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Energy consumption in mobile phones: a measurement study and implications for network applications
Abstract
In this paper, we present a measurement study of the energy consumption characteristics of three widespread mobile networking technologies: 3G, GSM, and WiFi. We find that 3G and GSM incur a high tail energy overhead because of lingering in high power states after completing a transfer. Based on these measurements, we develop a model for the energy consumed by network activity for each technology.Using this model, we develop TailEnder, a protocol that reduces energy consumption of common mobile applications. For applications that can tolerate a small delay such as e-mail, TailEnder schedules transfers so as to minimize the cumulative energy consumed meeting user-specified deadlines. We show that the TailEnder scheduling algorithm is within a factor 2x of the optimal and show that any online algorithm can at best be within a factor 1.62x of the optimal. For applications like web search that can benefit from prefetching, TailEnder aggressively prefetches several times more data and improves user-specified response times while consuming less energy. We evaluate the benefits of TailEnder for three different case study applications - email, news feeds, and web search - based on real user logs and show significant reduction in energy consumption in each case. Experiments conducted on the mobile phone show that TailEnder can download 60% more news feed updates and download search results for more than 50% of web queries, compared to using the default policy.
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Cited by
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Mobile Data Offloading through Opportunistic Communications and Social Participation
Han, B., Hui, P., Kumar, V. S. A., Marathe, M. V., Shao, J., & Srinivasan, A. (2012) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2011.101 · Cited by 489
A first look at traffic on smartphones
Falaki, H., Lymberopoulos, D., Mahajan, R., Kandula, S., & Estrin, D. (2010) · Proceedings of the 10th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1879141.1879176 · Cited by 388
Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services
Shojafar, M., Cordeschi, N., & Baccarelli, E. (2019) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2016.2551747 · Cited by 339
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud
Wang, J., Zhang, J., Bao, W., Zhu, X., Cao, B., & Yu, P. S. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220106 · Cited by 188
Metadata
- Authors
- Balasubramanian, N., Balasubramanian, A., & Venkataramani, A.
- Venue
- Proceedings of the 9th ACM SIGCOMM conference on Internet measurement
- Publisher
- ACM
- Published
- 2009-11-04
- Pages
- 280-293
- OpenAlex
- W2049577911
- Cited by
- 1,101
- References
- 0
- FWCI
- 101.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Balasubramanian, N., Balasubramanian, A., & Venkataramani, A. (2009). Energy consumption in mobile phones: a measurement study and implications for network applications. Proceedings of the 9th ACM SIGCOMM conference on Internet measurement. https://doi.org/10.1145/1644893.1644927
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
