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• W2918237603 abstract "Due to the limited battery capacity of mobile devices, various CPU power governors and dynamic frequency adjustment schemes have been proposed to reduce CPU energy consumption. However, most such schemes are app-oblivious, ignoring an important fact that real-world applications often exhibit multiple execution phases that perform different functionality and may request different amounts of hardware resources. Having a unified app-level frequency setting for different phases of an application may not be energy efficient enough and may even violate the desirable latency performance required by certain phases. Motivated by this observation, in this paper, we present PIFA, which is an intelligent Phase Identification and Frequency Adjustment framework for energy-efficient and time-sensitive mobile computing. PIFA addresses two major challenges of fully automatically identifying different execution phases of an application and efficiently integrating the phase identification results for runtime frequency adjustment. We have fully implemented PIFA on the Android platform. An extensive set of experiments using real-world Android applications from multiple app categories demonstrate that PIFA achieves closely better performance than the desired latency requirement specified for each phase, while dramatically reducing energy consumption (e.g., >30% energy reduction for most apps) and incurring rather small runtime overhead (e.g., <5% overhead for most apps)." @default.
• W2918237603 created "2019-03-11" @default.
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• W2918237603 date "2019-04-01" @default.
• W2918237603 modified "2023-09-23" @default.
• W2918237603 title "PIFA: An Intelligent Phase Identification and Frequency Adjustment Framework for Time-Sensitive Mobile Computing" @default.
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• W2918237603 doi "https://doi.org/10.1109/rtas.2019.00013" @default.
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