Computer Science ›› 2022, Vol. 49 ›› Issue (12): 374-380.doi: 10.11896/jsjkx.211000065

• Information Security • Previous Articles    

Data Center Power Attack Defense Strategy Based on PCPEC

OU Dong-yang1, ZHANG Kai-qiang1, CHEN Sheng-lei1, JIANG Cong-feng1, YAN Long-chuan2   

  1. 1 School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018,China
    2 Information and Communication Branch of State Grid Corporation of China,Beijing 100761,China
  • Received:2021-10-11 Revised:2022-05-15 Published:2022-12-14
  • About author:OU Dong-yang,born in 1980,Ph.D candidate.His main research interests include edge computing and cloud computing.JIANG Cong-feng,born in 1980,Ph.D,professor,is a member of China Computer Federation.His main research interests include edge computing,system optimization,performance evaluation and distributed system benchmarking.
  • Supported by:
    National Natural Science Foundation of China(61972118).

Abstract: Currently,due to the wide application of multi-tenancy,containerization,virtualization and power over-subscription in data centers,the possibility of power attack is becoming increasingly higher.The main means of power attack is to run malicious codes to increase the power consumption of servers,storage device and network equipment to exceed the power limit of a distribution system.And it causes server failure or circuit breaker trip,or even the interruption of the power supply system of the data centers.In order to reduce the risk of power attack on data center,this paper proposes a power capping method of performance equivalence configuration(PCPEC).This method takes advantage of the difference of power consumption in different configurations of virtual machines to implement the equivalent replacement of virtual machine configuration.Experiment result shows that PCPEC can reduce the dynamic power consumption of the server by 22.2%~29.6%,and the performance of most virtual machines increases by 2.12% after the replacement of resource configuration,thus effectively reducing the impact of power attack on the data center.

Key words: Virtual machine, Data center, Power attack, Power capping, Equivalent replacement

CLC Number: 

  • TP391
[1]ISLAM M A,YANG L,RANGANATH K,et al.Why some like it loud:timing power attacks in multi-tenant data centers using an acoustic side channel [J].ACM on Measurement and Analysis of Computing Systems,2018,2(1):1-33.
[2]LI C,WANG Z,HOU X,et al.Power attack defense:securing battery-backed data centers [J].ACM Computer Architecture News,2016,44(3):493-505.
[3]GAO X,XU Z,WANG H,et al.Reduced cooling redundancy:a new security vulnerability in a hot data center [C]//Network and Distributed System Security Symposium(NDSS).2018.
[4]Summary of the Amazon S3 service disruption in the northern Virginia region [EB/OL].[2020-03-07].https://aws.amazon.com/cn/message/41926/.
[5]DENG W,LIU F M,JIN H,et al.New energy application in cloud computing data center:research status and trend [J].Chinese Journal of Computers,2013,36(3):582-598.
[6]LI X,JIANG X H,WU Z H,et al.Research on heat management method of green data center [J].Chinese Journal of Computers,2015,38(10):1976-1996.
[7]SONG J,SUN Z Z,LIU H,et al.Research progress on energy consumption optimization of hybrid power supply data center [J].Chinese Journal of Computers,2018,41(12):2670-2688.
[8]WANG Z G,YI H,ZHANG W H.Data center energy consumption optimization method based on machine learning characteristics [J].Journal of Software,2014,25(7):1432-1447.
[9]ZHAO X G,HU Q P,DING L,et al.Data center energy-saving scheduling algorithm based on model predictive control [J].Journal of Software,2017,28(2):429-442.
[10]LI D H,ZHAO J C,CUI H M,et al.Design of DVFS impact model on program performance in data center [J].Journal of Software,2017,28(4):845-859.
[11]LEFURGY C,WANG X,WARE M.Power capping:A prelude to power shifting [J].Cluster Computing,2008,11(2):182-194.
[12]RAGHAVENDRA R,RANGANATHAN P,TALWAR V,et al.No “power” struggles:Coordinated multi-level power management for the data center [C]//Proceedings of the 13th International Conference on Architectural Support for Programming Languages and Operating Systems.Seattle:ASPLOS,2008:48-59.
[13]RANGANATHAN P,LEECH P,IRWIN D,et al.Ensemble-level power management for dense blade servers [J].ACM SIGARCH Computer Architecture News,2006,34(2):66-77.
[14]WANG X,CHEN M.Cluster-level feedback power control for performance optimization [C]//2008 IEEE 14th International Symposium on High Performance Computer Architecture.Salt Lake City:IEEE,2008:101-110.
[15]WANG X,CHEN M,LEFURGY C,et al.SHIP:Scalable hie-rarchical power control for large-scale data centers [C]//2009 18th International Conference on Parallel Architectures and Compilation Techniques.Raleigh:IEEE,2009:91-100.
[16]RANGANATHAN P,LEECH P,IRWIN D,et al.Ensemble-level power management for dense blade servers [J].ACM SIGARCH Computer Architecture News,2006,34(2):66-77.
[17]FAN X,WEBER W D,BARROSO L A.Power provisioning for a warehouse-sized computer [J].ACM SIGARCH Computer Architecture News,2007,35(2):12-22.
[18]ARROBA P,MOYA J M,AYALA J L,et al.Dynamic Voltage and Frequency Scaling-aware dynamic consolidation of virtual machines for energy efficient cloud data centers [J].Concurrency and Computation:Practice and Experience,2017,29(10):e4067.
[19]KUEHN P J,MASHALY M.DVFS-power management andperformance engineering of data center server clusters [C]//2019 15th Annual Conference on Wireless On-demand Network Systems and Services(WONS).Wengen:IEEE,2019:91-98.
[20]LIM H,KANSAL A,LIU J.Power budgeting for virtualized data centers [C]//2011 USENIX Annual Technical Conference(USENIX ATC’11).2011:59-63.
[21]GUITART J.Toward sustainable data centers:A comprehensive energy management strategy [J].Computing,2017,99(6):597-614.
[22]VAPNIK V.The nature of statistical learning theory[M].Springer Science & Business Media,2012:201-205.
[23]CHANG C C,LIN C J.LIBSVM:A library for support vector machines [J].ACM Transactions on Intelligent Systems and Technology(TIST),2011,2(3):1-27.
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