人工智能技术在云计算数据中心能量管理中的应用与展望
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摘要
云计算数据中心是重要的电力用户,其消耗电量随着互联网发展和国家数字化建设快速增加,对数据中心进行能量管理和优化是绿色经济必然要求。该文主要探讨人工智能技术在云计算数据中心能量管理和优化中的应用,介绍了深度学习、深度强化学习和知识图谱等新一代人工智能研究热点,提出了一个跨层的数据中心能耗感知和精确能量管理框架,梳理比较了机房、设备、云计算平台、业务系统和数据中心5个层面的能量管理和优化技术,总结分析了当前存在的不足和挑战,展望了未来新一代人工智能技术在云计算数据中心研究和应用趋势。
Cloud computing data center is the important electricity user. Its power consumption increases rapidly with the development of Internet and national digital construction. Energy management and optimization of data center is a necessary for green economy. This paper mainly discussed the application of artificial intelligence technology in the energy management and optimization for cloud computing data center. The paper introduced research hotspots of new artificial intelligence, such as deep learning, deep reinforcement learning, and knowledge graph, proposed a framework of cross layer data center energy aware and accurate energy management, and compared energy management and optimization technologies at five levels of computer room, equipment, cloud computing platform, business system, and data center. Finally, the paper summarized and analyzed the existing deficiencies and challenges, and discussed the future trends of research and application of new artificial intelligence technology in cloud computing data center energy management.
Cloud computing data center is the important electricity user. Its power consumption increases rapidly with the development of Internet and national digital construction. Energy management and optimization of data center is a necessary for green economy. This paper mainly discussed the application of artificial intelligence technology in the energy management and optimization for cloud computing data center. The paper introduced research hotspots of new artificial intelligence, such as deep learning, deep reinforcement learning, and knowledge graph, proposed a framework of cross layer data center energy aware and accurate energy management, and compared energy management and optimization technologies at five levels of computer room, equipment, cloud computing platform, business system, and data center. Finally, the paper summarized and analyzed the existing deficiencies and challenges, and discussed the future trends of research and application of new artificial intelligence technology in cloud computing data center energy management.
引文
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[2]Armbrust M,Fox A,Griffith R,et al.Above the clouds:a Berkeley view of cloud computing[EB/OL].Technical Report No.UCB/EECS-2009-28,Berkeley:EECSDepartment,University of California,(2009-02-10)[2018-05-25].http://www2.eecs.berkeley.edu/Pubs/Tech Rpts/2009/EECS-2009-28.html.
[3]Vaquero L M,Rodero-Merino L,Caceres J,et al.A break in the clouds:towards a cloud definition[J].ACMSIGCOMM Computer Communication Review,2008,39(1):50-55.
[4]Baliga J,Ayre R W A,Hinton K,et al.Green cloud computing:balancing energy in processing,storage,and transport[J].Proceedings of the IEEE,2010,99(1):149-167.
[5]Shehabi A,Smith S J,Horner N,et al.2016.United States data center energy usage report[R].Berkeley,California:Lawrence Berkeley National Laboratory,2016.
[6]Hinton G E,Osindero S,Teh Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554.
[7]Hinton G E,Salakhutdinov R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[8]Vincent P,Larochelle H,Lajoie I,et al.Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion[J].Journal of Machine Learning Research,2010,11:3371-3408.
[9]Le Roux N,Bengio Y.Representational power of restricted Boltzmann machines and deep belief networks[J].Neural Computation,2008,20(6):1631-1649.
[10]Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems.Lake Tahoe,Nevada:Curran Associates Inc.,2012:1097-1105.
[11]Hochreiter S,Schmidhuber J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[12]Larochelle H,Bengio Y.Classification using discriminative restricted Boltzmann machines[C]//Proceedings of the 25th International Conference on Machine Learning.Helsinki,Finland:ACM,2008:536-543.
[13]Taylor G W,Hinton G E.Factored conditional restricted Boltzmann machines for modeling motion style[C]//Proceedings of the 26th Annual International Conference on Machine Learning.Montreal,Quebec:ACM,2009:1025-1032.
[14]Salakhutdinov R,Mnih A,Hinton G.Restricted Boltzmann machines for collaborative filtering[C]//Proceedings of the 24th International Conference on Machine Learning.Corvalis,Oregon:ACM,2007:791-798.
