ABSTRACT
Software energy consumption is a relatively new concern for mobile application developers. Poor energy performance can harm adoption and sales of applications. Unfortunately for the developers, the measurement of software energy consumption is expensive in terms of hardware and difficult in terms of expertise. Many prior models of software energy consumption assume that developers can use hardware instrumentation and thus cannot evaluate software running within emulators or virtual machines. Some prior models require actual energy measurements from the previous versions of applications in order to model the energy consumption of later versions of the same application.
In this paper, we take a big-data approach to software energy consumption and present a model that can estimate software energy consumption mostly within 10% error (in joules) and does not require the developer to train on energy measurements of their own applications. This model leverages a big-data approach whereby a collection of prior applications' energy measurements allows us to train, transmit, and apply the model to estimate any foreign application's energy consumption for a test run. Our model is based on the dynamic traces of system calls and CPU utilization.
- Chromeshell apks. http://commondatastorage. googleapis.com/chromium-browser-continuous/index.html?prefix=Android/. (last accessed: 2015-May-22).Google Scholar
- Dalvikexplorer apks. https://code.google.com/archive/p/enh/downloads. (last accessed: 2015-Aug-22).Google Scholar
- F-droid: Free and open source android app repository. https://f-droid.org/. (last accessed: 2015-May-22).Google Scholar
- Firefox apks. https://ftp.mozilla.org/pub/mobile/nightly/. (last accessed: 2015-May-22).Google Scholar
- GreenOracle dataset. https://github.com/shaifulcse/GreenOracle-Data. (created on: 2016-Jan-29).Google Scholar
- Intro Linux Man Page. http://linux.die.net/man/2/intro. (last accessed: 2014-May-22).Google Scholar
- /proc/stat explained. http://www.linuxhowtos.org/System/procstat.htm. (last accessed: 2014-May-22).Google Scholar
- THE /proc FILES YSTEM. https://www.kernel.org/doc/Documentation/filesystems/proc.txt. (last accessed: 2014-May-22).Google Scholar
- VLC apks. http://nightlies.videolan.org/build/android-armv7/backup/. (last accessed: 2015-Aug-22).Google Scholar
- Why are synchronize expensive in Java? http://stackoverflow.com/questions/1671089/why-are-synchronize-expensive-in-java. (last accessed: 2014-May-22).Google Scholar
- K. Aggarwal, A. Hindle, and E. Stroulia. Greenadvisor: A tool for analyzing the impact of software evolution on energy consumption. In Software Maintenance and Evolution (ICSME), 2015 IEEE International Conference on, pages 311--320, Bremen, Germany, Sept 2015. Google ScholarDigital Library
- K. Aggarwal, C. Zhang, J. C. Campbell, A. Hindle, and E. Stroulia. The Power of System Call Traces: Predicting the Software Energy Consumption Impact of Changes. In CASCON '14, 2014. Google ScholarDigital Library
- M. J. Alam, P. Ouellet, P. Kenny, and D. O'Shaughnessy. Comparative evaluation of feature normalization techniques for speaker verification. In Proceedings of the 5th International Conference on Advances in Nonlinear Speech Processing, NOLISP'11, pages 246--253, Berlin, Heidelberg, 2011. Springer-Verlag. Google ScholarDigital Library
- E. Alpaydin. Combining multiple learners. In Introduction to Machine Learning (Second Edition). MIT Press.Google Scholar
- Banerjee, Abhijeet and Chong, Lee Kee and Chattopadhyay, Sudipta and Roychoudhury, Abhik. Detecting Energy Bugs and Hotspots in Mobile Apps. In FSE 2014, pages 588--598, Hong Kong, China, Novemeber 2014. Google ScholarDigital Library
- A. Carroll and G. Heiser. An Analysis of Power Consumption in a Smartphone. In Proceedings of the USENIXATC'10, 2010. Google ScholarDigital Library
- S. Chowdhury, K. Luke, J. Toukir, Imam Mohomed, S. Varun, K. Aggarwal, A. Hindle, and G. Russell. A System-call based Model of Software Energy Consumption without Hardware Instrumentation. In IGSC '15, Las Vegas, US, December 2015. Google ScholarDigital Library
- S. Chowdhury, S. Varun, and A. Hindle. Client-side Energy Efficiency of HTTP/2 for Web and Mobile App Developers. In SANER '16 (to appear), Osaka, Japan, March 2016.Google ScholarCross Ref
- Cisco. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2014-2019. Technical report, Cisco, February 2015.Google Scholar
- M. Dong and L. Zhong. Self-constructive High-rate System Energy Modeling for Battery-powered Mobile Systems. In Proceedings of the MobiSys '11, pages 335--348, June 2011. Google ScholarDigital Library
- eMarketer. 2 billion consumers worldwide to get smart(phones) by 2016. http://www.emarketer.com/Article/2-Billion-Consumers-Worldwide-Smartphones-by-2016/1011694. (last accessed: 2016-Jan-07).Google Scholar
- J. Flinn and M. Satyanarayanan. PowerScope: A Tool for Profiling the Energy Usage of Mobile Applications. In WMCSA '99, pages 2--10, New Orleans, Louisiana, USA, February 1999. Google ScholarDigital Library
- N. Gautam, H. Petander, and J. Noel. A Comparison of the Cost and Energy Efficiency of Prefetching and Streaming of Mobile Video. In Proceedings of the 5th Workshop on Mobile Video, MoVid '13, pages 7--12, Oslo, Norway, February 2013. Google ScholarDigital Library
