Advertisement

A Distributed Allocation Strategy for Data Mining Tasks in Mobile Environments

  • Carmela Comito
  • Deborah Falcone
  • Domenico Talia
  • Paolo Trunfio
Part of the Studies in Computational Intelligence book series (SCI, volume 446)

Abstract

The increasing computing power of mobile devices has opened the way to perform analysis and mining of data in many real-life mobile scenarios, such as body-health monitoring, vehicle control, and wireless security systems. A key aspect to enable data analysis and mining over mobile devices is ensuring energy efficiency, as mobile devices are battery-power operated. We worked in this direction by defining a distributed architecture in which mobile devices cooperate in a peer-to-peer style to perform a data mining process, tackling the problem of energy capacity shortage by distributing the energy consumption among the available devices. Within this framework, we propose an energy-aware (EA) scheduling strategy that assigns data mining tasks over a network of mobile devices optimizing the energy usage. The main design principle of the EA strategy is finding a task allocation that prolongs network lifetime by balancing the energy load among the devices. The EA strategy has been evaluated through discrete-event simulation. The experimental results show that significant energy savings can be achieved by using the EA scheduler in a mobile data mining scenario, compared to classical time-based schedulers.

Keywords

Mobile Device Mobile Node Network Lifetime Task Allocation Residual Life 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bhargava, R., Kargupta, H., Powers, M.: Energy Consumption in Data Analysis for On-Board and Distributed Applications. In: ICML 2003 (2003)Google Scholar
  2. 2.
    Comito, C., Falcone, D., Talia, D., Trunfio, P.: Energy Efficient Task Allocation over Mobile Networks. In: IEEE CGC 2011, pp. 380–387 (2011)Google Scholar
  3. 3.
    Comito, C., Talia, D., Trunfio, P.: An Energy-Aware Clustering Scheme for Mobile Applications. In: IEEE Scalcom 2011, pp. 15–22 (2011)Google Scholar
  4. 4.
    Garey, R., Johnson, D.: Complexity Bounds for Multiprocessor Scheduling with Resource Constraints. SIAM J. Computing 4, 187–200 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Chang, H.W.D., Oldham, W.J.B.: Dynamic Task Allocation Models for Large Distributed Computing Systems. IEEE Trans. Parallel Distrib. Syst. 6, 1301–1315 (1995)CrossRefGoogle Scholar
  6. 6.
    Li, K., Kumpf, R., Horton, P., Anderson, T.: A Quantitative Analysis of Disk Driver Power Management in Portable Computers. In: Winter 1994 USENIX Conference, pp. 279–292 (1994)Google Scholar
  7. 7.
    Zhuo, J., Chakrabarti, C.: An Efficient Dynamic Task Scheduling Algorithm for Battery Powered DVS Systems. In: ASP-DAC 2005, pp. 846–849 (2005)Google Scholar
  8. 8.
    Zhang, Y., Hu, X., Chen, D.: Task Scheduling and Voltage Selection for Energy Minimization. In: DAC 2002, pp. 183–188 (2002)Google Scholar
  9. 9.
    Aydin, H., Melhem, R., Moss, D., Mejia-Alvarez, P.: Power-Aware Scheduling for Periodic Real-Time Tasks. IEEE Trans. Computers 53(5), 584–600 (2004)CrossRefGoogle Scholar
  10. 10.
    Alsalih, W., Akl, S.G., Hassanein, H.S.: Energy-Aware Task Scheduling: Towards Enabling Mobile Computing over MANETs. In: IPDPS 2005, vol. 242a (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Carmela Comito
    • 1
  • Deborah Falcone
    • 1
  • Domenico Talia
    • 2
  • Paolo Trunfio
    • 1
  1. 1.DEISUniversity of CalabriaRendeItaly
  2. 2.ICAR-CNR and DEISUniversity of CalabriaRendeItaly

Personalised recommendations