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MAUI: making smartphones last longer with code offload
Abstract
This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.
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From Blockchain leveraged decentralized IoT ehealth framework (2020) · cites this paper as [61]
Citations
Cites
The case for vm-based cloudlets in mobile computing
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.82 · Cited by 3,660
Self-citationLive migration of virtual machines
Clark, C. J., Fraser, K. A., Hand, S., Hansen, J. G., Jul, E., Limpach, C., Pratt, I., & Warfield, A. (2005) · doi:10.5555/1251203.1251223 · Cited by 2,713
Recent advances in rechargeable battery materials: a chemist’s perspective
Palacín, M. R. (2009) · Chemical Society Reviews · doi:10.1039/b820555h · Cited by 1,421
Agile application-aware adaptation for mobility
Noble, B. D., Satyanarayanan, M., Narayanan, D., Tilton, J. E., Flinn, J., & Walker, K. R. (1997) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/268998.266708 · Cited by 740
Bandwidth estimation: metrics, measurement techniques, and tools
Prosad, R., Davrolis, C., Murray, M., & claffy, K. (2003) · IEEE Network · doi:10.1109/mnet.2003.1248658 · Cited by 729
Fine-grained mobility in the Emerald system
Jul, E., Levy, H. M., Hutchinson, N. C., & Black, A. P. (1988) · ACM Transactions on Computer Systems · doi:10.1145/35037.42182 · Cited by 728
Transparent process migration: Design alternatives and the Sprite implementation
Ousterhout, J. K., & Douglis, F. (n.d.) · NASA Technical Reports Server (NASA) · Cited by 446
Augmented smartphone applications through clone cloud execution
Chun, B.-G., & Maniatis, P. (2009) · Cited by 422
The design and implementation of Zap
Osman, S., Subhraveti, D., Su, G., & Nieh, J. (2002) · ACM SIGOPS Operating Systems Review · doi:10.1145/844128.844162 · Cited by 407
Amoeba: a distributed operating system for the 1990s
Mullender, S. J., van Rossum, G., Tananbaum, A. S., van Renesse, R., & van Staveren, H. (1990) · Computer · doi:10.1109/2.53354 · Cited by 405
Research works printed but not linked: 10
- 10.1002/spe.4380210802 · DOI
- 10.5555/874075.876398 · DOI
- 10.5555/850928.851899 · DOI
- 10.5555/826025.826367 · DOI
- P. Kougiouris . Use Reflection to Discover and Assess the Most Common Types in the .NET Framework . http://msdn.microsoft.com/en-us/magazine/cc188926.aspx#S3 , 2002 . P. Kougiouris. Use Reflection to Discover and Assess the Most Common Types in the .NET Framework. http://msdn.microsoft.com/en-us/magazine/cc188926.aspx#S3, 2002.
- U. Kremer , J. Hicks , and J. M. Rehg . Compiler-Directed Remote Task Execution for Power Management . In Proceedings of The Workshop on Compilers and Operating Systems for Low Power (COLP) , Philadelphia, PA , October 2000 . U. Kremer, J. Hicks, and J. M. Rehg. Compiler-Directed Remote Task Execution for Power Management. In Proceedings of The Workshop on Compilers and Operating Systems for Low Power (COLP), Philadelphia, PA, October 2000.
- J. Lewin . i Phone Users 30 Times More Likely To Watch YouTube Videos . http://www.podcastingnews.com/2008/03/19/iphone-users-30-times-watch-youtube-videos/ , 2008 . J. Lewin. iPhone Users 30 Times More Likely To Watch YouTube Videos. http://www.podcastingnews.com/2008/03/19/iphone-users-30-times-watch-youtube-videos/, 2008.
- 10.5555/1060289.1060323 · DOI
- J. Richter . CLR via C#. Microsoft Press ; 2 nd edition, 2006 . J. Richter. CLR via C#. Microsoft Press; 2nd edition, 2006.
- 10.5555/874075.876396 · DOI
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Metadata
- Authors
- Cuervo, E., Balasubramanian, A., Cho, D.-K., Wolman, A., Saroiu, S., Chandra, R., & Bahl, P.
- Affiliations
- Duke University (US)
- University of Massachusetts Amherst (US)
- University of California, Los Angeles (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 8th international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2010-06-15
- Pages
- 49-62
- OpenAlex
- W2101788345
- Cited by
- 2,281
- References
- 35
- Citations by year
- 2026: 10 · 2025: 25 · 2024: 35 · 2023: 50 · 2022: 76 · 2021: 106 · 2020: 155 · 2019: 178 · 2018: 241 · 2017: 247 · 2016: 239 · 2015: 295 · 2014: 237 · 2013: 192 · 2012: 127
- FWCI
- 168.65
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Mobile Systems, Applications, and Services (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Keywords
- mobile code, managed code, runtime optimization, connectivity constraints, face recognition, speech translation
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