Advertisement

Energy consumption laxity-based quorum selection for distributed object-based systems

  • Tomoya EnokidoEmail author
  • Dilawaer Duolikun
  • Makoto Takizawa
Special Issue
  • 34 Downloads

Abstract

In object based systems, an object is an unit of computation resource. Distributed applications are composed of multiple objects. Objects in an application are replicated to multiple servers in order to increase reliability, availability, and performance. On the other hand, the large amount of electric energy is consumed in a system compared with non-replication systems since multiple replicas of each object are manipulated on multiple servers. In this paper, the energy consumption laxity-based quorum selection (ECLBQS) algorithm is proposed to construct a quorum for each method issued by a transaction so that the total electric energy consumption of servers to perform methods can be reduced in the quorum based locking protocol. The total electric energy consumption of servers, the average execution time of each transaction, and the number of aborted transactions are shown to be more reduced in the ECLBQS algorithm than the random algorithm in evaluation.

Keywords

Quorum-based locking protocol Data management Energy-aware information systems Object-based systems Replication 

References

  1. 1.
    Natural Resources Defense Council (NRDS) (2014) Data center efficiency assessment—scaling up energy efficiency across the data center industry: evaluating key drivers and barriers. http://www.nrdc.org/energy/files/data-center-efficiency-assessment-IP.pdf. Accessed 3 Apr 2015
  2. 2.
    Natural Resources Defense Council (NRDS) (2012) Is cloud computing always greener? Finding the most energy and carbon efficient information technology solutions for small- and medium-sized organizations. http://www.nrdc.org/energy/files/cloud-computing-efficiency-IB.pdf. Accessed 6 Apr 2015
  3. 3.
    Enokido T, Duolikun D, Takizawa M (2015) An extended improved redundant power consumption laxity-based (EIRPCLB) algorithm for energy efficient server cluster systems. World Wide Web 18(6):1629–1630CrossRefGoogle Scholar
  4. 4.
    Tomimori M, Sugawara S (2017) Content sharing method using expected acquisition rate in hybrid peer-to-peer networks with cloud storages. Int J Space Based Situated Comput 7(4):187–196CrossRefGoogle Scholar
  5. 5.
    Tanaka K, Hasegawa K, Takizawa M (2000) Quorum-based replication in object-based systems. J Inf Sci Eng 16(3):317–331Google Scholar
  6. 6.
    Object Management Group Inc. (2012) Common object request broker architecture (CORBA) specification, version 3.3, Part 1—Interfaces. http://www.omg.org/spec/CORBA/3.3/Interfaces/PDF. Accessed 24 Apr 2017
  7. 7.
    Bernstein PA, Hadzilacos V, Goodman N (1987) Concurrency control and recovery in database systems. Addison-Wesley, BostonGoogle Scholar
  8. 8.
    Schneider FB (1993) Replication management using the state-machine approach. Distributed systems, 2nd edn. ACM Press, New YorkGoogle Scholar
  9. 9.
    Gray JN (1978) Notes on database operating systems. Lect Notes Comput Sci 60:393–481CrossRefGoogle Scholar
  10. 10.
    Garcia-Molina H, Barbara D (1985) How to assign votes in a distributed system. J ACM 32(4):814–860MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Khan S, Kolodziej J, Li J, Zomaya AY (2013) Evolutionary based solutions for green computing. Springer, New YorkCrossRefGoogle Scholar
  12. 12.
    Serhan Z, Diab WB (2016) Energy efficient QoS routing and adaptive status update in WMSNs. Int J Space Based Situated Comput 6(3):129–146CrossRefGoogle Scholar
  13. 13.
    Qu X, Peng X (2017) An energy-efficient virtual machine scheduler based on CPU share-reclaiming policy. Int J Grid Util Comput (IJGUC) 6(2):113–120MathSciNetGoogle Scholar
  14. 14.
    Intel Corporation (2010) Intel Xeon Processor 5600 Series: the next generation of intelligent server processors. http://www.intel.com/content/www/us/en/processors/xeon/xeon-5600-brief.html. Accessed 24 Apr 2017
  15. 15.
    Kaushik A, Vidyarthi DP (2018) A hybrid heuristic resource allocation model for computational grid for optimal energy usage. Int J Grid Util Comput (IJGUC) 9(1):51–74CrossRefGoogle Scholar
  16. 16.
    Kataoka H, Nakamura S, Duolikun D, Enokido T, Takizawa M (2017) Multi-level power consumption model and energy-aware server selection algorithm. Int J Grid Util Comput (IJGUC) 8(3):201–210CrossRefGoogle Scholar
  17. 17.
    Duolikun D, Enokido T, Takizawa M (2017) An energy-aware algorithm to migrate virtual machines in a server cluster. Int J Grid Util Comput (IJGUC) 7(1):32–42Google Scholar
  18. 18.
    Sawada A, Kataoka H, Duolikun D, Enokido T, Takizawa M (2016) Energy-aware clusters of servers for storage and computation applications. In: Proceedings of the 30th IEEE international conference on advanced information networking and applications (AINA-2016), pp 400–407Google Scholar
  19. 19.
    Enokido T, Aikebaier A, Takizawa M (2010) A model for reducing power consumption in peer-to-peer systems. IEEE Syst J 4(2):221–229CrossRefGoogle Scholar
  20. 20.
    Enokido T, Aikebaier A, Takizawa M (2011) Process allocation algorithms for saving power consumption in peer-to-peer systems. IEEE Trans Ind Electron 58(6):2097–2105CrossRefGoogle Scholar
  21. 21.
    Enokido T, Aikebaier A, Takizawa M (2014) An extended simple power consumption model for selecting a server to perform computation type processes in digital ecosystems. IEEE Trans Ind Inform 10(2):1627–1636CrossRefGoogle Scholar
  22. 22.
    Enokido T, Takizawa M (2013) Integrated power consumption model for distributed systems. IEEE Trans Ind Electron 60(2):824–836CrossRefGoogle Scholar
  23. 23.
    Enokido T, Takizawa M (2013) The evaluation of the extended transmission power consumption (ETPC) model to perform communication type processes. Computing 95(10–11):1019–1037CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Tomoya Enokido
    • 1
    Email author
  • Dilawaer Duolikun
    • 2
  • Makoto Takizawa
    • 2
  1. 1.Faculty of Business AdministrationRissho UniversityTokyoJapan
  2. 2.Department of Advanced Sciences, Faculty of Science and EngineeringHosei UniversityTokyoJapan

Personalised recommendations