Skip to main content

Advertisement

SpringerLink
Log in

A Machine Learning Method for Power Prediction on the Mobile Devices

  • Mobile Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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. Dong, W., et al., Enabling efficient reprogramming through reduction of executable modules in networked embedded systems. Ad Hoc Networks 11(1):473–489, 2013.

    Article  Google Scholar 

  2. Alonso, R. S., et al., Implementing a hardware-embedded reactive agents platform based on a service-oriented architecture over heterogeneous wireless sensor networks. Ad Hoc Networks 11.1:151–166, 2013.

    Article  Google Scholar 

  3. Severini, M., et al., Energy-Aware task scheduler for self-powered sensor nodes: From model to firmware. Ad Hoc Networks 24:73–91, 2015.

    Article  Google Scholar 

  4. Huang, K. L., Kanhere, S. S., and Wen, H., On the need for a reputation system in mobile phone based sensing. Ad Hoc Networks 12:130–149, 2014.

    Article  Google Scholar 

  5. Spanos, S., Meliones, A., and Stassinopoulos, G., The internals of advanced interrupt handling techniques: Performance optimization of an embedded Linux network interface. Computer Communications 31.14:3460–3468, 2008.

    Article  Google Scholar 

  6. Trifunovic, S., et al., WLAN-Opp: Ad-hoc-less opportunistic networking on smartphones. Ad Hoc Networks 25:346–358, 2015.

    Article  Google Scholar 

  7. Flinn, J., Satyanarayanan, M., Powerscope: a tool for profiling the energy usage of mobile applications. In Proceedings of Mobile Computing Systems and Applications, 1999. Second IEEE Workshop on 2–10. IEEE, 1999. doi: 10.1109/MCSA.1999.749272.

  8. Wang, G., Hao, J., Ma, J., and Huang, L., A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert Systems with Applications 37(9):6225–6232, 2010. doi:10.1016/j.eswa.2010.02.102.

    Article  Google Scholar 

  9. Han, S.-J., and Cho, S.-B., Evolutionary neural networks for anomaly detection based on the behavior of a program. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(3):559–570, 2005. doi:10.1109/TSMCB.2005.860136.

    Article  Google Scholar 

  10. Zhang, G., Sun, J., Applying fuzzy neural network to intrusion detection based on sequences of system calls. In Proceedings of first international conference Advanced Data Mining and Applications 3584: 483–490. Springer Berlin Heidelberg, 2005. doi: 10.1007/11527503_58.

  11. Fullér, R., Neural fuzzy systems. 1995: 13, 1995.

  12. Mashable GlobalWebIndex, The 10 most frequently used smartphone apps http://mashable.com/2013/08/05/most-used-smartphone-apps/, Q2, 2003.

  13. Freund, Y., Schapire, R. E., and Abe, N., A short introduction to boosting. Journal-Japanese Society for Artificial Intelligence 14:771–780, 1999.

    Google Scholar 

  14. Forrest, S., Hofmeyr, S.A., Somayaji, A., Longstaff, T.A., A sense of self for unix processes. In Proceedings of Security and Privacy, 1996 I.E. Symposium on 120–128. IEEE, 1996. doi: 10.1109/SECPRI.1996.502675.

  15. Liu, Z., Florez, G., Bridges, S.M., A comparison of input representations in neural networks: a case study in intrusion detection. In Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN’02 2: 1708–1703. IEEE, 2002. doi: 10.1109/IJCNN.2002.1007775.

  16. Kai-Feng Chiang (2014) On the Study of Energy Profiling and Scheduling on Multi-core Systems. Master’s thesis, National Taichung University of Science and Technology.

  17. Yaohua Sun, Ting Zhu, Ziguo Zhong, Tian He (2009, November) Energy profiling for mPlatform. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems 407–408. ACM. doi: 10.1145/1644038.1644125

  18. Brooks, D., Tiwari, V., Martonosi, M., Wattch: a framework for architectural-level power analysis and optimizations. In Proceedings of the 27th annual international symposium on Computer architecture 28:(2) 83–94. ACM, 2000. doi: 10.1145/342001.339657.

  19. Burger, D., Austin, T.M., The SimpleScalar tool set, version 2.0. ACM SIGARCH Computer Architecture News 25:(3) 13–25. ACM, 1997. doi: 10.1145/268806.268810.

  20. Jung, W., Kang, C., Yoon, C., Kim, D., Cha, H. DevScope: a nonintrusive and online power analysis tool for smartphone hardware components. In Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis 353–362. ACM, 2012. doi: 10.1145/2380445.2380502.

