本文選題：網(wǎng)絡(luò)擁塞控制 + 主動(dòng)隊(duì)列管理　； 參考：《江西理工大學(xué)》2014年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)Internet的飛速發(fā)展，網(wǎng)絡(luò)多媒體業(yè)務(wù)日趨多樣化，互聯(lián)網(wǎng)上的用戶和應(yīng)用都在急劇增加，網(wǎng)絡(luò)擁塞成為制約網(wǎng)絡(luò)發(fā)展和應(yīng)用的瓶頸。網(wǎng)絡(luò)擁塞控制因而至關(guān)重要，，作為改善網(wǎng)絡(luò)系統(tǒng)性能、提高服務(wù)質(zhì)量的主要手段，對(duì)網(wǎng)絡(luò)擁塞控制問(wèn)題的研究具有重大的理論意義和應(yīng)用價(jià)值。 本文分別從局部和全局的角度對(duì)網(wǎng)絡(luò)擁塞控制方法進(jìn)行研究分析。針對(duì)網(wǎng)絡(luò)系統(tǒng)的單一節(jié)點(diǎn)，主動(dòng)隊(duì)列管理（AQM）是一種網(wǎng)絡(luò)擁塞控制效果較好且廣泛使用的一種方法，是Internet擁塞控制領(lǐng)域的研究熱點(diǎn)。本文在目前存在的AQM算法的基礎(chǔ)上，在算法設(shè)計(jì)上引入智能優(yōu)化算法，設(shè)計(jì)了新的AQM算法，從而明顯地改善隊(duì)列的性能。從網(wǎng)絡(luò)系統(tǒng)的全局出發(fā)，針對(duì)網(wǎng)絡(luò)中的關(guān)鍵節(jié)點(diǎn)和鏈路實(shí)施新的路由算法，通過(guò)網(wǎng)絡(luò)路徑優(yōu)化，從而使得網(wǎng)絡(luò)負(fù)載均衡分配，降低丟包率，提高鏈路吞吐量，減小時(shí)延，獲得一個(gè)高QoS保證的網(wǎng)絡(luò)環(huán)境。本文的主要工作包括以下兩個(gè)方面： （1）基于隊(duì)列長(zhǎng)度和鏈路速率相對(duì)變化率，設(shè)計(jì)一種帶有參數(shù)優(yōu)化的模糊神經(jīng)網(wǎng)絡(luò)控制器的擁塞控制方法。該算法引入隊(duì)列長(zhǎng)度和期望隊(duì)列長(zhǎng)度以及鏈路速率與鏈路容量的相對(duì)誤差量作為網(wǎng)絡(luò)擁塞指示，通過(guò)改進(jìn)后的遺傳算法定時(shí)對(duì)模糊神經(jīng)網(wǎng)絡(luò)控制器進(jìn)行參數(shù)優(yōu)化，實(shí)現(xiàn)對(duì)網(wǎng)絡(luò)擁塞的有效控制。在大時(shí)滯環(huán)境和突發(fā)流情況下，該算法的穩(wěn)定性和控制效果都比較令人滿意。 （2）分析了QoS的技術(shù)特征、執(zhí)行過(guò)程以及現(xiàn)有路由算法的優(yōu)缺點(diǎn)，從路由優(yōu)化的角度，基于移動(dòng)Ad hoc網(wǎng)絡(luò)（MANETs），設(shè)計(jì)一種運(yùn)用在幾何路由中的基于遺傳算法的路由優(yōu)化算法，目標(biāo)是在滿足帶寬、時(shí)延、費(fèi)用等多項(xiàng)QoS指標(biāo)的基礎(chǔ)上，路徑時(shí)延最小化，負(fù)載盡量分布在有寬裕空閑資源的鏈路上，便于今后接納更多的請(qǐng)求，達(dá)到提高網(wǎng)絡(luò)吞吐量的目的，進(jìn)而避免擁塞。
[Abstract]:With the rapid development of Internet Internet, network multimedia services are becoming more and more diversified, and the number of users and applications on the Internet is increasing rapidly. Network congestion has become a bottleneck restricting the development and application of network. Therefore, network congestion control is of great importance. As the main means to improve the performance of network system and improve the quality of service, the research on network congestion control has great theoretical significance and application value. In this paper, the network congestion control methods are studied and analyzed from the local and global perspectives. For a single node in a network system, active queue management (AQM) is an effective and widely used method for network congestion control. It is a hot research topic in the field of Internet congestion control. In this paper, based on the existing AQM algorithm, an intelligent optimization algorithm is introduced into the algorithm design, and a new AQM algorithm is designed, which obviously improves the performance of the queue. Based on the overall situation of the network system, a new routing algorithm is implemented for the key nodes and links in the network. Through the network path optimization, the network load distribution is balanced, the packet loss rate is reduced, the link throughput is improved, and the delay is reduced. Obtain a high QoS guaranteed network environment. The main work of this paper includes the following two aspects: 1) based on the relative change rate of queue length and link rate, a congestion control method of fuzzy neural network controller with parameter optimization is designed. The algorithm introduces queue length and expected queue length as well as the relative error between link rate and link capacity as network congestion indication, and optimizes the parameters of fuzzy neural network controller by the improved genetic algorithm. The effective control of network congestion is realized. The stability and control effect of the algorithm are satisfactory in the case of large time delay and sudden flow. This paper analyzes the technical characteristics, execution process and advantages and disadvantages of existing routing algorithms of QoS. From the point of view of routing optimization, a genetic algorithm based on genetic algorithm for geometric routing is designed based on mobile Ad hoc networks. The goal is to minimize path delay on the basis of satisfying bandwidth, delay, cost and other QoS indicators, and to distribute the load on links with spare resources as much as possible, so that more requests can be accepted in the future, so that the throughput of the network can be improved. Thus avoiding congestion.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.06;TP18

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本文編號(hào)：1834906