title: A Machine Learning Method for Power Prediction on the Mobile Devices.
abstract: Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption.
authors: Department of Information Management, National Taichung University of Science and Technology, Taichung City, 404, Taiwan, Republic of China.
authors: Da-Ren
authors: DR
authors: Chen
authors: You-Shyang
authors: YS
authors: Chen
authors: Lin-Chih
authors: LC
authors: Chen
authors: Ming-Yang
authors: MY
authors: Hsu
authors: Kai-Feng
authors: KF
authors: Chiang
mesh_terms: D040421:Cell Phone; D011211:Electric Power Supplies; D017143:Fuzzy Logic; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D016571:Neural Networks, Computer