- S. Gurumurthi, A. Sivasubramaniam, M. J. Irwin, N. Vijaykrishnan, M. Kandemir, T. Li, and L. K. John. Using Complete Machine Simulation for Software Power Estimation: The SoftWatt Approach. In Proceedings of the 8th International Symposium on High-Performance Computer Architecture, HPCA '02, pages 141--150, 2002. Google ScholarDigital Library
- S. Hao, D. Li, W. G. J. Halfond, and R. Govindan. Estimating Mobile Application Energy Consumption Using Program Analysis. In ICSE '13, pages 92--101, 2013. Google ScholarDigital Library
- T. Hastie, R. Tibshirani, and J. Friedman. Linear methods for regression. In The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.Google Scholar
- T. Hastie, R. Tibshirani, and J. Friedman. Support vector machines and flexible discriminants. In The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.Google Scholar
- A. Hindle. Green Mining: Investigating Power Consumption Across Versions. In ICSE '12, pages 1301--1304, June 2012. Google ScholarDigital Library
- A. Hindle, A. Wilson, K. Rasmussen, E. J. Barlow, J. C. Campbell, and S. Romansky. GreenMiner: A Hardware Based Mining Software Repositories Software Energy Consumption Framework. In MSR 2014, pages 12--21, Hyderabad, India, May 2014. Google ScholarDigital Library
- T. Joachims. Making large-scale support vector machine learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods, pages 169--184. MIT Press, 1999. Google ScholarDigital Library
- M. Karagiannopoulos, D. Anyfantis, S. B. Kotsiantis, and P. E. Pintelas. Feature Selection for Regression Problems. http://www.math.upatras.gr/~dany/Downloads/hercma07.pdf. (last accessed: 2015-Oct-22).Google Scholar
- D. Li, A. H. Tran, and W. G. J. Halfond. Making Web Applications More Energy Efficient for OLED Smartphones. In ICSE 2014, pages 527--538, Hyderabad, India, June 2014. Google ScholarDigital Library
- J. Meier, M.-c. Ostendorp, J. Jelschen, and A. Winter. Certifying energy efficiency of android applications. In 4th Workshop on Energy Aware Software-Engineering and Development, 2014.Google Scholar
- A. P. Miettinen and J. K. Nurminen. Energy Efficiency of Mobile Clients in Cloud Computing. In Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud'10, pages 4--4, Boston, MA, USA, June 2010. Google ScholarDigital Library
- I. Moura, G. Pinto, F. Ebert, and F. Castor. Mining Energy-Aware Commits. In MSR 2015, Florence, Italy, May 2015. Google ScholarDigital Library
- M. Othman and S. Hailes. Power Conservation Strategy for Mobile Computers Using Load Sharing. SIGMOBILE Mob. Comput. Commun. Rev., 2(1):44--51, January 1998. Google ScholarDigital Library
- C. Pang, A. Hindle, B. Adams, and A. E. Hassan. What do programmers know about the energy consumption of software? Peer J PrePrints, 3:e1094, 2015.Google Scholar
- A. Pathak, Y. C. Hu, and M. Zhang. Where is the Energy Spent Inside My App?: Fine Grained Energy Accounting on Smartphones with Eprof. In EuroSys '12, pages 29--42, Bern, Switzerland, April 2012. Google ScholarDigital Library
- A. Pathak, Y. C. Hu, M. Zhang, P. Bahl, and Y.-M. Wang. Fine-grained Power Modeling for Smartphones Using System Call Tracing. In EuroSys '11, pages 153--168, Salzburg, Austria, April 2011. Google ScholarDigital Library
- G. Pinto, F. Castor, and Y. D. Liu. Mining Questions About Software Energy Consumption. In MSR 2014, pages 22--31, 2014. Google ScholarDigital Library
- P. Poole. Half of Us Have Computers in Our Pockets, Though You'd Hardly Know it. http://www.huffingtonpost.com/pamela-poole/smartphone-technology_b_2573671.html. (last accessed: 2014-Dec-22).Google Scholar
- K. Rasmussen, A. Wilson, and A. Hindle. Green Mining: Energy Consumption of Advertisement Blocking Methods. In GREENS 2014, pages 38--45, Hyderabad, India, June 2014. Google ScholarDigital Library
- C. Seo, S. Malek, and N. Medvidovic. Component-level energy consumption estimation for distributed java-based software systems. volume 5282 of Lecture Notes in Computer Science, pages 97--113. Springer Berlin Heidelberg, 2008. Google ScholarDigital Library
- A. Shye, B. Scholbrock, and G. Memik. Into the Wild: Studying Real User Activity Patterns to Guide Power Optimizations for Mobile Architectures. In IEEE/ACM MICRO 42, pages 168--178, New York, NY, USA, December 2009. Google ScholarDigital Library
- V. Vapnik. The nature of statistical learning theory. Springer, 2000. Google ScholarCross Ref
- C. Zhang, A. Hindle, and D. German. The impact of user choice on energy consumption. Software, IEEE, 31(3):69--75, May 2014.Google ScholarCross Ref
- L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M. Mao, and L. Yang. Accurate Online Power Estimation and Automatic Battery Behavior Based Power Model Generation for Smartphones. In Proceedings of the 8th IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, 2010. Google ScholarDigital Library
- H. Zou and T. Hastie. Regularization and Variable Selection via the Elastic Net. http://people.ee.duke.edu/~lcarin/Minhua11.7.08.pdf. (last accessed: 2015-Oct-22).Google Scholar
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