Skip to main content

Energy Efficient Resource Utilization: Architecture for Enterprise Network

Towards Reliability with SleepAlert

  • Conference paper
  • First Online:
Book cover Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1228))

Included in the following conference series:

  • 1080 Accesses

Abstract

Enterprise networks usually require all the computing machines to remain accessible (switched-on) at all times regardless of the workload in order to entertain user requests at any instant. This comes at the cost of excessive energy utilization. Many solutions have been put forwarded, however, only few of them are tested in a real-time environment, where the energy saving is achieved by compromising the systems’ reliability. Therefore, energy-efficient resource utilization without compromising the system’s reliability is still a challenge. In this research, a novel architecture, “Sleep Alert”, is proposed that not only avoids the excessive energy utilization but also improves the system reliability by using Resource Manager (RM) concept. In contrary to traditional approaches, Primary and Secondary Resource Managers i.e. RMP and RMS respectively are used to avoid the single point of failure. The proposed architecture is tested on a network where active users were accessing the distributed virtual storage and other applications deployed on the desktop machines, those are connected with each other through a peer-to-peer network. Experimental results show that the solution can save considerable amount of energy while making sure that reliability is not compromised. This solution is useful for small enterprise networks, where saving energy is a big challenge besides reliability.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. US Department of Energy Efficiency and Renewable Energy. http://www.eere.energy.gov/

  2. InternetWorldStats. http://www.internetworldstats.com

  3. Network World. http://www.networkworld.com

  4. Wang, D.: Meeting green computing challenges. In: 10th Electronics Packaging Technology Conference. Teradata Corporation, USA. IEEE (2008)

    Google Scholar 

  5. Okaor Kennedy, C., Udeze Chidiebele, C., Okafor, E.C.N., Okezie, C.C.: Smart grids: a new framework for efficient power management in datacenter networks. In: IJACSA, vol. 3, no. 7, pp. 59–66 (2012)

    Google Scholar 

  6. Gyarmati, L., Anh Trinh, T.: How can architecture help to reduce energy consumption in data center networking? e-Energy (2010)

    Google Scholar 

  7. Marcos, A.: A survey on techniques for improving the energy efficiency of large scale distributed systems. ACM Comput. Surv. 46(4), 1–35 (2014)

    Google Scholar 

  8. Chen, X., Li, C., Jiang, Y.: Optimization model and algorithm for energy efficient virtual node embedding. IEEE Commun. Lett. 19, 1327–1330 (2015). ISSN 1089-7798

    Article  Google Scholar 

  9. Panarello, C., et al.: Energy saving and network performance: a trade-off approach. e-Energy (2010)

    Google Scholar 

  10. Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A., Khan, S.U., Zomaya, A.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98, 1–24 (2014). https://doi.org/10.1007/s00607-014-0407-8

    Article  MathSciNet  Google Scholar 

  11. Choi, K., Soma, R., Pedram, M.: Dynamic voltage and frequency scaling based on workload decomposition. Department of EE-Systems, University of Southern California, Los Angeles, CA 90089

    Google Scholar 

  12. Green Manufacturing Initiative, Annual Report (2012). http://www.wmich.edu/mfe/mrc/greenmanufacturing/pdf/2012%20GMI%20Annual%20Report.pdf

  13. Reich, J., Goraczko, M., Kansal, A., Padhye, J.: Sleepless in seattle no longer. In: Proceedings of the 2010 USENIX Conference, Columbia University, Microsoft Research, June 2010

    Google Scholar 

  14. Nedevschi, S., Popa, L., Iallaccone, G., Ratnasamy, S., Wetherall, D.: Reducing network energy consumption via sleeping and rate-adaptation. In: NSDI 2008, Berkeley, CA, USA (2008)

    Google Scholar 

  15. Apple Wake On Lan. http://www.macworld.com/article/142468/2009/08/wake_on_demand.html

  16. Agarwal, Y., Hodges, S., Chandra, R., Scott, J., Bahl, P., Gupta, R.: Somniloquy: augmenting network interfaces to reduce pc energy usage. In: NSDI 2009, Berkeley, CA, USA (2009)

    Google Scholar 

  17. Gumstix. http://www.gumstix.com

  18. Wake on Lan. http://en.wikipedia.org/wiki/Wake-on-LAN

  19. Kant, K.: Data center evolution: a tutorial on state of the art, issues, and challenges. Comput. Netw. 53, 2939–2965 (2009)

    Article  Google Scholar 

  20. Koomey, J.: Growth in data center electricity use 2005 to 2010. A report by Analytical Press, completed at the request of The New York Times (2011)

    Google Scholar 

  21. Borah, J., Singh, S.K., Borah, A.D.: Cellular base station and its greening issues. Int. J. Adv. Electron. Commun. Syst. (CSIR-NISCAIR Approved) 3(2), 1–4 (2014)

    Google Scholar 

  22. Chabarek, J., Sommers, J., Barford, P., Estan, C., Tsiang, D., Wright, S.: Power awareness in network design and routing. In: The 27th Conference on Computer Communications IEEE INFOCOM 2008, pp. 457–465 (2008)

    Google Scholar 

  23. Ghani, I., Niknejad, N., Jeong, S.R.: Energy saving in green cloud computing data centers: a review. J. Theor. Appl. Inf. Technol. 74(1) (2015)

    Google Scholar 

  24. Song, Y., Wang, H., Li, Y., Feng, B., Sun, Y.: Multi-tiered on-demand resource scheduling for vm-based data center. In: Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 148–155 (2009)

    Google Scholar 

  25. Cardosa, M., Korupolu, M., Singh, A.: Shares and utilities based power consolidation in virtualized server environments. In: Proceedings of IFIP/IEEE Integrated Network Management (IM) (2009)

    Google Scholar 

  26. Beloglazov, A., Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (2010). https://doi.org/10.1109/ccgrid.2010.46

  27. Liu, C., Liu, C., Shang, Y., Chen, S., Cheng, B., Chen, J.: An adaptive prediction approach based on workload pattern discrimination in the cloud. J. Netw. Comput. Appl. (2016, in Press)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dilawar Ali .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ali, D., Raja, F.R., Saleem, M.A. (2020). Energy Efficient Resource Utilization: Architecture for Enterprise Network. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1228. Springer, Cham. https://doi.org/10.1007/978-3-030-52249-0_2

Download citation

Publish with us

Policies and ethics