Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Software for improving the energy efficiency of a computer cluster

  • Published:
Programming and Computer Software Aims and scope Submit manuscript

Abstract

Methods for reducing the energy consumption of a uniform computer cluster due to flexible control strategies of the node states (waking them up or shutting down) and of the execution order of the awaiting tasks are considered. A software system developed in the Institute for System Programming of the Russian Academy of Sciences (ISP RAN) for the dynamic control of the nodes in order to reduce the energy consumption is described. Several strategies for controlling the stats of the nodes are proposed and investigated. Simulation showed that when the average density of tasks1 is 0.5, the energy saving is about 10%. When the density of the flow of tasks decreases, the effect of using the proposed system drastically increases: when the average density is 0.3, the saving is 30%; when the average density is 0.2, the saving is 50%; and when the average density is 0.1, the saving is 70%.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Seager, M., What Are the Future Trends in High-Performance Interconnects for Parallel Computers?, IEEE Symposium on High-Performance Interconnects Panel, 2004.

  2. Feng, W., The Importance of Being Low Power in High-Performance Computing, CTWatch Quarterly, 2005, vol. 1,no. 3, pp. 11–20.

    Google Scholar 

  3. Power Consumption of Supercomputers, TOP500 List Highlights, 2008, June; www.top500.org.

  4. Feng, W. and Hsu, G., Green Destiny and Its Evolving Parts, 19th Int. Supercomputer Conference, Heidelberg, Germany, 2004.

  5. Albers, S., Algorithms for Energy Saving, in Efficient Algorithms: Essays Dedicated to Kurt Mehlhorn on the Occasion of His 60th Birthday, 2009, pp. 173–186.

  6. Albers, S. and Fujiwara, H., Energy-Efficient Algorithms for Flow Time Minimization, Lect. Notes Comput. Sci., 2006, vol. 3884, pp. 621–633.

    Article  MathSciNet  Google Scholar 

  7. Augustine, J., Irani, S., and Swamy, C., Optimal Power-Down Strategies, SIAM J. Comput., 2008, vol. 37, pp. 1499–1516.

    Article  MATH  MathSciNet  Google Scholar 

  8. Irani, S., Shukla, S.K., and Gupta, R., Algorithms for Power Savings, ACM Trans. Algorithms, 2007, vol. 3.

  9. Irani, S. and Pruhs, K., Algorithmic Problems in Power Management, SIGACT News, 2005, vol. 36, no. 2, pp. 63–76.

    Article  Google Scholar 

  10. Zhang, S. and Chatha, K., Approximation Algorithm for the Temperature-aware Scheduling Problem, Proc. of the 2007 IEEE/ACM Int. Conf. on Computer-aided Design (ICCAD’07), Piscataway, NJ: IEEE Press, 2007, pp. 281–288.

    Chapter  Google Scholar 

  11. Moab Cluster Suite, http://www.clusterre-sources.com/solutions/greencomputing.php.

  12. Grushin, D., Kuzyurin, N., Pospelov, A., and Shokurov, A., Grid Behavior Using Workload Data, in Proc. of the 3rd Int. Conf. on Distributed Computing and Grid-Technologies in Sciences and Education, 2008.

  13. Golding, R., Bosch, P., and Wilkes, J., Idleness Is Not Sloth, USENIX Winter Conference, 1995, pp. 201–212.

  14. Karlin, A., Manasse, M., McGeoch, L., and Qwicki, S., Randomized Competitive Algorithms for Nonuniform Problems, ACM-SIAM Symposium on Discrete Algorithms, 1990, pp. 301–309.

  15. Douglis, F., Aceres, R., Kaashoek, F., et al., Storage Alternatives for Mobile Computers, USENIX Symposium on Operating Systems Design and Implementation, 1994, pp. 25–37.

  16. Lu, Y.H. and Micheli, G.D., Adaptive Hard Disk Power Management on Personal Computes, Great Lakes Symposium on VLSI, 1999, pp. 50–53.

  17. Srivastava, M., Chandrakasan, A., and Brodersen, R., Predictive System Shutdown and Other Architecture Techniques for Energy Efficient Programmable Computation, IEEE Trans. VLSI Syst., 1996, vol. 4, pp. 42–55.

    Article  Google Scholar 

  18. Chung, E.Y., Benini, L., and Micheli, G.D., Dynamic Power Management Using Adaptive Learning Tree, Int. Conf. on Computer-Aided Design, 1999, pp. 274–279.

  19. Sheldon, M., Introduction to Probability Models, Academic, 1997.

  20. Chung, E.Y., Benini, L., Bogliolo, A., and Micheli, G.D., Dynamic Power Management for Non-Stationary Service Requests, Design Automation and Test in Europe, 1999, pp. 77–81.

  21. Qiu, Q. and Pedram, M., Dynamic Power Management Based on Continuous-Time Markov Decision Processess, Design Automation Conference, 1999, pp. 555–561.

  22. Hwang, C.H. and Wu, A.C., A Predictive System Shutdown Method for Energy Saving of Event-Driven Computation, Int. Conf. on Computer-Aided Design, 1997, pp. 28–32.

  23. Benini, L., Bogliolo, A., and Micheli, G.D., Policy Optimization for Dynamic Power Management, Computer-Aided Design of Integrated Circuits and Systems, 1999, vol. 18, pp. 813–833.

    Article  Google Scholar 

  24. http://www.clusterresources.com.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. N. Kuzyurin.

Additional information

Original Russian Text © V.P. Ivannikov, D.A. Grushin, N.N. Kuzyurin, A.I. Pospelov, A.V. Shokurov, 2010, published in Programmirovanie, 2010, Vol. 36, No. 6.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ivannikov, V.P., Grushin, D.A., Kuzyurin, N.N. et al. Software for improving the energy efficiency of a computer cluster. Program Comput Soft 36, 327–336 (2010). https://doi.org/10.1134/S0361768810060022

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0361768810060022

Keywords

Navigation