  21. Qu, G., Kawabe, N., Usami, K., Potkonjak, M., Function-level power estimation methodology for microprocessors. In Proceedings of the 37th Annual Design Automation Conference 810–813. ACM, 2000. doi: 10.1145/337292.337786.

  22. Shin, I., Easwaran, A., Lee, I., Hierarchical scheduling framework for virtual clustering of multiprocessors. In Real-Time Systems, 2008. ECRTS’08. Euromicro Conference on 181–190. IEEE, 2008. doi: 10.1109/ECRTS.2008.28.

  23. Tiwari, V., Malik, S., and Wolfe, A., Power analysis of embedded software: a first step towards software power minimization. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2(4):437–445, 1994. doi:10.1109/92.335012.

    Article  Google Scholar 

  24. Tsao, S.-L., and Chen, J. J., SEProf: A high-level software energy profiling tool for an embedded processor enabling power management functions. Journal of Systems and Software 85(8):1757–1769, 2012. doi:10.1016/j.jss.2012.03.027.

    Article  Google Scholar 

  25. Tongaonkar, A., et al., Towards self adaptive network traffic classification. Computer Communications 56:35–46, 2015.

    Article  Google Scholar 

  26. Salah, K., et al., On Linux starvation of CPU-bound processes in the presence of network I/O. Computers & Electrical Engineering 37(6):1090–1105, 2011.

    Article  Google Scholar 

  27. Montoya, F. G., et al., Comparative analysis of power variables in high performance embedded and x86 architectures using GNU/Linux. Computers & Electrical Engineering 37(4):541–549, 2011.

    Article  Google Scholar 

  28. Krenick, T., and Fabian, S., Data acquisition system for diagnostics of manufacturing system operational states. Annals of Faculty of Engineering Hunedoara Journal of Engineering 7(1):211–214, 2009.

    Google Scholar 

  29. Bishop, R.H., Learning with labview. Prentice Hall, 2014. ISBN: 9780134022123.

  30. Love, R., Linux kernel development. Pearson Education, 2010.

  31. Chen, D.-R., and Chiang, K.-F., Cloud-based power estimation and power-aware scheduling for embedded systems. Accepted, Computers & Electrical Engineering, 2015. doi:10.1016/j.compeleceng.2015.06.004.

    Google Scholar 

  32. Yuan, F., Huanrui, W., Ge Y., Web users’ classification using fuzzy neural network. Knowledge-Based Intelligent Information and Engineering Systems 2313: 1030–1036. Springer Berlin Heidelberg,2004. doi: 10.1007/978-3-540-30132-5_139.

  33. Lee, W., Salvatore, S.J., Data mining approaches for intrusion detection. In Proceedings of the 7th USENIX Security Symposium, 1998. https://www.usenix.org/legacy/publications /library/proceedings/sec98/full_papers/full_papers/lee/lee_html/lee.html.

  34. Carroll, A., Heiser, G., The systems hacker’s guide to the galaxy energy usage in a modern smartphone. Proceedings of the 4th Asia-Pacific Workshop on Systems. ACM, 2013.

  35. Krenick, T., and Fabian, S., Data acquisition system for diagnostics of manufacturing system operational states. Annals of Faculty of Engineering Hunedoara 7(1):211–214, 2009.

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the Ministry of Science and Technology of the Republic of China under grant MOST 102-2221-E-025-003 and MOST 104-2221-E-025-002.

Author information

Authors and Affiliations

  1. Department of Information Management, National Taichung University of Science and Technology, Taichung City, 404, Taiwan, Republic of China

    Da-Ren Chen, Ming-Yang Hsu & Kai-Feng Chiang

  2. Department of Information Management, Hwa Hsia University of Science and Technology, New Taipei City, 235, Taiwan, Republic of China

    You-Shyang Chen

  3. Department of Information Management, National Dong Hwa University, Hualien, 97401, Taiwan, Republic of China

    Lin-Chih Chen

Authors
  1. Da-Ren Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. You-Shyang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin-Chih Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ming-Yang Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kai-Feng Chiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to You-Shyang Chen.

Additional information

This article is part of the Topical Collection on Mobile Systems.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, DR., Chen, YS., Chen, LC. et al. A Machine Learning Method for Power Prediction on the Mobile Devices. J Med Syst 39, 126 (2015). https://doi.org/10.1007/s10916-015-0320-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0320-5

Keywords

Search

Navigation