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Issue No. 10 - Oct. (2015 vol. 14)
ISSN: 1536-1233
pp: 2059-2072
Yongin Kwon , Department of Electrical and Computer Engineering, Seoul National University, Kwanak-gu Kwanak-ro 1, Seoul
Sangmin Lee , Department of Computer Science, University of Texas at Austin, USA
Hayoon Yi , Department of Electrical and Computer Engineering, Seoul National University, Kwanak-gu Kwanak-ro 1, Seoul
Donghyun Kwon , Department of Electrical and Computer Engineering, Seoul National University, Kwanak-gu Kwanak-ro 1, Seoul
Seungjun Yang , Department of Electrical and Computer Engineering, Seoul National University, Kwanak-gu Kwanak-ro 1, Seoul
Byung-gon Chun , Department of Electrical and Computer Engineering, Seoul National University, Kwanak-gu Kwanak-ro 1, Seoul
Ling Huang , Research Department, Intel Labs, 2150 Shattuck, Suite 1300, Berkeley, CA, USA
Petros Maniatis , Research Department, Intel Labs, 2150 Shattuck, Suite 1300, Berkeley, CA, USA
Mayur Naik , School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
Yunheung Paek , Department of Electrical and Computer Engineering, Seoul National University, Kwanak-gu Kwanak-ro 1, Seoul
ABSTRACT
We present Mantis, a framework for predicting the computational resource consumption (CRC) of Android applications on given inputs accurately, and efficiently. A key insight underlying Mantis is that program codes often contain features that correlate with performance and these features can be automatically computed efficiently. Mantis synergistically combines techniques from program analysis and machine learning. It constructs concise CRC models by choosing from many program execution features only a handful that are most correlated with the program’s CRC metric yet can be evaluated efficiently from the program’s input. We apply program slicing to reduce evaluation time of a feature and automatically generate executable code snippets for efficiently evaluating features. Our evaluation shows that Mantis predicts four CRC metrics of seven Android apps with estimation error in the range of 0-11.1 percent by executing predictor code spending at most 1.3 percent of their execution time on Galaxy Nexus.
INDEX TERMS
Instruments, Feature extraction, Computational modeling, Measurement, Generators, Predictive models, Mobile computing,Computational Resource Consumption, Prediction, Program analysis, Machine Learning, Slicing, Smartphone
CITATION
Yongin Kwon, Sangmin Lee, Hayoon Yi, Donghyun Kwon, Seungjun Yang, Byung-gon Chun, Ling Huang, Petros Maniatis, Mayur Naik, Yunheung Paek, "Mantis: Efficient Predictions of Execution Time, Energy Usage, Memory Usage and Network Usage on Smart Mobile Devices", IEEE Transactions on Mobile Computing, vol. 14, no. , pp. 2059-2072, Oct. 2015, doi:10.1109/TMC.2014.2374153
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