本文選題：網(wǎng)絡(luò)化系統(tǒng) + 分布式算法��； 參考：《北京郵電大學(xué)》2017年博士論文

【摘要】：網(wǎng)絡(luò)化系統(tǒng)作為計(jì)算機(jī)網(wǎng)絡(luò)未來(lái)發(fā)展的趨勢(shì)已經(jīng)受到越來(lái)越廣泛的關(guān)注。所謂網(wǎng)絡(luò)化系統(tǒng)即指具備如下兩點(diǎn)特征的系統(tǒng)。第一,大規(guī)模的網(wǎng)絡(luò)化系統(tǒng)可以被劃分為許多子模塊,各模塊間能夠按照特定的網(wǎng)絡(luò)通信模型進(jìn)行互聯(lián)。第二,個(gè)體間能夠相互協(xié)作,整個(gè)網(wǎng)絡(luò)拓?fù)涑尸F(xiàn)為分布式或者半分布式結(jié)構(gòu)。我們?nèi)粘Ｉ钪械拇蠖鄶?shù)網(wǎng)絡(luò)都可以看做為網(wǎng)絡(luò)化系統(tǒng)中的一種,例如:車(chē)聯(lián)網(wǎng)、無(wú)線(xiàn)傳感網(wǎng)、移動(dòng)數(shù)據(jù)網(wǎng)以及工業(yè)互聯(lián)網(wǎng)等。當(dāng)前,面向全分布式的網(wǎng)絡(luò)化系統(tǒng)已有大量的研究,其中,大多數(shù)研究主要集中在相對(duì)應(yīng)的分布式算法上。所謂分布式算法,即各個(gè)網(wǎng)絡(luò)節(jié)點(diǎn)僅通過(guò)自身與周?chē)?jié)點(diǎn)的信息交互來(lái)共同解決網(wǎng)絡(luò)中存在的問(wèn)題。與傳統(tǒng)的集中式算法不同,在分布式算法中,往往不需要控制中心節(jié)點(diǎn),網(wǎng)絡(luò)中每個(gè)節(jié)點(diǎn)的地位都是等價(jià)的。能夠有效避免單點(diǎn)失效、關(guān)鍵節(jié)點(diǎn)負(fù)載過(guò)大等問(wèn)題,魯棒性,實(shí)時(shí)性高。但同時(shí)分布式算法會(huì)增加整個(gè)網(wǎng)絡(luò)的通信量,帶寬消耗巨大,且對(duì)網(wǎng)絡(luò)中的拓?fù)溆幸欢ǖ囊蟆Ａ硗?現(xiàn)有面向網(wǎng)絡(luò)化系統(tǒng)的分布式算法研究中,大多數(shù)都對(duì)網(wǎng)絡(luò)環(huán)境進(jìn)行了過(guò)于理想的假設(shè),導(dǎo)致大多數(shù)研究在實(shí)際問(wèn)題分析中不能得到有效的應(yīng)用。本文重點(diǎn)圍繞網(wǎng)絡(luò)化系統(tǒng)下如何建立符合實(shí)際應(yīng)用環(huán)境的模型、如何采用有效的分布式優(yōu)化算法對(duì)模型進(jìn)行理論分析以及針對(duì)網(wǎng)絡(luò)化系統(tǒng)中具體應(yīng)用問(wèn)題進(jìn)行分布式優(yōu)化算法構(gòu)建求解等內(nèi)容展開(kāi)深入研究。主要工作體現(xiàn)在對(duì)網(wǎng)絡(luò)化系統(tǒng)下分布式算法的理論推導(dǎo)分析與具體應(yīng)用建模求解兩個(gè)方面,具體研究?jī)?nèi)容如下:(1)針對(duì)無(wú)線(xiàn)虛擬傳感網(wǎng)絡(luò)下鏈路狀態(tài)不穩(wěn)定特點(diǎn),提出了基于概率穩(wěn)定性的鏈路動(dòng)態(tài)拓?fù)淠Ｐ?并提出了動(dòng)態(tài)擴(kuò)散性卡爾曼濾波算法,該算法對(duì)于實(shí)時(shí)系統(tǒng)中的運(yùn)動(dòng)物體軌跡的估計(jì)、跟蹤及預(yù)測(cè)都有著良好的性能表現(xiàn)。為了進(jìn)一步證明算法的有效性,本研究點(diǎn)從理論層面對(duì)算法進(jìn)行了深入分析,分別對(duì)其均值與均方誤差值進(jìn)行了嚴(yán)格的推導(dǎo)證明,以此證明了所提算法是無(wú)偏且收斂的,能夠很好的用于上述網(wǎng)絡(luò)化系統(tǒng)模型中。最后,通過(guò)仿真對(duì)算法進(jìn)行了驗(yàn)證,并通過(guò)與相關(guān)實(shí)驗(yàn)對(duì)比分析,進(jìn)一步證明了所提算法在所提動(dòng)態(tài)拓?fù)渚W(wǎng)絡(luò)模型下的良好性能表現(xiàn)。(2)考慮移動(dòng)群智感知網(wǎng)絡(luò)系統(tǒng)內(nèi)大量分布的低功耗設(shè)備,由于計(jì)算、存儲(chǔ)資源不足、帶寬受限,設(shè)備間不能夠進(jìn)行大規(guī)模高精度數(shù)據(jù)傳輸。針對(duì)上述問(wèn)題,首先,提出了基于抖動(dòng)隨機(jī)量的量化模型,其次,基于該模型提出了量化擴(kuò)散卡爾曼濾波算法。在該算法中,將節(jié)點(diǎn)間交互的不同信息進(jìn)行量化,包括觀(guān)測(cè)值,噪聲值等。由于量化后的數(shù)據(jù)會(huì)對(duì)原算法的穩(wěn)定性和收斂性產(chǎn)生負(fù)面的影響,本研究點(diǎn)著重分析了量化后算法的各種理論性能表現(xiàn),并通過(guò)仿真實(shí)驗(yàn)來(lái)對(duì)此進(jìn)行驗(yàn)證。通過(guò)分析可知,該算法在在網(wǎng)絡(luò)資源受限情況下仍具有較高的數(shù)值估計(jì)準(zhǔn)確率。(3)針對(duì)5G內(nèi)容中心網(wǎng)絡(luò)下內(nèi)容緩存能效最優(yōu)化問(wèn)題,采用基于投影的擴(kuò)散分布式優(yōu)化算法進(jìn)行解決。為了能夠有效運(yùn)用該算法,首先針對(duì)內(nèi)容緩存策略問(wèn)題進(jìn)行抽象建模,并提出連續(xù)性及凸性等假設(shè)條件來(lái)保障算法的有效實(shí)現(xiàn)。實(shí)際上,本研究點(diǎn)重點(diǎn)分析了擴(kuò)散性分布式優(yōu)化算法在具體應(yīng)用問(wèn)題進(jìn)行建模求解的方法,具有普適性。最后,將所提方案在幾種具有代表性的場(chǎng)景下進(jìn)行了仿真實(shí)驗(yàn),結(jié)果表明其能夠達(dá)到預(yù)期的效果。由于網(wǎng)絡(luò)化系統(tǒng)和分布式算法種類(lèi)繁多,本文中提出的所有算法及應(yīng)用都只是提供了一種研究該類(lèi)問(wèn)題的思想和方法。因此本文的研究具有很高的可擴(kuò)展性。在具體研究過(guò)程中,本文通過(guò)對(duì)相關(guān)工作進(jìn)行調(diào)研,并通過(guò)理論建模、算法設(shè)計(jì)、分析以及實(shí)驗(yàn)仿真驗(yàn)證等一系列方法對(duì)相關(guān)問(wèn)題進(jìn)行了深入研究,對(duì)所提算法的優(yōu)越性以及實(shí)用性進(jìn)行了驗(yàn)證。本文取得的研究成果對(duì)網(wǎng)絡(luò)化系統(tǒng)下分布式算法的研究與發(fā)展具有很好的借鑒意義。
[Abstract]:The network system as the future development trend of the computer network has been paid more and more attention. The so-called network system refers to the system with the following two characteristics. First, the large-scale network system can be divided into many sub modules, each module can be interconnected according to the network communication model. Second, The whole network topology is distributed or semi distributed. Most of the networks in our daily life can be regarded as one of the network systems, such as car networking, wireless sensor networks, mobile data networks, and industrial Internet. Most of the studies mainly focus on the corresponding distributed algorithms. The so-called distributed algorithms, that is, each network node can solve the problems in the network only through the information interaction between themselves and the surrounding nodes. Unlike the traditional centralized algorithm, the central node is often not required in the distributed algorithm. The status of each node in the collaterals is equivalent. It can effectively avoid the single point failure, the key node is overloaded and so on, it is robust and real-time. At the same time, the distributed algorithm will increase the communication amount of the whole network, the bandwidth consumption is huge, and the topology of the network is required. In addition, the existing distributed system oriented distributed system is distributed. In the research of algorithm, most of the network environment is too ideal. Most of the research can not be applied effectively in the actual problem analysis. This paper focuses on how to establish a model which conforms to the actual application environment under the network system, and how to use an effective distributed optimization algorithm to analyze the model. The main work is two aspects: theoretical derivation analysis and specific application modeling solution of distributed algorithm under networked system. The specific research contents are as follows: (1) the chain under the wireless virtual sensor network The link dynamic topology model based on the probability stability is proposed, and the dynamic diffusion Calman filter algorithm is proposed. The algorithm has good performance performance for the estimation, tracking and prediction of the moving object trajectory in the real-time system. In order to further prove the effectiveness of the algorithm, this research point is based on the theory. In this paper, the algorithm is deeply analyzed, and the mean and mean square error value are strictly derived, which proves that the proposed algorithm is unbiased and convergent, and can be used in the networked system model well. Finally, the algorithm is verified by simulation, and the analysis is further compared with the related experiments. The performance performance of the proposed algorithm is proved under the proposed dynamic topology network model. (2) considering the large number of low-power devices distributed in the mobile swarm intelligence network system, because of the lack of storage resources and limited bandwidth, the high precision data transmission can not be carried out among the devices. Secondly, based on the model, a quantitative diffusion Calman filtering algorithm is proposed. In this algorithm, the different information between nodes is quantified, including observation value, noise value, etc. because the quantized data will have a negative impact on the stability and convergence of the original algorithm, this research point focuses on the analysis of quantization. All kinds of theoretical performance of the post algorithm are presented and verified by simulation experiments. Through analysis, it can be found that the algorithm still has a high accuracy rate in the case of network resource constraints. (3) aiming at the problem of content cache energy efficiency optimization under the 5G content center network, the distributed distributed optimization algorithm based on projection is adopted. In order to effectively use the algorithm, first of all, it aims at the abstract modeling of the problem of content caching strategy, and puts forward the hypothesis of continuity and convexity to ensure the effective implementation of the algorithm. In fact, this research point focuses on the analysis of the method of modeling and solving the diffusion distributed optimization algorithm in specific application problems. Finally, the proposed scheme is simulated in several representative scenes, and the results show that they can achieve the desired results. All the algorithms and applications proposed in this paper provide a kind of thought and method for studying the problem because of the variety of network and distributed algorithms. In the course of specific research, this paper makes a thorough study of related problems through a series of methods, such as theoretical modeling, algorithm design, analysis and experimental simulation verification, and proves the advantages and practicability of the proposed algorithm. The results have a good reference for the research and development of distributed algorithm under networked system.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP393.0;TN713

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 馬蘭申;自適應(yīng)網(wǎng)絡(luò)的分布式估計(jì)研究[D];蘇州大學(xué);2014年

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本文編號(hào)：2039334