[15]Lécun Y,Bottou L,Bengio Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[16]魏东,龚庆武,来文青,等.基于卷积神经网络的输电线路区内外故障判断及故障选相方法研究[J].中国电机工程学报,2016,36(S1):21-28.Wei Dong,Gong Qingwu,Lai Wenqing,et al.Research on internal and external fault diagnosis and fault-selection of transmission line based on convolutional neural network[J].Proceedings of the CSEE,2016,36(S1):21-28(in Chinese).
[17]胡青,刘本永.基于卷积神经网络的说话人识别算法[J].计算机应用,2016,36(S1):79-81,200.Hu Qing,Liu Benyong.Speaker recognition based on convolutional neural network[J].Journal of Computer Application,2016,36(S1):79-81,200(in Chinese).
[18]刘龙飞,杨亮,张绍武,等.基于卷积神经网络的微博情感倾向性分析[J].中文信息学报,2015,29(6):159-165.Liu Longfei,Yang Liang,Zhang Shaowu,et al.Convolutional neural networks for Chinese micro-blog sentiment analysis[J].Journal of Chinese Information Processing,2015,29(6):159-165(in Chinese).
[19]Hu Jie,Shen Li,Albanie S,et al.Squeeze-and-Excitation Networks[EB/OL].2017[2018-06-02].https://arxiv.org/pdf/1709.01507.pdf.
[20]Han S S,Kim M S,Lim W,et al.Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm[J].Journal of Investigative Dermatology,2018,138(7):1529-1538.
[21]Mnih V,Kavukcuoglu K,Silver D,et al.Playing Atari with deep reinforcement learning[C]//Proceedings of Workshop at the 26th Neural Information Processing Systems 2013.Lake Tahoe,USA:ACM,2013:201-220.
[22]Mnih V,Kavukcuoglu K,Silver D,et al.Human-level control through deep reinforcement learning[J].Nature,2015,518(7540):529-533.
[23]Sutton R S,McAllester D A,Singh S P,et al.Policy gradient methods for reinforcement learning with function approximation[C]//Proceedings of the 12th International Conference on Neural Information Processing Systems.Denver:MIT Press,1999,12:1057-1063.
[24]Lillicrap T P,Hunt J J,Pritzel A,et al.Continuous control with deep reinforcement learning[EB/OL].2015[2018-06-08].http://cn.arxiv.org/pdf/1509.02971v5.
[25]Heess N,Wayne G,Silver D,et al.Learning continuous control policies by stochastic value gradients[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.Montreal,Canada:MIT Press,2015:2944-2952.
[26]Mnih V,Badia A P,Mirza M,et al.Asynchronous methods for deep reinforcement learning[C]//Proceedings of The 33rd International Conference on Machine Learning.New York:The International Machine Learning Society,2016:1928-1937.
[27]Silver D,Huang A,Maddison C J,et al.Mastering the game of Go with deep neural networks and tree search[J].Nature,2016,529(7587):484-489.
[28]Coulom R.Efficient selectivity and backup operators in Monte-Carlo tree search[C]//Proceedings of the 5th International Conference on Computers and Games.Turin,Italy:Springer,2006:72-83.
[29]Turian J,Ratinov L,Bengio Y.Word representations:a simple and general method for semi-supervised learning[C]//Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.Uppsala,Sweden:Association for Computational Linguistics,2010:384-394.
[30]Mikolov T,Sutskever I,Chen Kai,et al.Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems.Lake Tahoe,Nevada:Curran Associates Inc.,2013,26:3111-3119.
[31]Bordes A,Usunier N,Garcia-Durán A,et al.Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems.Lake Tahoe,Nevada:Curran Associates Inc.,2013:2787-2795.
[32]刘峤,李杨,段宏,等.知识图谱构建技术综述[J].计算机研究与发展,2016,53(3):582-600.Liu Qiao,Li Yang,Duan Hong,et al.Knowledge graph construction techniques[J].Journal of Computer Research and Development,2016,53(3):582-600(in Chinese).
[33]Hsu C H,Deng Qingyuan,Mars J,et al.SmoothOperator:Reducing Power Fragmentation and Improving Power Utilization in Large-scale Datacenters[C]//Proceedings of the 23rd International Conference on Architectural Support for Programming Languages and Operating Systems.Williamsburg:ACM,2018:535-548.
