Advertisement

Energy Adaptive Computing for a Sustainable ICT Ecosystem

  • Krishna Kant
  • Muthukumar Murugan
  • David Hung Chang Du
Chapter

Abstract

The goal of the Energy Adaptive Computing (EAC) paradigm is to go beyond energy efficiency and address more directly the issue of sustainability of Information and Computing Technology (ICT). This is done by attempting to reduce the carbon footprint of the infrastructure via two mechanisms in addition to the intelligent energy management: (a) replacing the wide-spread overdesign of the infrastructure components with rightsizing coupled with smart control to handle occasional overshoot in resource – particularly the energy-requirements, and (b) operation on renewable sources of energy. Renewable energy sources often have variable output and also require intelligent adaptation to the energy envelop. After a brief introduction to EAC and the corresponding challenges, we describe the design and implementation of energy adaptation mechanisms for data centers with potentially multiple tiers of service. Energy adaptation is realized by intelligent allocation of energy at various levels of the hierarchy, migration of virtual machines among servers, and shutting down of over-provisioned servers. We study both single and multi-tier systems, and provide detailed evaluation of the proposed adaptation techniques. It is shown that energy adaptation could substantially reduce the energy consumption and yet provide the required quality of service.

Keywords

Power Budget Energy Adaptation Time Granularity Thermal Constraint Backup Power 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Heller B, Seetharaman S, Mahadevan P, Yiakoumis Y, Sharma P, Banerjee S, McKeown N (2010) ElasticTree: saving energy in data center networks. In: NSDI’10: Proceedings of the 7th USENIX symposium on networked systems design and implementation, San JoseGoogle Scholar
  2. 2.
    Gurumurthi S, Sivasubramaniam A, Kandemir M, Franke H (2003) DRPM: dynamic speed control for power management in server class disks. SIGARCH Comput Archit News 31(2):169–181CrossRefGoogle Scholar
  3. 3.
    Chang J, Meza J, Ranganathan P, Bash C, Shah A (2010) Green server design: beyond operational energy to sustainability. In: Proceedings of the 2010 international conference on power aware computing and systems, ser. Vancouver Canada. HotPower’10Google Scholar
  4. 4.
    Kant K, Murugan M, Du D (2012) Enhancing data center sustainability through energy adaptive computing. ACM J Emer Technol Comput Syst 8:1–20CrossRefGoogle Scholar
  5. 5.
    Kant K, Murugan M, Du DHC (2011) Willow: a control system for energy and thermal adaptive computing. In: Proceedings of 25th IEEE international parallel & distributed processing symposium, IPDPS’11, AnchorageGoogle Scholar
  6. 6.
    Greenberg S, Mills E, Tschudi B, Rumsey P, Myatt B (2006) Best practices for data centers: lessons learned from benchmarking 22 data centers. ACEEE summer study on energy efficiency in buildings, 2006, Pacific GroveGoogle Scholar
  7. 7.
    Flinn J, Satyanarayanan M(2004) Managing battery lifetime with energy-aware adaptation. ACM Trans Comput Syst 22:137–179CrossRefGoogle Scholar
  8. 8.
    Kant K (2009) Challenges in distributed energy adaptive computing. In: Proceedings of ACM HotMetrics, SeattleGoogle Scholar
  9. 9.
    Krishnan B, Amur H, Gavrilovska A, Schwan K (2011) VM power metering: feasibility and challenges. SIGMETRICS Perform Eval Rev 38:56–60CrossRefGoogle Scholar
  10. 10.
    Kusic D, Kandasamy N, Jiang G (2011) Combined power and performance management of virtualized computing environments serving session-based workloads. IEEE Trans Netw Serv Manage 8(3):245–258CrossRefGoogle Scholar
  11. 11.
