【摘要】：隨著嵌入式系統(tǒng)的高速發(fā)展,高性能和低功耗的矛盾日益突出,功耗已成為嵌入式系統(tǒng)設(shè)計的主要問題。高功耗消耗增加冷卻成本,降低系統(tǒng)的可靠性和電池的使用期限。減少電能的消耗不僅能延長電池的壽命,降低用戶更換電池的周期,而且能帶來提高系統(tǒng)性能與降低系統(tǒng)開銷的好處,甚至能起到保護環(huán)境的作用。低功耗設(shè)計就是在保證性能約束的條件下最大限度地降低功耗。 動態(tài)電源管理(Dynamic Power Management, DPM)已被證明是一種有效減少系統(tǒng)功耗的系統(tǒng)級低功耗技術(shù)。它在一定的請求服務(wù)和性能的約束下,對系統(tǒng)進(jìn)行動態(tài)配置,關(guān)閉閑置系統(tǒng)組件或?qū)⑦@些組件轉(zhuǎn)入低能耗狀態(tài),從而實現(xiàn)對能耗的有效利用。 隨著對DPM的深入研究,一個標(biāo)準(zhǔn)化、支持不同電源管理策略的策略框架日益變得重要。DPM框架從整個系統(tǒng)的角度來處理系統(tǒng)的電源管理問題,采用結(jié)構(gòu)化的規(guī)則和機制來整合系統(tǒng)不同組件的DPM技術(shù)或相關(guān)策略。 現(xiàn)有的DPM框架缺乏對策略的調(diào)度方法及對策略性能的有效評估,鑒于此,本文在原有DPM框架的基礎(chǔ)上,加入策略選擇與策略評估模塊,對原有框架進(jìn)行擴展,得到一個更為完善的DPM框架。 DPM策略是系統(tǒng)級動態(tài)電源管理技術(shù)研究的重點,它決定了空閑組件何時關(guān)閉或轉(zhuǎn)入低功耗狀態(tài),動態(tài)電源管理對能耗的有效利用,很大程度上取決與所采用的策略的性能。傳統(tǒng)DPM策略主要有Timeout策略、預(yù)測策略、隨機模型策略三種,這些策略都有各自的缺點且策略的有效性嚴(yán)重依賴于一個精確的負(fù)載模型,然而對于一個復(fù)雜的系統(tǒng)來說,負(fù)載通常是不可預(yù)測的,因為它取決與應(yīng)用程序的性質(zhì)、輸入數(shù)據(jù)于用戶上下文。 本文在分析傳統(tǒng)DPM策略不足的基礎(chǔ)上,提出系統(tǒng)級動態(tài)電源管理的在線學(xué)習(xí)策略。策略通過獎勵／懲罰改變系統(tǒng)狀態(tài)的行為,調(diào)整行為,從而實現(xiàn)動態(tài)電源管理策略的在線自更新,解決在部分可觀察環(huán)境(負(fù)載模型無法預(yù)測)下的動態(tài)電源管理問題。 最后,在擴展的DPM框架上,對策略選擇、在線策略與傳統(tǒng)DPM策略進(jìn)行對比實驗,驗證了本文擴展的動態(tài)電源管理框架的有效性及策略選擇和在線策略的省電性能。
[Abstract]:With the rapid development of embedded systems, the contradiction between high performance and low power consumption has become increasingly prominent, power consumption has become the main problem of embedded system design. High power consumption increases cooling costs and reduces system reliability and battery life. Reducing the consumption of electric energy can not only prolong the battery life, reduce the cycle of battery replacement, but also improve the system performance and reduce the cost of the system, and even play a role in protecting the environment. Low power design is to minimize power consumption while ensuring performance constraints. Dynamic Power Management (DPM) has been proved to be a system level low power technology which can effectively reduce system power consumption. It dynamically configures the system under the constraints of request service and performance, closes the idle system components or turns them into a low energy consumption state, so as to realize the effective utilization of energy consumption. With the in-depth study of DPM, a standardized policy framework supporting different power management strategies has become increasingly important. DPM framework deals with the power management problems of the system from the perspective of the whole system. Adopt structured rules and mechanisms to integrate DPM technology or related strategies of different components of the system. The existing DPM framework lacks the scheduling method of the policy and the effective evaluation of the policy performance. In view of this, this paper adds the policy selection and policy evaluation module on the basis of the original DPM framework, and extends the original framework. Get a more perfect DPM framework. DPM strategy is the focus of system-level dynamic power management technology. It determines when idle components are turned off or into low-power state, and dynamic power management makes effective use of energy consumption. Much depends on the performance of the strategy used. There are three kinds of traditional DPM strategies: timeout strategy, prediction strategy and stochastic model strategy. These strategies have their own shortcomings and their effectiveness depends heavily on an accurate load model. However, for a complex system, the effectiveness of these strategies depends heavily on an accurate load model. The load is usually unpredictable because it depends on the nature of the application and inputs data into the user context. Based on the analysis of the deficiency of traditional DPM strategy, this paper proposes an online learning strategy for dynamic power supply management at system level. The strategy changes the behavior of the system state and adjusts the behavior through reward / punishment to realize the online self-updating of the dynamic power management strategy and to solve the dynamic power management problem under the partially observable environment (the load model can not be predicted). Finally, in the extended DPM framework, the comparison of policy selection, online strategy and traditional DPM strategy is carried out to verify the effectiveness of the extended dynamic power supply management framework and the power saving performance of the strategy selection and online strategy.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP303.3

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