[34]Wu Qiang,Deng Qingyuan,Ganesh L,et al.Dynamo:Facebook's data center-wide power management system[C]//Proceedings of the ACM/IEEE 43rd Annual International Symposium on Computer Architecture.Seoul,South Korea:IEEE,2016:469-480.
[35]Li Yuanlong,Wen Yonggang,Guan K,et al.Transforming cooling optimization for green data center via deep reinforcement learning[EB/OL].2017[2018-05-20].https://arxiv.org/p df/1709.05077.pdf.
[36]Azimi R,Zhan Xin,Reda S.Thermal-aware layout planning for heterogeneous datacenters[C]//Proceedings of2014 IEEE/ACM International Symposium on Low Power Electronics and Design.La Jolla,CA,USA:IEEE,2015:245-250.
[37]Moore J,Chase J,Ranganathan P,et al.Making scheduling"Cool":temperature-aware workload placement in data centers[C]//Proceedings of the annual conference on USENIX Annual Technical Conference.Anaheim:USENIX,2005:61-75.
[38]Li Hongjian,Zhu Guofeng,Cui Chengyuan,et al.Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing[J].Computing,2016,98(3):303-317.
[39]Su Bo,Gu Junli,Shen Li,et al.PPEP:online performance,power,and energy prediction framework and DVFS space exploration[C]//Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture.Cambridge:IEEE,2014:445-457.
[40]Zhu Qi,Wu Bo,Shen Xipeng,et al.Co-run scheduling with power cap on integrated CPU-GPU systems[C]//Proceedings of 2017 IEEE International Parallel and Distributed Processing Symposium.Orlando,FL,USA:IEEE,2017:967-977.
[41]Heller B,Seetharaman S,Mahadevan P,et al.Elastic Tree:saving energy in data center networks[C]//Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation.San Jose,California:USENIX Association,2010:17-17.
[42]Li Dan,Shang Yunfei,He Wu,et al.EXR:greening data center network with software defined exclusive routing[J].IEEE Transactions on Computers,2015,64(9):2534-2544.
[43]叶可江,吴朝晖,姜晓红,等.虚拟化云计算平台的能耗管理[J].计算机学报,2012,35(6):1262-1285.Ye Kejiang,Wu Zhaohui,Jiang Xiaohong,et al.Power management of virtualized cloud computing platfoom[J].Chinese Journal of Computers,2012,35(6):1262-1285(in Chinese).
[44]Beloglazov A,Abawajy J,Buyya R.Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing[J].Future Generation Computer Systems,2012,28(5):755-768.
[45]熊永华,张因升,陈鑫,等.云视频监控系统的能耗优化研究[J].软件学报,2015,26(3):680-698.Xiong Yonghua,Zhang Yinsheng,Chen Xin,et al.Research of energy consumption optimization methods for cloud video surveillance system[J].Journal of Software,2015,26(3):680-698(in Chinese).
[46]Liu Ning,Li Zhe,Xu Jielong,et al.A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning[C]//Proceedings of the 37th IEEE International Conference on Distributed Computing Systems.Atlanta:IEEE,2017:372-382.
[47]Rajamani K,Lefurgy C.On evaluating requestdistribution schemes for saving energy in server clusters[C]//Proceedings of 2003 IEEE International Symposium on Performance Analysis of Systems and Software.Austin:IEEE,2003:111-122.
[48]Casalicchio E,Lundberg L,Shirinbab S.Energy-aware auto-scaling algorithms for Cassandra virtual data centers[J].Cluster Computing,2017,20(3):2065-2082.
[49]Wong D.Peak efficiency aware scheduling for highly energy proportional servers[C]//Proceedings of the 43rd Annual International Symposium on Computer Architecture.Seoul,South Korea:IEEE,2016:481-492.
[50]S?rbu A,Babaoglu O.A data‐driven approach to modeling power consumption for a hybrid supercomputer[J].Concurrency and Computation Practice and Experience,2018,30(9):e4410.
[51]Hu Zhiming,Li Baochun,Luo Jun.Time-and costefficient task scheduling across geo-distributed data centers[J].IEEE Transactions on Parallel and Distributed Systems,2018,29(3):705-718.
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