發(fā)布時間：2019-02-14 22:32

【摘要】：隨著云計算(CC, Cloud Computing)勺蓬勃發(fā)展,云數(shù)據(jù)中心高能耗、高碳排放的問題日益凸顯,給云服務(wù)提供商帶來高額運營成本的同時,嚴(yán)重制約了云計算的可持續(xù)發(fā)展。云計算應(yīng)用領(lǐng)域的不斷拓展使其服務(wù)對象已由傳統(tǒng)的桌面用戶群滲透到移動用戶群,催生了移動云計算(MC2, Mobile Cloud Computing)這一新興計算模式。MC2通過移動互聯(lián)網(wǎng)連接移動設(shè)備端與云端,對端到端數(shù)據(jù)傳輸?shù)哪苄岢隽溯^高的要求。本文圍繞CC和MC2的性能與節(jié)能優(yōu)化展開研究,運用動態(tài)優(yōu)化方法構(gòu)建理論分析模型,設(shè)計在線控制算法,優(yōu)化系統(tǒng)的能耗和性能。論文的研究內(nèi)容和成果包括： (1)數(shù)據(jù)中心計算資源自配置的性能與節(jié)能優(yōu)化。首先,運用馬爾科夫決策過程(MDP, Markov Decision Process)理論構(gòu)建資源自配置問題的動態(tài)優(yōu)化模型；然后,鑒于外部環(huán)境模型的未知性,綜合運用強化學(xué)習(xí)和近似動態(tài)規(guī)劃方法,提出了一種計算資源自配置算法RASA, RASA算法利用服務(wù)器CPU的動態(tài)頻率調(diào)節(jié)機制,動態(tài)匹配資源分配量與系統(tǒng)負(fù)載,優(yōu)化系統(tǒng)能耗和性能；仿真實驗驗證了RASA算法的有效性。 (2)分布式SaaS云請求路由與虛擬機調(diào)度的節(jié)能優(yōu)化。首先,構(gòu)建分布式SaaS云成本與性能管理問題的動態(tài)優(yōu)化模型,目標(biāo)是在保證應(yīng)用請求隊列穩(wěn)定性的前提下,最小化時間平均(Time Average)能源成本、碳稅成本和帶寬租用成本；然后,運用Lyapunov隨機優(yōu)化方法,提出了一種分布式的在線調(diào)度算法GREEN,在運營成本最優(yōu)性與隊列穩(wěn)定性之間實現(xiàn)平衡控制；最后,設(shè)計基于真實數(shù)據(jù)集的仿真實驗,驗證GREEN算法在非穩(wěn)態(tài)環(huán)境下的有效性。 (3)MC2鏈路選擇與傳輸調(diào)度的性能與節(jié)能優(yōu)化。首先,運用MDP理論構(gòu)建端到端上下行數(shù)據(jù)傳輸問題的動態(tài)優(yōu)化模型；然后,提出了一種基于近似動態(tài)規(guī)劃的在線學(xué)習(xí)算法eLean,該算法利用不同鏈路的能效差異性和部分移動應(yīng)用的延遲容忍特性,通過動態(tài)的鏈路選擇與數(shù)據(jù)傳輸調(diào)度,優(yōu)化移動設(shè)備能耗和吞吐量；最后,設(shè)計仿真實驗對eLean算法的有效性進行了驗證。
[Abstract]:With the rapid development of cloud computing (CC, Cloud Computing), the problem of high energy consumption and high carbon emissions in cloud data centers is becoming increasingly prominent, which brings high operating costs to cloud service providers and seriously restricts the sustainable development of cloud computing. With the continuous expansion of cloud computing application field, the traditional desktop user group has penetrated into the mobile user group, which has given birth to the mobile cloud computing (MC2,). Mobile Cloud Computing) is a new computing mode. MC2 demands high efficiency of end-to-end data transmission by connecting mobile device and cloud via mobile Internet. This paper focuses on the performance and energy saving optimization of CC and MC2. The dynamic optimization method is used to construct the theoretical analysis model and design the on-line control algorithm to optimize the energy consumption and performance of the system. The research contents and achievements are as follows: (1) performance and energy saving optimization of data center computing resource self-configuration. Firstly, the (MDP, Markov Decision Process) theory of Markov decision process is used to construct the dynamic optimization model of resource self-allocation problem. Then, in view of the uncertainty of the external environment model, a computational resource self-configuration algorithm (RASA, RASA) is proposed to utilize the dynamic frequency regulation mechanism of server CPU by using reinforcement learning and approximate dynamic programming. Dynamically match resource allocation with system load, optimize system energy consumption and performance; Simulation results show the effectiveness of RASA algorithm. (2) Energy saving optimization of distributed SaaS cloud request routing and virtual machine scheduling. Firstly, the dynamic optimization model of distributed SaaS cloud cost and performance management is constructed. The objective is to minimize the time average (Time Average) energy cost, carbon tax cost and bandwidth rental cost under the premise of ensuring the stability of application request queue. Then, using Lyapunov stochastic optimization method, a distributed online scheduling algorithm, GREEN, is proposed to achieve balance control between operational cost optimality and queue stability. Finally, a simulation experiment based on real data set is designed to verify the effectiveness of GREEN algorithm in unsteady environment. (3) performance and energy saving optimization of MC2 link selection and transmission scheduling. Firstly, the dynamic optimization model of end-to-end uplink and downlink data transmission is constructed by using MDP theory. Then, an online learning algorithm based on approximate dynamic programming (eLean,) is proposed, which makes use of the difference of energy efficiency of different links and the delay tolerance of some mobile applications, through dynamic link selection and data transmission scheduling. Optimize energy consumption and throughput of mobile devices; Finally, simulation experiments are designed to verify the effectiveness of the eLean algorithm.
【學(xué)位授予單位】：北京科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：TP308

【參考文獻】

相關(guān)期刊論文 前4條

1 林闖;萬劍雄;向旭東;孟坤;王元卓;;計算機系統(tǒng)與計算機網(wǎng)絡(luò)中的動態(tài)優(yōu)化:模型、求解與應(yīng)用[J];計算機學(xué)報;2012年07期

2 鄧維;劉方明;金海;李丹;;云計算數(shù)據(jù)中心的新能源應(yīng)用:研究現(xiàn)狀與趨勢[J];計算機學(xué)報;2013年03期

3 宋明娟;孫斌煜;徐紹明;;轉(zhuǎn)爐冶煉軸承鋼過程中鈦的行為熱力學(xué)分析[J];山西冶金;2013年01期

4 ;A Survey of Mobile Cloud Computing[J];ZTE Communications;2011年01期

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本文編號：2422676