Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Online learning based on a novel cost function for system power management

A novel system power management technique is proposed that employs a novel cost function based on state-action. Compared with the conventional algorithm, by using multiple parameter constraints in cost function of power management framework, the improved Q-learning can effectively make decisions to achieve a rational optimisation room. The proposed power management framework does not need any prior data and is running on a power model. As uncertainties can be effectively captured and modelled, the framework based on the model can help to explore an ideal trade-off and converge to the best power management policy. The results obtained showed that improved algorithm achieved remarkable significance.

References

    1. 1)
      • 9. Smullen, C.W., Coffman, J., Gurumurthi, S.: ‘Accelerating enterprise solid-state disks with nonvolatile merge caching’. Int. Conf. on Green Computing, Chicago, IL, USA, 2010, pp. 203214.
    2. 2)
      • 11. Azevedo, A., Issenin, I., Cornea, R., et al: ‘Profile-based dynamic voltage scheduling using program checkpoints’. IEEE Conf. on Design, Automation and Test in Europe, Paris, France, 2002, pp. 168175.
    3. 3)
      • 10. Zhu, Y., Mueller, F.: ‘Feedback EDF scheduling of real-time tasks exploiting dynamic voltage scaling’, Real-Time Syst., 2005, 31, pp. 3363.
    4. 4)
      • 1. Langen, P., Juurlink, B.: ‘Leakage-aware multiprocessor scheduling for low power’, J. Signal Process. Syst., 2006, 57, pp. 7380.
    5. 5)
      • 20. MediaBench: Available athttp://euler.slu.edu/~fritts/mediabench/.
    6. 6)
      • 17. Li, C., Wang, R., Goswami, N., et al: ‘Chameleon: adapting throughput server to time-varying green power budget using online learning’. IEEE Int. Symp. on Low Power Electronics and Design, Beijing, China, 2013, pp. 100105.
    7. 7)
      • 16. Cai, L., Pettis, N., Lu, Y.: ‘Joint power management of memory and disk under performance constraints’, IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., 2006, 25, pp. 26972711.
    8. 8)
      • 6. Jung, H., Pedram, M.: ‘Dynamic power management under uncertain information’. EDA Consortium Conf. on Design, Automation and Test, Nice, France, 2007, pp. 10601065.
    9. 9)
      • 14. Tan, Y., Malani, P., Qiu, Q., et al: ‘Workload prediction and dynamic voltage scaling for MPEG decoding’. IEEE Conf. on Design Automation, Yokohama, Japan, 2006, pp. 911916.
    10. 10)
      • 4. Jejurikar, R., Pereira, C., Gupta, R.: ‘Leakage aware dynamic voltage scaling for real-time embedded systems’. Design Automation Conf., San Diego, USA, 2004, pp. 275280.
    11. 11)
      • 19. Fujishiqe, S.: ‘Submodular functions and optimization’, vol. 58 (Elsevier Science, Elsevier, 2005).
    12. 12)
      • 5. Lie, M., Wang, W.S., Orshansky, M.: ‘Leakage power reduction by dual-Vth designs under probabilistic analysis of Vth variation’. IEEE Int. Symp. on Low Power Electronics and Design, California, USA, 2004, pp. 27.
    13. 13)
      • 18. Hao, S., Lu, J., Qiu, Q.: ‘Learning based DVFS for simultaneous temperature, performance and energy management’. Int. Symp. on Quality Electronic Design, Santa Clara, CA, USA, 2012, pp. 747754.
    14. 14)
      • 15. Coskun, A.K., Rosing, T.S., Gross, K.C.: ‘Utilizing predictors for efficient thermal management in multiprocessor SoCs’, IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., 2009, 28, pp. 15031516.
    15. 15)
      • 12. Ge, Y., Qiu, Q.: ‘Dynamic thermal management for multimedia application using machine leaning’. IEEE Conf. Design Automation, New York, USA, 2011, pp. 95100.
    16. 16)
      • 2. Tsai, Y.F., Vijaykrishnan, N., Xie, Y., et al: ‘Influence of leakage reduction techniques on delay/leakage uncertainty’. IEEE Conf. on VLSI Design, Kolkata, India, 2005, pp. 374379.
    17. 17)
      • 13. Chung, E.Y., Micheli, G.D., Benini, L.: ‘Contents provider-assisted dynamic voltage scaling for low energy multimedia applications’. ACM Int. Symp. on Low Power Electronics and Design, Monterey, CA, USA, 2002, pp. 4247.
    18. 18)
      • 8. Agarwal, Y., Savage, S., Gupta, R.: ‘Sleepserver: a software-only approach for reducing the energy consumption of PCs within enterprise environments’. Usenix Conf. on Usenix Technical Conf., Boston, MA, USA, 2010.
    19. 19)
      • 7. Dhiman, G., Rosing, T.S.: ‘System-level power management using online learning’, IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., 2009, 28, pp. 676689.
    20. 20)
      • 3. Basu, A., Lin, S.C., Wason, V., et al: ‘Simultaneous optimization of supply and threshold voltages for low-power and high-performance circuits in the leakage dominate era’. ACM Conf. on Design Automation, San Diego, USA, 2004, vol. 43, pp. 884887.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cdt.2017.0211
Loading

Related content

content/journals/10.1049/iet-cdt.2017.0211
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address