Stochastic Petri Net Models for the Analysis of Trade-Offs in Data Centres with Power Management

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8945)

Abstract

Due to the growth in energy consumption of data centres, the demand for optimal usage of servers has become a relevant topic. This paper contributes to the early design phases of data centres by providing insight into the power-performance trade-off that arises from power management. This paper proposes a flexible set of stochastic Petri net models which can be used easily to study the trade-off between performance and power consumption.

Keywords

Data centre Power management Power Performance Trade-offs Stochastic Petri nets Numerical models Efficiency 

References

  1. 1.
    Koomey, J.G.: Growth in data center electricity use 2005 to 2010. Analytics Press, Oakland (2011)Google Scholar
  2. 2.
    Haverkort, B.R., Postema, B.F.: Towards simple models for energy-performance trade-offs in data centres. In: Fishbach, K., Grossmann, M., Krieger, U.R., Staake, T. (eds.) Proceedings of International Workshop on Demand Modeling and Quantitative Analysis of Future Generation Energy Networks and Energy Efficient Systems, pp. 113–122. University of Bamberg Press (2014)Google Scholar
  3. 3.
    Emerson Network Power: Energy logic: Reducing data center energy consumption by creating savings that cascade across systems. White paper of Emerson Electric Co, pp. 1–19 (2009)Google Scholar
  4. 4.
    Ohara, D.: Sustainable computing: Is it time to turn off your servers? TechNet Magazine (2008)Google Scholar
  5. 5.
    Narayanan, D., Donnelly, A., Rowstron, A.: Write off-loading: practical power management for enterprise storage. ACM Trans. Storage 4(3), 1–23 (2008)CrossRefGoogle Scholar
  6. 6.
    Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., Zhao, F.: Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: Proceedings of 5th USENIX Symposium on Networked Systems Design and Implementation, pp. 337–350 (2008)Google Scholar
  7. 7.
    Haverkort, B.R.: Performance of Computer Communication Systems: A Model-Based Approach. Wiley, New York (1998)CrossRefGoogle Scholar
  8. 8.
    Clark, G., Courtney, T., Daly, D., Deavours, D., Derisavi, S., Doyle, J., Sanders, W., Webster, P.: The Mobius modeling tool. In: Proceedings of 9th International Workshop on Petri Nets and Performance Models, pp. 241–250. IEEE Computer Society (2001)Google Scholar
  9. 9.
    Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. Computer 40(12), 33–37 (2007)CrossRefGoogle Scholar
  10. 10.
    DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: Amazon’s highly available key-value store. ACM SIGOPS Oper. Syst. Rev. 41(6), 205–220 (2007)CrossRefGoogle Scholar
  11. 11.
    Gandhi, A., Harchol-Balter, M., Kozuch, M.A.: Are sleep states effective in data centers? In: Proceedings of International Green Computing Conference, pp. 1–10. IEEE (2012)Google Scholar
  12. 12.
    Ghosh, R., Naik, V.K., Trivedi, K.S.: Power-performance trade-offs in IaaS cloud: a scalable analytic approach. In: Proceedings of 41st International Conference on Dependable Systems and Networks Workshops, pp. 152–157. IEEE (2011)Google Scholar
  13. 13.
    Kuhn, P., Mashaly, M.: Performance of self-adapting power-saving algorithms for ICT systems. In: Proceedings of International Symposium IFIP/IEEE on Integrated Network Management, pp. 720–723 (2013)Google Scholar
  14. 14.
    Bruneo, D., Lhoas, A., Longo, F., Puliafito, A.: Analytical evaluation of resource allocation policies in green IaaS clouds. In: Proceedings of 3rd International Conference on Cloud and Green Computing, pp. 84–91 (2013)Google Scholar
  15. 15.
    Bruneo, D., Longo, F., Puliafito, A.: Modeling energy-aware cloud federations with SRNs. In: Jensen, K., van der Aalst, W.M., Ajmone Marsan, M., Franceschinis, G., Kleijn, J., Kristensen, L.M. (eds.) Transactions on Petri Nets and Other Models of Concurrency VI. LNCS, vol. 7400, pp. 277–307. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  16. 16.
    Gandhi, A., Doroudi, S., Harchol-Balter, M., Scheller-Wolf, A.: Exact analysis of the M/M/k/setup class of Markov chains via recursive renewal reward. In: Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, pp. 153–166 (2013)Google Scholar
  17. 17.
    Basmadjian, R., Niedermeier, F., De Meer, H.: Modelling and analysing the power consumption of idle servers. In: Proceedings of 2nd IFIP Conference on Sustainable Internet and ICT for Sustainability, pp. 1–9 (2012)Google Scholar
  18. 18.
    Lovász, G., Niedermeier, F., Meer, H.: Performance tradeoffs of energy-aware virtual machine consolidation. Cluster Comput. 16(3), 481–496 (2012)CrossRefGoogle Scholar
  19. 19.
    Bostoen, T., Mullender, S., Berbers, Y.: Power-reduction techniques for data-center storage systems. ACM Comput. Surv. 45(3), 1–38 (2011)Google Scholar
  20. 20.
    Bostoen, T., Mullender, S., Berbers, Y.: Analysis of disk power management for data-center storage systems. In: Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet, pp. 1–10 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Centre for Telematics and Information TechnologyUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations