【摘要】：隨著云計(jì)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(wèn)題日益凸顯,給云服務(wù)提供商帶來(lái)高額運(yùn)營(yíng)成本的同時(shí),嚴(yán)重制約了云計(jì)算的可持續(xù)發(fā)展。云計(jì)算應(yīng)用領(lǐng)域的不斷拓展使其服務(wù)對(duì)象已由傳統(tǒng)的桌面用戶群滲透到移動(dòng)用戶群,催生了移動(dòng)云計(jì)算(MC2, Mobile Cloud Computing)這一新興計(jì)算模式。MC2通過(guò)移動(dòng)互聯(lián)網(wǎng)連接移動(dòng)設(shè)備端與云端,對(duì)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(kāi)研究,運(yùn)用動(dòng)態(tài)優(yōu)化方法構(gòu)建理論分析模型,設(shè)計(jì)在線控制算法,優(yōu)化系統(tǒng)的能耗和性能。論文的研究?jī)?nèi)容和成果包括： (1)數(shù)據(jù)中心計(jì)算資源自配置的性能與節(jié)能優(yōu)化。首先,運(yùn)用馬爾科夫決策過(guò)程(MDP, Markov Decision Process)理論構(gòu)建資源自配置問(wèn)題的動(dòng)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(yùn)用強(qiáng)化學(xué)習(xí)和近似動(dòng)態(tài)規(guī)劃方法,提出了一種計(jì)算資源自配置算法RASA, RASA算法利用服務(wù)器CPU的動(dòng)態(tài)頻率調(diào)節(jié)機(jī)制,動(dòng)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(shí)驗(yàn)驗(yàn)證了RASA算法的有效性。 (2)分布式SaaS云請(qǐng)求路由與虛擬機(jī)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(wèn)題的動(dòng)態(tài)優(yōu)化模型,目標(biāo)是在保證應(yīng)用請(qǐng)求隊(duì)列穩(wěn)定性的前提下,最小化時(shí)間平均(Time Average)能源成本、碳稅成本和帶寬租用成本；然后,運(yùn)用Lyapunov隨機(jī)優(yōu)化方法,提出了一種分布式的在線調(diào)度算法GREEN,在運(yùn)營(yíng)成本最優(yōu)性與隊(duì)列穩(wěn)定性之間實(shí)現(xiàn)平衡控制；最后,設(shè)計(jì)基于真實(shí)數(shù)據(jù)集的仿真實(shí)驗(yàn),驗(yàn)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(yùn)用MDP理論構(gòu)建端到端上下行數(shù)據(jù)傳輸問(wèn)題的動(dòng)態(tài)優(yōu)化模型；然后,提出了一種基于近似動(dòng)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(dòng)應(yīng)用的延遲容忍特性,通過(guò)動(dòng)態(tài)的鏈路選擇與數(shù)據(jù)傳輸調(diào)度,優(yōu)化移動(dòng)設(shè)備能耗和吞吐量；最后,設(shè)計(jì)仿真實(shí)驗(yàn)對(duì)eLean算法的有效性進(jìn)行了驗(yàn)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(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP308

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本文編號(hào)：2422676