Skip to main content

Advertisement

Log in

Using priced timed automaton to analyse the energy consumption in cloud computing environment

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

According to the fact that cloud servers have different energy consumption on different running states, as well as the energy waste problem caused by the mismatching between cloud servers and cloud tasks, we carry out researches on the energy optimal method achieved by a priced timed automaton for the cloud computing center in this paper. The priced timed automaton is used to model the running behaviors of the cloud computing system. After introducing the matching matrix of cloud tasks and cloud resources as well as the power matrix of the running states of cloud servers, we design a generation algorithm for the cloud system automaton based on the generation rules and reduction rules given ahead. Then, we propose another algorithm to settle the minimum path energy consumption problem in the cloud system automaton, therefore obtaining an energy optimal solution and an energy optimal value for the cloud system. A case study and repeated experimental analyses manifest that our method is effective and feasible.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. J. Glanz. Google details, and defends, its use of electricity. NewYork Times[Online], http://www.nytimes.com/2011/09/09/technology/google-details-and-defends-its-use-of-electricity.html. Sept 2011

  2. Pedram, M.: Energy-efficient datacenters. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 31(10), 1465–1484 (2012)

    Article  Google Scholar 

  3. Chuang, Lin, Yuan, Tian, Min, Yao: Green network and green evaluation: Mechanism, modeling and evaluation. Chin. J. Comput. 34(4), 593–612 (2011)

    Article  Google Scholar 

  4. Venkatachalam, V., Franz, M.: Power reduction techniques for microprocessor Systems. ACM Comput. Surv. 37(3), 195–237 (2005)

    Article  Google Scholar 

  5. K. H. Kim, R. Buyya, J. Kim. Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clusters. In: Seventh IEEE International Symposium on Cluster Computing and the Grid, 2007, pp. 541–548

  6. Goh, L.K., Veeravalli, B., Viswanathan, S.: Design of fast and efficient energy-aware gradient-based scheduling algorithms. IEEE Trans. Parallel Distrib. Syst. 20(1), 1–12 (2009)

    Article  Google Scholar 

  7. Lee, Y.C., Zomaya, A.Y.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22(8), 1374–1381 (2011)

    Article  Google Scholar 

  8. Yi-Ming, Tan, Guo-Sun, Zeng, Wei, Wang: Policy of energy optimal management for cloud computing platform with stochastic tasks. J. Softw. 23(2), 266–278 (2012)

    Article  Google Scholar 

  9. Rasmussen, J.I., Larsen, K.G., Subramani, K.: On using priced timed automata to achieve optimal. Form. Methods Syst. Des. 29(1), 97–114 (2006)

    Article  MATH  Google Scholar 

  10. A. Lungu, P. Bose, et al. Multicore power management: Ensuring robustness via early-stage formal verification. In:Proceeding of the Seventh IEEE/ACM Internatinal Conference on Formal Methods Models Codesign, 2009, pp. 78–87

  11. Nocco, S., Quer, S.: A novel SAT-based approach to the task graph cost-optimal scheduling problem. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 29(12), 2027–2040 (2010)

    Article  Google Scholar 

  12. Alur, R., Dill, D.L.: A theory of timed automata. Theor. Comput. Sci. 126(2), 183–235 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  13. Behrmann, G., Fehnker, A., et al.: Minimum-cost reachability for priced timed automata. Lect. Notes Comput. Sci. 2034, 147–161 (2001)

    Article  Google Scholar 

  14. Kwok, Y.K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. 31(4), 406–471 (1999)

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the anonymous reviewers for their insightful comments and suggestions. The research supported by the National High Technology Research and Development Program of China (863 program) under Grant of 2009AA012201, the National Natural Science Foundation of China under Grant of 61272107, 61202173, and 61103068, the Program of Shanghai Subject Chief Scientist under grant of 10XD1404400, the special Fund for Fast Sharing of Science Paper in Net Era by CSTD under Grant of 20110740001.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhigang Deng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, Z., Zeng, G., He, Q. et al. Using priced timed automaton to analyse the energy consumption in cloud computing environment. Cluster Comput 17, 1295–1307 (2014). https://doi.org/10.1007/s10586-014-0378-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-014-0378-8

Keywords

Navigation