Skip to main content

Advertisement

Log in

An extended improved redundant power consumption laxity-based (EIRPCLB) algorithm for energy efficient server cluster systems

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

In cloud computing systems, server cluster systems are used to provide flexible, scalable, and fault-tolerant application services. One way to provide a fault-tolerant application service is that multiple replicas of each application process are performed on multiple servers in a server cluster. However, a large amount of electric energy is consumed in a server cluster. Hence, it is critical to discuss how to make information systems not only fault-tolerant but also energy-efficient. In our previous studies, the extended improved redundant power consumption laxity-based (EIRPCLB) algorithm is proposed to reduce the total energy consumption of a server cluster to redundantly perform application processes. Once a replica successfully terminates on one server, replicas being or to be performed on other servers are meaningless. In the EIRPCLB algorithm, the total energy consumption of a server cluster can be reduced by forcing meaningless replicas to terminate and differentiating the starting time of each replica. In this paper, we evaluate the EIRPCLB algorithm in terms of total energy consumption and the average response time in homogeneous and heterogeneous clusters. We make clear how the total energy consumption of a server cluster and response time of each process change according to the change of inter-arrival time of request processes, inter-request time of replicas, redundancy of each process, and delay time between servers.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Barolli, L., Xhafa, F.: Jxta-overlay: a P2P platform for distributed, collaborative and ubiquitous computing. IEEE Trans. Ind. Electron. 58(6), 2063–2172 (2011)

    Article  Google Scholar 

  2. Calheiros, R.N., Buyya, R.: Meeting deadlines of scientific workflows in public clouds with tasks replication. IEEE Trans. Parallel Distrib. Syst. 25(7), 1787–1796 (2014)

    Article  Google Scholar 

  3. Enokido, T., Aikebaier, A., Takizawa, M.: A model for reducing power consumption in peer-to-peer systems. IEEE Syst. J. 4(2), 221–229 (2010)

    Article  Google Scholar 

  4. Enokido, T., Aikebaier, A., Takizawa, M.: Process allocation algorithms for saving power consumption in peer-to-peer systems. IEEE Trans. Ind. Electron. 58(6), 2097–2105 (2011)

    Article  Google Scholar 

  5. Enokido, T., Aikebaier, A., Takizawa, M.: An extended simple power consumption model for selecting a server to perform computation type processes in digital ecosystems. IEEE Trans. Ind. Inform. 10(2), 1627–1636 (2014)

    Article  Google Scholar 

  6. Enokido, T., Aikebaier, A., Takizawa, M.: Evaluation of the extended improved redundant power consumption laxity-based (EIRPCLB) algorithm. In: Proceedings of the IEEE 28th International Conference on Advanced Information Networking and Applications (AINA-2014), pp. 940–947 (2014)

  7. Enokido, T., Aikebaier, A., Takizawa, M.: Energy-efficient redundant execution of processes in a fault-tolerant cluster of servers. Int. J. Parallel Program. 42(5), 789–819 (2014)

    Google Scholar 

  8. Enokido, T., Takizawa, M.: The evaluation of the extended transmission power consumption (ETPC) model to perform communication type processes. Computing 95(10–11), 1019–1037 (2013)

    Article  Google Scholar 

  9. Enokido, T., Takizawa, M.: Integrated power consumption model for distributed systems. IEEE Trans. Ind. Electron 60(2), 824–836 (2013)

    Article  Google Scholar 

  10. Gracia-Tinedo, R., Artigas, M.S., Moreno-Martinez, A., Cotes, C., Lopez, P.G.: Actively measuring personal cloud storage. In: Proceedings of the 6th International Conference on Cloud Computing (CLOUD), pp. 121–126 (2013)

  11. Grossman, R.L.: The case for cloud computing. IT Prof. 11(2), 23–27 (2010)

    Article  Google Scholar 

  12. Google: Google green. http://www.google.com/green/the-big-picture.html (2012)

  13. Hemmert, S.: Green HPC: from nice to necessity. Comput. Sci. Eng. 12(6), 8–10 (2010)

    Article  Google Scholar 

  14. Inoue, T., Aikebaier, A., Enokido, T., Takizawa, M.: Algorithms for selecting energy-efficient storage servers in storage and computation oriented applications. In: Proceedings of IEEE the 26th International Conference on Advanced Information Networking and Applications (AINA-2012), pp. 920–927 (2012)

  15. Khan, S., Kolodziej, J., Li, J., A. Z.: Evolutionary Based Solutions for Green Computing. Springer (2013)

  16. Kiani, S.L., Anjum, A., Bessis, N., Hill, R., Knappmeyer, M.: Energy conservation in mobile devices and applications: a case for context parsing, processing and distribution in clouds. Mob. Inf. Syst. 9(1), 1–17 (2013)

    Google Scholar 

  17. Kim, K.H.: Reward-based allocation of cluster and grid resources for imprecise computation model-based applications. Int. J. Web Grid Serv. (IJWGS) 9(2), 146–171 (2013)

    Article  Google Scholar 

  18. Lamport, R., Shostak, R., Pease, M.: The byzantine generals problems. ACM Trans. Program. Lang. Syst. 4(3), 382–401 (1982)

    Article  MATH  Google Scholar 

  19. LVS project: Job scheduling algorithms in linux virtual server. http://www.linuxvirtualserver.org/docs/scheduling.html (2010)

  20. Murphy, R., Sterling, T., Dekate, C.: Advanced architectures and execution models to support green computing. Comput. Sci. Eng. 12(6), 38–47 (2010)

    Article  Google Scholar 

  21. Tao, F., Li, Y.L., Xu, L., Zhang, L.: FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans. Ind. Inform. 9(4), 2023–2033 (2013)

    Article  Google Scholar 

  22. Yan, J., Vyatkin, V.: Distributed software architecture enabling peer-to-peer communicating controllers. IEEE Trans. Ind. Electron. 9(4), 2200–2209 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tomoya Enokido.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Enokido, T., Duolikun, D. & Takizawa, M. An extended improved redundant power consumption laxity-based (EIRPCLB) algorithm for energy efficient server cluster systems. World Wide Web 18, 1603–1629 (2015). https://doi.org/10.1007/s11280-014-0315-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-014-0315-z

Keywords

Navigation