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Reducing energy bill of data center via flexible partial execution

  • Shubin WangEmail author
  • Xinni Liu
  • Shen Jiang
  • Yong Zhan
Original Research
  • 77 Downloads

Abstract

Several Demand Response (DR) strategies are emerged recently to modulate the workloads of Data Center (DC) and shave the corresponding energy bill. However, since most of these DR strategies will result in the increase of latency, they can only be used for modulating the elastic workloads, which are delay-tolerant. To improve the flexibility of workload modulation and reduction of energy bill, we propose flexible partial execution for DC, which can be used to handle inelastic workloads. Further, to incentivize users of DC grant flexible partial execution of their workloads, we offer them time-varying price discount, on top of commonly-applied usage-based pricing policy. With real-world data traces, the results show that a DC with our proposed flexible partial execution can shave its peak power consumption and energy bill by \(30.9\%\) and \(20.8\%\) while improving its profit by \(18.8\%\) when comparing against the one with rigid partial execution, i.e., a fixed percentage of requests/workloads can be partially executed, which is commonly employed by today’s DCs.

Keywords

Demand response (DR) Data center (DC) Flexible partial execution Time-varying price discount 

Notes

Acknowledgements

This work was partially funded by Humanities and social sciences project grant number 13XJA790006, China industrial technology association of economic management colleges grant number 16GYJS030 and Xi’an social science planning fund project 17Z06.

