- 學位論文
探討手機耗電與啟動延遲之平衡以提升使用者感受度
Enhancing User Experiences by Exploiting Energy and Launch Delay Tradeoff of Mobile Applications
摘要
手機上的應用程式之啟動延遲已經成為影響使用者感受的一個重要因素，為了加速啟動的過程，Android採用LRU演算法為基礎的背景應用程式管理機制，然而在背景儲存過多的應用程式將導致手機耗電量增加，進而影響電池使用的時間，因此啟動延遲與手機續航力將成為一個兩難的課題。 本論文的主要困難點有二，第一是如何監控與評估手機上應用程式的耗電行為，我們提供了一個靜態的耗電量測工具:ANEPROF，以及一套動態的耗電模型:Energy Hunter，第二個困難點在於如何管理背景的應用程式，把低耗電或高延遲的應用快取起來，把不重要的應用程式移出背景。 根據我們邀請的受測者之模擬結果，我們所開發的Android應用程式管理系統將能同時減少15%的啟動延遲以及18%的電池續航力。
並列摘要
Launch delay has been an important factor affecting users' experiences in mobile applications. To launch applications quickly, modern mobile systems such as Android often employ mechanisms such as LRU-based activity stack to keep inactive applications in the background. Whenever the user wants to run and interact with a background application again, that application can be switched back into the foreground immediately from the activity stack without delay in initializing the applications anew. Since background applications still consume the battery power of the smart phone, the challenge is to effect a balance between application launch delay and battery lifetime. The challenge entails two aspects. First, it is necessary to understand the power consumption and launch delay of mobile applications. Although it is not difficult to estimate the execution time of applications, their power consumption is hard to measure. In this thesis, we introduce two tools for measuring the power consumption of applications. The first is an offline power analysis tool called {em ANEPROF}, and the second is an online energy model called {em Energy Hunter}. Both offer systematic and formalized approaches for power estimation. ANEPROF is a measurement-based power analyzer which can obtain function-level power distribution and distinguish the power consumptions of threads, Java methods, and JVM services. We apply ANEPROF to provide the power consumption of default Android applications through a series of static measurement. Energy Hunter is a model-based power estimator which can track the energy footprint of individual Android applications down to the hardware components. We adopt Energy Hunter to offer runtime system power and the power consumption of third-party applications running on the system. Given information of the power consumption and launch delay of applications, the second aspect is to manage the applications to balance the power consumption and launch delay of the whole system. Current Android implementation examines only those applications that are already in the activity stack, which limits the amount of tradeoffs that can be made. If it is possible to predict what applications to be used in the near future, then the activity stack can be better managed to optimize the tradeoff of power consumption and launch delay. In this thesis, we propose an innovative application management strategy that terminates "unfavorable" background applications to save energy and pre-launch "favorable" applications to improve the application launch delay. The core of the strategy is a context-aware prediction scheme that predicts what applications will be used next based on the environmental contexts of the mobile system. The proposed tools and the application management strategy are evaluated through a trace-driven simulation and a 30 volunteers experiment. To evaluate the accuracy and overhead of ANEPROF, we design five sets of experiments to compare with other estimation schemes. With a 5% overhead and 14% maximal profiling error rate, ANEPROF can provide the accurate enough power consumption information for application designers. To verify Energy Hunter, we collect the energy consumption by ANEPROF as ground truth and compare the results with that from Energy Hunter. In the experiments, Energy Hunter can provide average 92% accuracy estimation. With ANEPROF and Energy Hunter, the proposed application management framework achieves 15% reduction of average application launch delay and 18% increase of average battery lifetime.