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A Promise Theory Approach to Collaborative Power Reduction in a Pervasive Computing Environment

  • Mark Burgess
  • Frode Eika Sandnes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4159)

Abstract

A grid-like environment may be constructed from ad hoc processing devices, including portable battery-powered devices. Battery lifetime is a current limitation here. In this paper we propose policies for minimizing power consumption using voluntary collaboration between the autonomously controlled nodes. We exploit the quadratic relationship between processor clock-speed and power consumption to identify processing devices which can be slowed down to save energy while maintaining an overall computational performance across a collaboration of nodes.

Keywords

Power Consumption Power Saving Processing Node Clock Speed Battery Lifetime 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Burgess, M.: An approach to policy based on autonomy and voluntary cooperation. In: Schönwälder, J., Serrat, J. (eds.) DSOM 2005. LNCS, vol. 3775, pp. 97–108. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Burgess, M.: Voluntary cooperation in a pervasive computing environment. In: Proceedings of the Nineteenth Systems Administration Conference (LISA XIX), p. 143. USENIX Association, Berkeley, CA (2005)Google Scholar
  3. 3.
    Benini, L., Bruni, D., Mach, A., Macii, E., Poncino, M.: Discharge current steering for battery lifetime optimization. IEEE Transactions on Computers 52(8), 985–995 (2001)CrossRefGoogle Scholar
  4. 4.
    Benini, L., et al.: Extending lifetime of portable systems by battery scheduling. In: Proceedings of Design, Automation and Test in Europe 2001, pp. 197–201 (2001)Google Scholar
  5. 5.
    Benini, L., et al.: Scheduling battery usage in mobile systems. IEEE Transactions on Very Large Scale Integration (VLSI) System 11(6), 1136–1143 (2003)CrossRefGoogle Scholar
  6. 6.
    Bloom, L., Eardley, R., Geelhoed, E.: Investigating the relationship between battery life and user acceptance of dynamic, energy-aware interfaces on handhelds. In: Brewster, S.A., Dunlop, M.D. (eds.) Mobile HCI 2004. LNCS, vol. 3160, pp. 13–24. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Gurumurthy, S., Sivasubramaniam, A., Kamdemir, M., Franke, H.: Reducing disk power consumption in servers with drpm. IEEE Computer 36(12), 59–66 (2003)Google Scholar
  8. 8.
    Aydin, H., Melhem, R., Mosse, D., Mejia-Alvarez, P.: Power-aware scheduling for periodic real-time tasks. IEEE Transactions on Computers 53(5), 584–600 (2004)CrossRefGoogle Scholar
  9. 9.
    Han, J.-J., Li, Q.-H.: Dynamic power-aware scheduling algorithms for real-time task sets with fault-tolerance in parallel and distributed computing environment. In: Proceedings of 19th IEEE International Parallel and Distributed Processing Symposium, pp. 6–6 (2005)Google Scholar
  10. 10.
    Zhu, D., Melhem, R., Childers, B.: Scheduling with dynamic voltage/speed adjustment using slack reclamation in multi-processor real-time systems. In: Proceedings of 22th IEEE Real-Time Systems Symposium, pp. 84–94 (2001)Google Scholar
  11. 11.
    Zhu, D., Melhem, R., Childers, B.: Scheduling with dynamic voltage/speed adjustment using slack reclamation. IEEE Transactions on Parallel and Distributed Systems 14, 686–700 (2003)CrossRefGoogle Scholar
  12. 12.
    Sinnen, O., Sousa, L., Sandnes, F.E.: Towards a realistic task scheduling model. IEEE Transactions on Parallel and Distributed Systems 17(3), 263–275 (2006)CrossRefGoogle Scholar
  13. 13.
    Sandnes, F.E., Sinnen, O.: A new scheduling algorithm for cyclic graphs. International Journal of High Performance Computing and Networking 3(1), 62–71 (2005)CrossRefGoogle Scholar
  14. 14.
    Sandnes, F.E., Sinnen, O.: Stochastic dfs for multiprocessor scheduling of cyclic taskgraphs. In: Liew, K.-M., Shen, H., See, S., Cai, W. (eds.) PDCAT 2004. LNCS, vol. 3320, pp. 342–350. Springer, Heidelberg (2004)Google Scholar
  15. 15.
    Hecrickx, J.M., et al.: Rigidity and persistence of three and higher dimensional forms. In: Proceedings of the MARS 2005 Workshop on Multi-Agent Robotic Systems, p. 39 (2005)Google Scholar
  16. 16.
    Burgess, M., Fagernes, S.: Pervasive computing management i: A model of network policy with local autonomy. IEEE eTransactions on Network and Service Management, page (submitted)Google Scholar
  17. 17.
    Burgess, M., Fagernes, S.: Pervasive computing management ii: Voluntary cooperation. IEEE eTransactions on Network and Service Management, page (submitted)Google Scholar
  18. 18.
    Axelrod, R.: The Complexity of Cooperation: Agent-based Models of Competition and Collaboration. Princeton Studies in Complexity, Princeton (1997)Google Scholar
  19. 19.
    Axelrod, R.: The Evolution of Co-operation. Penguin Books, 1990 (1984)Google Scholar
  20. 20.
    Hecrickx, J.M., et al.: Structural persistence of three dimensional autonomous formations. In: Proceedings of the MARS 2005 Workshop on Multi-Agent Robotic Systems, p. 47 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mark Burgess
    • 1
  • Frode Eika Sandnes
    • 1
  1. 1.Faculty of EngineeringOslo University CollegeOsloNorway

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