References

  1. Al-Ayyoub M, Al-Quraan M, Jararweh Y, Benkhelifa E, Hariri S (2018) Resilient service provisioning in cloud based data centers. Futur Gener Comput Syst 86:765–774.  https://doi.org/10.1016/j.future.2017.07.005 CrossRefGoogle Scholar
  2. Alasseri R, Rao TJ, Sreekanth K (2018) Conceptual framework for introducing incentive-based demand response programs for retail electricity markets. Energy Strateg Rev 19:44–62.  https://doi.org/10.1016/j.esr.2017.12.001 CrossRefGoogle Scholar
  3. Alkhanak EN, Lee SP (2018) A hyper-heuristic cost optimisation approach for scientific workflow scheduling in cloud computing. Futur Gener Comput Syst 86:480–506.  https://doi.org/10.1016/j.future.2018.03.055 CrossRefGoogle Scholar
  4. Balaji M, Kumar CA, Rao GSV (2018) Predictive cloud resource management framework for enterprise workloads. J King Saud Univ Comput Inf Sci 30(3):404–415.  https://doi.org/10.1016/j.jksuci.2016.10.005 CrossRefGoogle Scholar
  5. Cheung H, Wang S, Zhuang C, Gu J (2018) A simplified power consumption model of information technology (it) equipment in data centers for energy system real-time dynamic simulation. Appl Energy 222(15):329–342.  https://doi.org/10.1016/j.apenergy.2018.03.138 CrossRefGoogle Scholar
  6. El Fissaoui M, Beni-Hssane A, Saadi M (2018) Energy efficient and fault tolerant distributed algorithm for data aggregation in wireless sensor networks. J Ambient Intell Hum Comput  https://doi.org/10.1007/s12652-018-0704-8 CrossRefGoogle Scholar
  7. Espadas J, Molina A, Jimnez G, Molina M, Ramrez R, Concha D (2013) A tenant-based resource allocation modelfor scaling software-as-a-service applications over cloud computinginfrastructures. Futur Gener Comput Syst 29(1):273–286.  https://doi.org/10.1016/j.future.2011.10.013 CrossRefGoogle Scholar
  8. Fong S, Li J, Song W, Tian Y, Wong RK, Dey N (2018) Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall. J Ambient Intell Hum Comput 9(4):1197–1221.  https://doi.org/10.1007/s12652-018-0685-7 CrossRefGoogle Scholar
  9. Gao Y, Guan H, Qi Z, Wang B, Liu L (2013) Quality of service aware power management for virtualized data centers. J Syst Archit 59(4–5):245–259.  https://doi.org/10.1016/j.sysarc.2013.03.007 CrossRefGoogle Scholar
  10. Heredia FJ, Cuadrado MD, Corchero C (2018) On optimal participation in the electricity markets of wind power plants with battery energy storage systems. Comput Oper Res 96:316–329.  https://doi.org/10.1016/j.cor.2018.03.004 CrossRefzbMATHGoogle Scholar
  11. Javaid N, Ahmad Z, Sher A, Wadud Z, Khan ZA, Ahmed SH (2018) Fair energy management with void hole avoidance in intelligent heterogeneous underwater WSNs. J Ambient Intell Hum Comput  https://doi.org/10.1007/s12652-018-0765-8 CrossRefGoogle Scholar
  12. Kleineberg KK (2017) Collective navigation of complex networks: participatory greedy routing. Sci Rep 7:2045–2322.  https://doi.org/10.1038/s41598-017-02910-x CrossRefGoogle Scholar
  13. Kumar V, Kumar A (2018) Improved network lifetime and avoidance of uneven energy consumption using load factor. J Ambient Intell Hum Comput  https://doi.org/10.1007/s12652-018-0857-5 CrossRefGoogle Scholar
  14. Li H, Gao B, Chen Z, Zhao Y, Huang P, Ye H, Liu L, Liu X, Kang J (2015a) A learnable parallel processing architecture towards unity of memory and computing. Sci Rep 5:13330.  https://doi.org/10.1038/srep13330 CrossRefGoogle Scholar
  15. Li J, Bao Z, Li Z (2015b) Modeling demand response capability by internet data centers processing batch computing jobs. IEEE Trans Smart Grid 6(2):737–747.  https://doi.org/10.1109/TSG.2014.2363583 CrossRefGoogle Scholar
  16. Liu Z, Lin M, Wierman A, Low SH, Andrew LLH (2011) Greening geographical load balancing. IEEE ACM Trans Netw 39(2):193–204.  https://doi.org/10.1109/TNET.2014.2308295 CrossRefGoogle Scholar
  17. Lounis M, Bounceur A, Euler R, Pottier B (2017) Estimation of energy consumption through parallel computing in wireless sensor networks. J Ambient Intell Hum Comput  https://doi.org/10.1007/s12652-017-0582-5
  18. Maenhaut PJ, Moens H, Volckaert B, Ongenae V, Turck FD (2017) A dynamic tenant-defined storage system for efficient resource management in cloud applications. J Netw Comput Appl 93(1):182–196.  https://doi.org/10.1016/j.jnca.2017.05.014 CrossRefGoogle Scholar
  19. Mamun A, Narayanan I, Wang D, Sivasubramaniam A, Fathy H (2016) Multi-objective optimization of demand response in a datacenter with lithium-ion battery storage. J Energy Storage 7:258–269.  https://doi.org/10.1016/j.est.2016.08.002 CrossRefGoogle Scholar
  20. Muhammad-Bello BL, Aritsugi M (2018) A transparent approach to performance analysis and comparison of infrastructure as a service providers. Comput Electr Eng 69:317–333.  https://doi.org/10.1016/j.compeleceng.2017.12.034 CrossRefGoogle Scholar
  21. Oh E, Kwon Y, Son SY (2017) A new method for cost-effective demand response strategy for apartment-type factory buildings. Energy Build 151(15):275–282.  https://doi.org/10.1016/j.enbuild.2017.06.044 CrossRefGoogle Scholar
  22. Ortiz E, Starnini M, Serrano MA (2017) Navigability of temporal networks in hyperbolic space. Sci Rep 7:15054.  https://doi.org/10.1038/s41598-017-15041-0 CrossRefGoogle Scholar
  23. Piparo D, Tejedor E, Mato P, Mascetti L, Moscicki J, Lamanna M (2018) Swan: a service for interactive analysis in the cloud. Futur Gener Comput Syst 78(3):1071–1078.  https://doi.org/10.1016/j.future.2016.11.035 CrossRefGoogle Scholar
  24. Rahman A, Liu X, Kong F (2014) A survey on geographic load balancing based data center power management in the smart grid environment. IEEE Commun Surv Tutor 16(1):214–233.  https://doi.org/10.1109/SURV.2013.070813.00183 CrossRefGoogle Scholar
  25. Xu H, Li B (2014) Reducing electricity demand charge for data centers with partial execution. In: e-Energy '14 Proceedings of the 5th international conference on Future energy systems, pp 51–61.  https://doi.org/10.1145/2602044.2602048
  26. Zhan Y, Ghamkhari M, Xu D, Ren S, Mohsenian-Rad H (2017) Extending demand response to tenants in cloud data centers via non-intrusive workload flexibility pricing. Dental Mater 33(4):333–343.  https://doi.org/10.1109/TSG.2016.2628886 CrossRefGoogle Scholar
  27. Zhou Y, Wu J, Long C (2018) Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework. Appl Energy 222(15):993–1022.  https://doi.org/10.1016/j.apenergy.2018.02.089 CrossRefGoogle Scholar
  28. Zu Y, Liu C, Dai R, Sharma A, Dong J (2018) Real-time energy-efficient traffic control via convex optimization. Transp Res Part C Emerg Technol 92:119–136.  https://doi.org/10.1016/j.trc.2018.04.017 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Economics and ManagementXi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.Faculty of Electrical & Electronics EngineeringUniversity Malaysia PahangPekanMalaysia
  3. 3.School of CommunicationBeijing Normal UniversityBeijingChina
  4. 4.Key Laboratory of Optical Fiber Sensing and Communications, Ministry of EducationUniversity of Electronic Science and Technology of ChinaChengduChina

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