ANALYZING MOBILE APPLICATION SOFTWARE POWER CONSUMPTION VIA MODEL-DRIVEN ENGINEERING

Chris Thompson, Douglas Schmidt, Hamilton Turner, Jules White

Abstract

Smartphones are mobile devices that travel with their owners and provide increasingly powerful services. The software implementing these services must conserve battery power since smartphones may operate for days without being recharged. It is hard, however, to design smartphone software that minimizes power consumption. For example, multiple layers of abstractions and middleware sit between an application and the hardware, which make it hard to predict the power consumption of a potential application design accurately. Application developers must therefore wait until after implementation (when changes are more expensive) to determine the power consumption characteristics of a design. This paper provides three contributions to the study of applying model-driven engineering to analyze power consumption early in the lifecycle of smartphone applications. First, it presents a model-driven methodology for accurately emulating the power consumption of smartphone application architectures. Second, it describes the System Power Optimization Tool (SPOT), which is a model-driven tool that automates power consumption emulation code generation and simplifies analysis. Third, it empirically demonstrates how SPOT can estimate power consumption to within 3-4% of actual power consumption for representative smartphone applications.

References

  1. Agarwal, Y., Chandra, R., Wolman, A., Bahl, P., Chin, K., and Gupta, R. (2007). Wireless wakeups revisited: energy management for voip over wi-fi smartphones. In ACM MobiSys, volume 7.
  2. Budinsky, F., Steinberg, D., Merks, E., Ellersick, R., and Grose, T. J. (2003). Eclipse Modeling Framework. Addison-Wesley, Reading, MA.
  3. Chen, J., Sivalingam, K., Agrawal, P., and Kishore, S. (1998). A comparison of MAC protocols for wireless local networks based on battery power consumption. In IEEE INFOCOM, volume 1, pages 150-157. Citeseer.
  4. Creus, G. and Kuulusa, M. (2007). Optimizing Mobile Software with Built-in Power Profiling. Mobile Phone Programming and its Application to Wireless Networking, F. Fitzek and F. Reichert, Eds. Springer.
  5. Feeney, L. and Nilsson, M. (2001). Investigating the energy consumption of a wireless network interface in an ad hoc networking environment. In IEEE INFOCOM, volume 3, pages 1548-1557. Citeseer.
  6. Heinzelman, W., Chandrakasan, A., and Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii International Conference on System Sciences, volume 8, page 8020. Citeseer.
  7. Hill, J., Schmidt, D. C., Slaby, J., and Porter, A. (2008). CiCUTS: Combining System Execution Modeling Tools with Continuous Integration Environments. In Proceeedings of 15th Annual IEEE International Conference and Workshops on the Engineering of Computer Based Systems (ECBS), Belfast, Northern Ireland.
  8. Kang, J., Park, C., Seo, S., Choi, M., and Hong, J. (2008). User-centric prediction for battery lifetime of mobile devices. In Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management, pages 531-534. Springer.
  9. Krashinsky, R. and Balakrishnan, H. (2005). Minimizing energy for wireless web access with bounded slowdown. Wireless Networks, 11(1):135-148.
  10. Krause, A., Ihmig, M., Rankin, E., Leong, D., Gupta, S., Siewiorek, D., Smailagic, A., Deisher, M., and Sengupta, U. (2005). Trading off prediction accuracy and power consumption for context-aware wearable computing. In Proceedings of the Ninth IEEE International Symposium on Wearable Computers, pages 20- 26. IEEE Computer Society.
  11. Landsiedel, O., Wehrle, K., and Gotz, S. (2005). Accurate prediction of power consumption in sensor networks. In Proceedings of The Second IEEE Workshop on Embedded Networked Sensors (EmNetS-II). Citeseer.
  12. Lédeczi, Í., Bakay, A., Maroti, M., V ”olgyesi, P., Nordstrom, G., Sprinkle, J., and Karsai, G. (2001). Composing domain-specific design environments. Computer, pages 44-51.
  13. Liu, T., Sadler, C., Zhang, P., and Martonosi, M. (2004). Implementing software on resource-constrained mobile sensors: experiences with impala and zebranet. In Proceedings of the 2nd international conference on Mobile systems, applications, and services, pages 256-269. ACM New York, NY, USA.
  14. Parikh, D., Skadron, K., Zhang, Y., Barcella, M., and Stan, M. (2002). Power issues related to branch prediction. In Proceedings of the Eighth International Symposium on High-Performance Computer Architecture, pages 233-44. Citeseer.
  15. Pering, T., Agarwal, Y., Gupta, R., and Want, R. (2006). Coolspots: Reducing the power consumption of wireless mobile devices with multiple radio interfaces. In Proceedings of the 4th International Conference on Mobile systems, Applications and Services, page 232. ACM.
  16. Schmidt, D. C. (2006). Model-Driven Engineering. IEEE Computer, 39(2):25-31.
  17. Smith, C. and Williams, L. (2001). Performance Solutions: A Practical Guide to Creating Responsive, Scalable Software. Addison-Wesley Professional, Boston, MA, USA.
  18. Thompson, C., White, J., Dougherty, B., and Schmidt, D. (2009). Optimizing Mobile Application Performance with Model-Driven Engineering. In Proceedings of the 7th IFIP Workshop on Software Technologies for Future Embedded and Ubiquitous Systems.
  19. Turner, H., White, J., Thompson, C., Zienkiewicz, K., Campbell, S., and Schmidt, D. (2009). Handbook of Research on Mobility and Computing: Evolving Technologies and Ubiquitous Impacts, chapter Building Mobile Sensor Networks Using Smartphones and Web Services: Ramifications and Development Challenges. IGI Global.
  20. Wang, Q., Hempstead, M., and Yang, W. (2006). A realistic power consumption model for wireless sensor network devices. In Proceedings of the Third Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).
  21. White, J., Clarke, S., Dougherty, B., Thompson, C., and Schmidt, D. (2010). R&D Challenges and Solutions for Mobile Cyber-Physical Applications and Supporting Internet Services. Springer Journal of Internet Services and Applications (to appear).
  22. White, J., Hill, J., Tambe, S., Gray, J., Gokhale, A., and Schmidt, D. C. (2009). Improving Domain-specific Language Reuse through Software Product-line Configuration Techniques. IEEE Software Special Issue: Domain-Specific Languages and Modeling.
Download


Paper Citation


in Harvard Style

Thompson C., Schmidt D., Turner H. and White J. (2011). ANALYZING MOBILE APPLICATION SOFTWARE POWER CONSUMPTION VIA MODEL-DRIVEN ENGINEERING . In Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS, ISBN 978-989-8425-48-5, pages 101-113. DOI: 10.5220/0003372801010113


in Bibtex Style

@conference{peccs11,
author={Chris Thompson and Douglas Schmidt and Hamilton Turner and Jules White},
title={ANALYZING MOBILE APPLICATION SOFTWARE POWER CONSUMPTION VIA MODEL-DRIVEN ENGINEERING},
booktitle={Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,},
year={2011},
pages={101-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003372801010113},
isbn={978-989-8425-48-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,
TI - ANALYZING MOBILE APPLICATION SOFTWARE POWER CONSUMPTION VIA MODEL-DRIVEN ENGINEERING
SN - 978-989-8425-48-5
AU - Thompson C.
AU - Schmidt D.
AU - Turner H.
AU - White J.
PY - 2011
SP - 101
EP - 113
DO - 10.5220/0003372801010113