficient users caching scheme for fog computing. In Proceedings of the IEEE/CIC International Conference on Communications in China.
[111]
Ruilong Deng, Rongxing Lu, Chengzhe Lai, Tom Hao Luan, and Liang Hao. 2017. Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE Internet of Things Journal 3, 6 (2017), 1171--1181.
[112]
Kai Liang, Liqiang Zhao, Xiaohui Zhao, Yong Wang, and Shumao Ou. 2016. Joint resource allocation and coordinated computation offloading for fog radio access networks. China Communications 13, 2 (2016), 131--139.

Cited By

View all
  • (2025)A Coalitional Game-Based Adaptive Scheduler Leveraging Task Heterogeneity for Greener Data CentersIEEE Transactions on Green Communications and Networking10.1109/TGCN.2024.34146719:1(55-69)Online publication date: Mar-2025
  • (2025)DRCD: a regional-contention-driven arbitration policy for CPU–GPU heterogeneous systemsThe Journal of Supercomputing10.1007/s11227-025-07001-781:3Online publication date: 9-Feb-2025
  • (2024)Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers: A ReviewIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33463329:3(571-590)Online publication date: May-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys
ACM Computing Surveys  Volume 53, Issue 5
September 2021
782 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3426973
Issue’s Table of Contents
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 September 2020
Accepted: 01 June 2020
Revised: 01 June 2020
Received: 01 October 2019
Published in CSUR Volume 53, Issue 5

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Cloud server
  2. data center
  3. power consumption
  4. power model
  5. power modeling

Qualifiers

  • Survey
  • Research
  • Refereed

Funding Sources

  • Guangzhou Science and Technology Program key projects
  • Guangdong Major Project of Basic and Applied Basic Research
  • Guangzhou Development Zone Science and Technology
  • National Natural Science Foundation of China
  • Key-Area Research and Development Program of Guangdong Province
  • Fundamental Research Funds for the Central Universities, SCUT

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)192
  • Downloads (Last 6 weeks)29
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)A Coalitional Game-Based Adaptive Scheduler Leveraging Task Heterogeneity for Greener Data CentersIEEE Transactions on Green Communications and Networking10.1109/TGCN.2024.34146719:1(55-69)Online publication date: Mar-2025
  • (2025)DRCD: a regional-contention-driven arbitration policy for CPU–GPU heterogeneous systemsThe Journal of Supercomputing10.1007/s11227-025-07001-781:3Online publication date: 9-Feb-2025
  • (2024)Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers: A ReviewIEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33463329:3(571-590)Online publication date: May-2024
  • (2024)Assessing the Energetical Cost of 5G Softwarization2024 IEEE 30th International Symposium on Local and Metropolitan Area Networks (LANMAN)10.1109/LANMAN61958.2024.10621896(33-38)Online publication date: 10-Jul-2024
  • (2024)A Protocol to Assess the Accuracy of Process-Level Power Models2024 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER59578.2024.00014(74-84)Online publication date: 24-Sep-2024
  • (2024)Power Consumption Prediction of Edge Servers Based on Mixed Features and Self-Attention Mechanism2024 International Conference on Artificial Intelligence of Things and Systems (AIoTSys)10.1109/AIoTSys63104.2024.10780522(1-8)Online publication date: 17-Oct-2024
  • (2024)Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis ApproachIEEE Access10.1109/ACCESS.2024.338743612(52524-52538)Online publication date: 2024
  • (2024)A tri-chromosome-based evolutionary algorithm for energy-efficient workflow scheduling in cloudsSwarm and Evolutionary Computation10.1016/j.swevo.2024.10175191(101751)Online publication date: Dec-2024
  • (2024)Energy-aware virtual machine placement based on a holistic thermal model for cloud data centersFuture Generation Computer Systems10.1016/j.future.2024.07.020161(302-314)Online publication date: Dec-2024
  • (2024)Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy modelFuture Generation Computer Systems10.1016/j.future.2024.04.059159:C(379-394)Online publication date: 1-Oct-2024
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media