Skip to main content
SpringerLink
Log in

On Optimizing the Energy Consumption of Urban Data Centers

  • Chapter
  • First Online:
Hardware Accelerators in Data Centers

  • 889 Accesses

Abstract

With the proliferation of energy-hungry small- and medium-sized data centers in urban contexts and granted the ever-increasing demand for digitalization of increasingly more services, an urgent need for managing the energy consumption of urban data centers has arisen. Such energy consumption management should be subject to limitations of variable elasticity such as the balancing needs of the smart grid, the service level agreements (SLAs) signed between the cloud services providers and their customers, the actual computational capacity of the data centers, and the needs of the data center owners for maximizing profit through service provisioning. The EU research project DOLFIN has proposed an approach towards optimizing the energy consumption of federated data centers by means of continuous resources monitoring and by exploiting flexible SLA renegotiation, coupled with the operation of predictive, multilayered optimization components.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For the present evaluation, only average CPU utilization has been considered, though setting RAM utilization is also allowable through the evaluation framework settings.

  2. 2.

    The semi-randomness is based on the following: for each VM, a pseudo-random numerical ID gets generated and is fed to a sine function to affect the respective period. Next, based on the current emulation time, a value between 0 and 1 is calculated and is multiplied by the CPU/RAM characteristics of the VM, as dictated by its flavor to get the semi-random, to get the CPU/RAM measurements.

References

  1. Data Centre Dynamics (Online). http://www.dataCentredynamics.com/research/market-growth-2011-2012

  2. Index Mundi (2011) Historical data graphs per year (Online). http://www.indexmundi.com/g/g.aspx?v=81&c=us&l=en

  3. Katz R (2009) Tech titans building boom: Google, Microsoft, and other internet giants race to build the mega data centres that will power cloud computing. IEEE Spectr

    Google Scholar 

  4. Hamilton J Internet-scale service efficiency (Online). http://research.microsoft.com/~JamesRH

  5. Bash C, Forman G (2007) Cool job allocation: measuring the power savings of placing jobs at cooling-efficient locations in the data centre

    Google Scholar 

  6. Multi-Tenant Data Centers Need To Play Bigger Energy Efficiency Role (Online). http://goo.gl/vplg4v

  7. Zhuravlev S, Saez J, Blagodurov S, Fedorova A, Prieto M (2013) Survey of energy-cognizant scheduling techniques. IEEE Trans Parallel Distrib Syst 24(7):1447–1464

    Article  Google Scholar 

  8. Orgerie AC, De Assuncao MD, Lefevre L (2014) A survey on techniques for improving the energy efficiency of large scale distributed systems. ACM Comput Surv 46(4)

    Google Scholar 

  9. Weiser M, Welch B, Demers A, Shenker S (1994) Scheduling for reduced cpu energy. In: Proceedings of the 1st USENIX conference on operating systems design and implementation

    Google Scholar 

  10. Ge R, Feng X, Chun Feng W, Cameron K (2007) Cpu miser: a performance-directed, run-time system for power-aware clusters. In: International conference on parallel processing, ICPP 2007

    Google Scholar 

  11. Kim W, Gupta M, Wei G-Y, Brooks D (2008) System level analysis of fast, per-core DVFS using on-chip switching regulators. In: IEEE 14th international symposium on high performance computer architecture

    Google Scholar 

  12. Le Sueur E, Heiser G (2010) Dynamic voltage and frequency scaling:the laws of diminishing returns. In: Proceedings of the 2010 international conference on power aware computing and systems, Berkleley, CA, USA

    Google Scholar 

  13. Lee YC, Zomaya A (2011) Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans Parallel Distrib Syst 22(8):1374–1381

    Article  Google Scholar 

  14. David H, Fallin C, Gorbatov E, Hanebutte UR, Mutlu O (2011) Memory power management via dynamic voltage/frequency scaling. In: Proceedings of the 8th ACM international conference on autonomic computing, New York, NY, USA

    Google Scholar 

  15. Deng Q, Meisner D, Bhattacharjee A, Wenisch TF, Bianchini R (2012) Coscale: coordinating cpu and memory system DVFS in server systems. In: 2012 45th annual IEEE/ACM international symposium on microarchitecture

    Google Scholar 

  16. Mahadevan P, Banerjee S, Sharma P, Shah A, Ranganathan P (2011) On energy efficiency for enterprise and data center networks. Commun Mag 49(8):94–100

    Article  Google Scholar 

  17. Chabarek J, Sommers J, Barford P, Estan C, Tsiang D, Wright S (2008) Power awareness in network design and routing. In: The 27th conference on computer communications (INFOCOM)