    Miettinen AP, Nurminen JK (2010) Energy efficiency of mobile clients in cloud computing. In: HotCloud’10. BostonGoogle Scholar
  12. 12.
    Chun B-G, Ihm S, Maniatis P, Naik M, Patti A (2011) Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of the 6th conference on computer systems, ser. EuroSys’11. ACM, New York, pp 301–314. Available http://doi.acm.org/10.1145/1966445.1966473
  13. 13.
    Cuervo E, Balasubramanian A, Cho D-K, Wolman A, Saroiu S, Chandra R, Bahl P (2010) Maui: making smartphones last longer with code offload. In: Proceedings of the 8th international conference on mobile systems, applications, and services, ser. MobiSys’10. ACM, New York, pp 49–62. Available: http://doi.acm.org/10.1145/1814433.1814441
  14. 14.
    Petrucci V, Loques O, Mossé D (2009) A framework for dynamic adaptation of power-aware server clusters. In: Proceedings of the 2009 ACM symposium on applied computing, ser. SAC’09. Honolulu, HawaiiGoogle Scholar
  15. 15.
    Raj M, Kant K, Das S (2012) Energy adaptive mechanism for P2P file sharing protocols. In: CoreGRID/ERCIM workshop on grids, clouds and P2P computing, ser. EuroPar’12. Rhodes Island, GreeceGoogle Scholar
  16. 16.
    Sharma A, Navda V, Ramjee R, Padmanabhan VN, Belding EM (2009) Cool-tether: energy efficient on-the-fly wifi hot-spots using mobile phones. In: Proceedings of the 5th international conference on emerging networking experiments and technologies, ser. CoNEXT’09. ACM, New York, pp 109–120Google Scholar
  17. 17.
    Friesen DK, Langston MA (1986) Variable sized bin packing. SIAM J Comput 15(1):222–230CrossRefzbMATHGoogle Scholar
  18. 18.
  19. 19.
    Arlitt M, Jin T, 1998 World Cup Web Site Access Logs. http://www.acm.org/sigcomm/ITA/
  20. 20.
    Kant K (1992) Introduction to computer system performance evaluation. McGraw-Hill, New YorkGoogle Scholar
  21. 21.
    Murugan M, Kant K, Du D (2012) Energy adaptation for multi-tiered datacenter applications. Intel Technol J 16: 152–170Google Scholar
  22. 22.
    Barroso LA, Dean J, Hölzle U (2003) Web search for a planet: the Google cluster architecture. IEEE Micro 23:22–28CrossRefGoogle Scholar
  23. 23.
    Dhiman G, Rosing TS (2007) Dynamic voltage frequency scaling for multi-tasking systems using online learning. In: ISLPED’07: proceedings of the 2007 international symposium on Low power electronics and design, PortlandGoogle Scholar
  24. 24.
    NoSQL Databases, http://nosql-database.org/
  25. 25.
    Pinheiro E, Bianchini R (2004) Energy conservation techniques for disk array-based servers. In: Proceedings of the 18th annual international conference on supercomputing, ser. ICS’04. Malo, France ACM, New YorkGoogle Scholar
  26. 26.
    Colarelli D, Grunwald D (2002) Massive arrays of idle disks for storage archives. In: Supercomputing’02: proceedings of the 2002 ACM/IEEE conference on supercomputing. IEEE Computer Society Press, Los Alamitos, Baltimore, pp 1–11Google Scholar
  27. 27.
    Urgaonkar B, Pacifici G, Shenoy P, Spreitzer M, Tantawi A (2005) An analytical model for multi-tier internet services and its applications. In: Proceedings of the 2005 ACM SIGMETRICS international conference on measurement and modeling of computer systems, BanffGoogle Scholar
  28. 28.
    Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall, Englewood CliffsGoogle Scholar
  29. 29.
    Stewart C, Shen K (2009) Some joules are more precious than others: managing renewable energy in the datacenter. In: Workshop on power aware computing and systems (HotPower) Big Sky, MontanaGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Krishna Kant
    • 1
  • Muthukumar Murugan
    • 2
  • David Hung Chang Du
    • 2
  1. 1.George Mason UniversityFairfaxUSA
  2. 2.University of MinnesotaMinneapolisUSA

Personalised recommendations