【摘要】：諸如智能手機、平板電腦等智能移動設備已成為人們?nèi)粘Ｉ钪械谋匦杵�。隨著智能手機上配備更多的硬件模塊,開發(fā)者使用它們開發(fā)出各類軟件,以最大限度提升用戶使用體驗。高能耗是此類軟件所要考慮的重要因素。然而,當前電池密度的增長相對緩慢,電池技術(shù)無法滿足移動設備的重度使用。為了解決這個問題,已有許多能耗管理技術(shù)被用來設計能量高效的移動系統(tǒng)。當前電源管理方案主要存在的問題包括過分重視單個子系統(tǒng)而忽視全局優(yōu)化,占用大量計算資源,云技術(shù)泄漏用戶隱私的風險等。由于電量是移動設備的一種極度稀缺資源,電源能耗管理已成為移動設備系統(tǒng)中必不可少的組成部分。針對當前電源管理方案的不足,本文提出了一種基于半Markov決策過程的動態(tài)電源管理方案。該方案基于半Markov決策過程建模,同時考慮了用戶體驗和設備能耗,與Boe方案相比需要更少的狀態(tài)和計算時間。為完善模型,通過設備Power monitor監(jiān)測華為G610-T00智能手機的使用狀態(tài)并記錄不同模塊的功耗,設計Android控制軟件以調(diào)節(jié)GPS更新速率和LCD屏幕亮度,根據(jù)開源軟件Power Tutor二次開發(fā)以記錄用戶使用數(shù)據(jù)。此外本文給出了兩種在線算法,Q學習和策略梯度估計算法,并分析了仿真結(jié)果。為了比較所得策略,制定了多種評估準則,以此驗證節(jié)能算法的有效性。本文通過仿真驗證了所建模型的可行性和節(jié)能算法的有效性。仿真結(jié)果顯示本方案的最優(yōu)策略與常用策略相比能延長53%的使用時長,增加51%的總用戶體驗。所給出的策略梯度估計算法能在不使用云端技術(shù)條件下更新策略,計算量小,不依賴于模型參數(shù),同時用戶可自由設置策略更新時間,便于實現(xiàn)。本文所提出的動態(tài)電源管理方案對現(xiàn)實手機節(jié)能有積極的指導意義。
[Abstract]:Smart mobile devices such as smartphones and tablets have become a daily necessity. With more hardware modules on smartphones, developers use them to develop software to maximize user experience. High energy consumption is an important factor to be considered in such software. However, the current cell density growth is relatively slow, battery technology can not meet the heavy use of mobile devices. To solve this problem, many energy management techniques have been used to design energy-efficient mobile systems. The main problems of the current power management scheme include paying too much attention to a single subsystem and neglecting global optimization, occupying a large amount of computing resources, and the risk of cloud technology leaking users' privacy and so on. Power consumption management has become an essential part of mobile device systems because of the fact that power consumption is an extremely scarce resource for mobile devices. A dynamic power management scheme based on semi-Markov decision process is proposed in this paper in view of the shortcomings of the current power management scheme. This scheme is based on semi-Markov decision process modeling, taking into account the user experience and device energy consumption. Compared with the Boe scheme, it requires less state and computing time. In order to perfect the model, Huawei G610-T00 smartphone is monitored by device Power monitor and the power consumption of different modules is recorded. The Android control software is designed to adjust the update rate of GPS and the brightness of LCD screen. Second development based on open source software Power Tutor to record user usage data. In addition, two online algorithms, namely, Q learning and strategy gradient estimation, are presented, and the simulation results are analyzed. In order to compare the strategies, a variety of evaluation criteria are developed to verify the effectiveness of the energy-saving algorithm. The feasibility of the proposed model and the effectiveness of the energy saving algorithm are verified by simulation in this paper. The simulation results show that the optimal strategy can prolong the service time by 53% and increase the total user experience by 51%. The proposed strategy gradient estimation algorithm can update the policy without using cloud technology. It has less computation and does not depend on the model parameters. At the same time, the user is free to set the policy update time, which is easy to implement. The dynamic power management scheme proposed in this paper has positive guiding significance for practical mobile phone energy saving.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.53

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本文編號：2261401