    Google Scholar 

  18. Sohan R, Rice A, Moore A, Mansley K (2010) Characterizing 10 gbps network interface energy consumption. In: 2010 IEEE 35th conference on local computer networks (LCN)

    Google Scholar 

  19. Heller B, Seetharaman S, Mahadevan P, Yiakoumis Y, Sharma P, Banerjee S, McKeown N (2010) Elastictree: saving energy in data center networks. In: Proceedings of the 7th USENIX conference on networked systems design and implementation

    Google Scholar 

  20. Mahadevan P, Sharma P, Banerjee S, Ranganathan P (2009) Energy aware network operations. In: Proceedings of the 28th IEEE international conference on computer communications workshops, Piscataway, NJ, USA

    Google Scholar 

  21. Chen G, He W, Liu J, Nath S, Rigas L, Xiaom L, Zhao F (2008) Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: Proceedings of the 5th USENIX symposium on networked systems design and implementation, Berkeley, CA, USA

    Google Scholar 

  22. Voorsluys W, Broberg J, Venugopal S, Buyya R (2009) Cost of virtual machine live migration in clouds: a performance evaluation. In: Proceedings of the 1st international conference on cloud computing

    Google Scholar 

  23. Beloglazov A, Buyya R (2010) Energy efficient allocation of virtual machines in cloud data centers. In: 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing (CCGrid)

    Google Scholar 

  24. Ghribi C, Hadji M, Zeghlache D (2013) Energy efficient vm scheduling for cloud data centers: exact allocation and migration algorithms. In: 2013 13th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid)

    Google Scholar 

  25. Mandal U, Habib MF, Zhang S, Mukherjee B, Tornatore M (2013) Greening the cloud using renewable-energy-aware service migration. IEEE Netw 27(6):36–43

    Article  Google Scholar 

  26. DOLFIN Project Number: 609140; Strategic objective: FP7-SMARTCITIES-2013(ICT-2013.6.2) (Online). http://www.dolfin-fp7.eu/

  27. GEYSER Project Number: 609211; Strategic objective: FP7-SMARTCITIES-2013(ICT-2013.6.2) (Online)

    Google Scholar 

  28. Bash C, Forman G (2007) Cool job allocation: measuring the power savings of placing jobs at cooling-efficient locations in the data centre. In: ATC’07 2007 USENIX annual technical conference on proceedings of the USENIX annual technical conference

    Google Scholar 

  29. Bohrer P, Elnozahy EN, Keller T, Kistler M, Lefurgy C, McDowell C, Rajamony R (2002) The case for power management in web servers. In: Power aware computing, Kluwer Academic Publishers, Norwell, MA

    Google Scholar 

  30. Fan X, Weber W-D, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: Proceedings of the ACM international symposium on computer architecture, San Diego, CA, USA

    Google Scholar 

  31. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. W.H.Freeman and Company, New York, NY

    MATH  Google Scholar 

  32. Openstack Foundation Openstack (Online)

    Google Scholar 

  33. OpenNebula OpenNebula (Online)

    Google Scholar 

  34. Eucalyptus (Online)

    Google Scholar 

  35. DOLFIN Evaluation framework source code (Online). https://stash.i2cat.net/scm/dol/evaluation_framework.git

  36. APC Calculating total cooling requirements for data centers (Online). http://apcmedia.com/salestools/nran-5te6he/nran-5te6he_r3_en.pdf. Accessed July 2016

Download references

Author information

Authors and Affiliations

  1. Synelixis, Chalkida, Greece

    Artemis C. Voulkidis & Theodore Zahariadis

  2. Singular Logic, Kifisia, Greece

    Terpsichori Helen Velivassaki

Authors
  1. Artemis C. Voulkidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Terpsichori Helen Velivassaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Theodore Zahariadis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Artemis C. Voulkidis .

Editor information

Editors and Affiliations

  1. Microprocessors and Digital Systems Lab, National Technical University of Athens, Athens, Greece

    Christoforos Kachris

  2. IC IINFCOM PARSA, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Babak Falsafi

  3. National Technical University of Athens, Athens, Greece

    Dimitrios Soudris

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Voulkidis, A.C., Velivassaki, T.H., Zahariadis, T. (2019). On Optimizing the Energy Consumption of Urban Data Centers. In: Kachris, C., Falsafi, B., Soudris, D. (eds) Hardware Accelerators in Data Centers. Springer, Cham. https://doi.org/10.1007/978-3-319-92792-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-92792-3_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92791-6

  • Online ISBN: 978-3-319-92792-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation