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  本文選題：云計算　切入點：能效　出處：《云南大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：高性能、低功耗且具有QOS保障的高能效云是云計算領(lǐng)域的一個熱點研究問題。目前的研究大多是通過限定一個約束條件尋求另外指標(biāo)的最優(yōu)化來實現(xiàn)三者之間的折衷或均衡,現(xiàn)有的方法存在四個瓶頸性的問題：1)涉及能效的參數(shù)眾多,綜合能效指標(biāo)的統(tǒng)一模型難以建立；2)在能效評價過程中,缺乏對云計算環(huán)境的能效優(yōu)劣程度直觀、定性的評估；3)資源分配粒度單一、固定,難以實時感知作業(yè)任務(wù)執(zhí)行過程中能效的動態(tài)變化；4)能效參數(shù)間密切耦合,相互影響,相互制約,建模的誤差和干擾因素的影響,導(dǎo)致能效值呈現(xiàn)不確定性,系統(tǒng)不具有魯棒性。論文針對云環(huán)境下面向獨立應(yīng)用任務(wù)的能效指標(biāo)之間的定量分析和均衡性問題進(jìn)行了研究,同時針對大規(guī)模數(shù)據(jù)密集型應(yīng)用任務(wù)的能效預(yù)測和解耦分析展開了研究,主要研究內(nèi)容包括以下幾個方面：(1)針對獨立應(yīng)用任務(wù)的QoS與系統(tǒng)能耗之間關(guān)系的定量分析問題,論文提出了一種單位能耗計算量的能源效率模型。首先將能耗作為一個關(guān)鍵因素引入到QoS指標(biāo)中,基于能耗、性能和QoS之間的相互關(guān)系建立了一個系統(tǒng)能效模型,根據(jù)該模型所計算的能效值,采用演化博弈策略設(shè)計實現(xiàn)了滿足不同服務(wù)需求的資源配置。針對不同數(shù)量的獨立應(yīng)用作業(yè)任務(wù),所提出的能源效率模型可以較好刻地畫性能、能耗和QoS三者間的相互關(guān)系。(2)針對不同應(yīng)用任務(wù)類型性能、功耗和QoS保障三者之間的均衡性問題,論文提出一種基于QOS參數(shù)歸約的加權(quán)能效模型。首先,把系統(tǒng)性能指標(biāo)引入QOS,并將多個離散的QoS參數(shù)度量值歸約到同一個量綱區(qū)域內(nèi),獲得評價權(quán)重矩陣,求得用戶最終的QoS評價值,以單位能耗所提供的整體QoS水平值作為能效值,并根據(jù)能效值將云數(shù)據(jù)中心進(jìn)行了分級標(biāo)識。通過在單機環(huán)境和云環(huán)境下實驗測試,該模型能夠較好的描述CPU密集型計算、I/O密集型計算和交互式任務(wù)的能效值,定性的評估云環(huán)境下的系統(tǒng)能效。(3)針對大規(guī)模數(shù)據(jù)密集、計算密集和分析密集等密集型計算任務(wù),資源分配對系統(tǒng)能效的感知往往滯后的問題,論文建立了一種基于Lasso回歸的能效預(yù)測模型,根據(jù)該模型提出了一種能效感知的、基于粒度空間尋優(yōu)的資源分配方法。首先,在圍繞待處理作業(yè)任務(wù)特點和虛擬資源特征的基礎(chǔ)上,選用粒度合適的“�！弊鳛槿蝿�(wù)處理對象,針對不同粒度的“任務(wù)粒”在多維資源粒度空間以能效驅(qū)動的方式分配合理的“資源�！�,根據(jù)用戶實時的需求變化進(jìn)行有效的粒度層次快速切換。所提出的方法針對密集型任務(wù)在資源分配的過程中可以實時有效的保障用戶的QoS,提高系統(tǒng)吞吐量,同時降低系統(tǒng)的能源消耗。(4)針對大規(guī)模密集型計算任務(wù)能效模型建模的誤差和干擾因素的影響,導(dǎo)致系統(tǒng)能效值呈現(xiàn)出不確定性,系統(tǒng)不具有魯棒性的問題,論文提出一種基于模糊解耦的能效優(yōu)化方法。通過建立能效的模糊規(guī)則和隸屬度函數(shù),設(shè)計了一種多變量模糊解耦控制器,將MIMO耦合的云系統(tǒng)轉(zhuǎn)化為幾個互不干擾的SISO輸出模型。所提出的方法可以明確影響系統(tǒng)能效的關(guān)鍵參數(shù)并及時定位系統(tǒng)能效瓶頸,同時在指定的不確定界的擾動下,仍能維持預(yù)期的能效值。
[Abstract]:High performance, a hot research issue in high energy efficiency and low power consumption with QOS cloud security computing. Most of the current study is to seek another index between optimization by defining a constraint conditions to achieve the three compromise or equilibrium, the present method has four bottleneck problems: 1) parameters involved the energy efficiency of many, it is difficult to establish a unified model of comprehensive energy efficiency index; 2) in energy efficiency in the process of evaluation, the lack of efficiency of the extent of cloud computing environment of the intuitive, qualitative evaluation; 3) resource allocation granularity is single, fixed task execution, sensing the dynamic change process of energy efficiency to real time; 4) energy efficiency parameters close coupling, mutual influence, mutual restraint, error and interference factor modeling, resulting in energy efficiency value is uncertain, the system does not have the robustness. The thesis focuses on the cloud environment to the independent application as below The quantitative analysis and the equilibrium problem between the energy efficiency index is studied, and the research of energy efficiency prediction and decoupling analysis for large-scale data intensive application tasks, the main contents include the following aspects: (1) the problem of quantitative analysis of the relationship between QoS and energy consumption of the system according to the independent application task, is proposed in this paper a model of energy efficiency calculation unit energy consumption. The energy consumption as a key factor is introduced to the QoS index, based on energy consumption, the relationship between performance and QoS established a system efficiency model, according to the calculation value of energy efficiency, using the evolutionary game strategy is designed to meet the needs of different service the allocation of resources. In view of the independent application tasks in different quantities, energy efficiency of the proposed model can better moment painting performance, mutual energy and QoS between the three . (2) according to the different types of application of task performance, the balance of power between the three and QoS security, this paper proposes a weighted QOS model parameters based on reduction efficiency. Firstly, the system performance index QOS is introduced, and a plurality of discrete QoS parameters to measure value reduction to the same dimension within the region and get the weights of the evaluation matrix, the QoS evaluation of the final value of the user, to provide the overall energy consumption per unit of QoS energy level value as the value, and according to the efficiency value of cloud data center to the classification identification. Through experiments in the stand-alone environment and cloud environment test, the model can describe CPU intensive computing, I/O intensive computing and interactive tasks. The value of energy efficiency, energy efficiency evaluation system under the cloud environment qualitative. (3) for large-scale data intensive computing intensive and intensive analysis and computation intensive tasks, resources distribution system efficiency Perceptions tend to lag problem, this thesis proposes a prediction model based on Lasso regression efficiency, according to the model proposed an energy aware resource allocation method, spatial optimization based on particle size. First of all, in around the processing based task characteristics and virtual resources characteristic, the suitable size of granule as the task object, according to the different size of "task grain" reasonable distribution of energy efficiency drive in the multi-dimensional resource granularity space mode of "resource grain", effectively fast switching granularity according to user needs change in real time. The proposed method for intensive tasks in the process of resource allocation in real time can be effectively protected users of QoS, improve the system throughput, and reduce system energy consumption. (4) for large scale intensive computing task efficiency modeling error and interference factors In effect, the energy efficiency of the system value is uncertain, the system does not have the robustness problem, this paper proposes a method of energy efficiency optimization based on fuzzy decoupling. Through the establishment of fuzzy rules and membership functions of the energy efficiency, design a multivariable fuzzy decoupling controller, cloud system MIMO coupling into the SISO output model do not interfere with each other. The proposed method can clear the key parameters influencing the system efficiency and timely positioning system efficiency bottleneck, at the same time in the specified uncertainties disturbances can still maintain the expected value of energy efficiency.

【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP3

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