本文選題：自律計算　切入點：分布式代理系統(tǒng)　出處：《哈爾濱工業(yè)大學(xué)》2014年碩士論文

【摘要】：現(xiàn)如今萬維網(wǎng)已經(jīng)滲透到了人類社會發(fā)展的許多方面，成為了不可或缺的一部分。隨著網(wǎng)絡(luò)技術(shù)的發(fā)展，，各種相應(yīng)的網(wǎng)絡(luò)服務(wù)也隨之出現(xiàn)，然而大規(guī)模的網(wǎng)絡(luò)應(yīng)用和網(wǎng)絡(luò)服務(wù)使得網(wǎng)絡(luò)中的流量劇增，導(dǎo)致了網(wǎng)絡(luò)流量擁塞、網(wǎng)絡(luò)響應(yīng)延遲等問題。HTTP請求產(chǎn)生的網(wǎng)絡(luò)流量所占的比重持續(xù)增長，使得Web服務(wù)器承擔(dān)的訪問負載越來越大，同時人們對于HTTP請求的響應(yīng)速度期望也越來越高。代理技術(shù)是減輕Web服務(wù)器訪問負載、提高HTTP請求響應(yīng)速度的重要措施，而分布式代理技術(shù)可以解決單臺代理服務(wù)器服務(wù)能力不足的問題，提供更好的網(wǎng)絡(luò)服務(wù)質(zhì)量，同時采用了負載均衡技術(shù)和緩存技術(shù)，提高分布式代理系統(tǒng)的整體性能。為提升緩存服務(wù)器的性能，本文提出了以二級域名為基礎(chǔ)的內(nèi)容熱度預(yù)測算法，為自動靈活的、細粒度的自動管理分布式代理系統(tǒng)，本文建立了基于自律計算的分布式代理系統(tǒng)管理機制，其中運用了自律計算和強化學(xué)習(xí)的思想。 首先，介紹了與本課題相關(guān)的理論與技術(shù)，了解了MAPE-K自律循環(huán)模型的框架結(jié)構(gòu)、組成部分以及相應(yīng)的功能；了解了當(dāng)前流行的一些緩存替換算法的原理以及各算法之間的優(yōu)缺點有了更加深入的理解；通過舉例理解了Q學(xué)習(xí)算法的原理以及簡單應(yīng)用。其次，講述了基于自律計算的分布式代理系統(tǒng)的設(shè)計，提出了分布式代理系統(tǒng)框架結(jié)構(gòu)，包括負載均衡、代理以及緩存等三個模塊；提出了自律的分布式代理系統(tǒng)模型，從而將自律計算技術(shù)與分布式代理系統(tǒng)結(jié)合在一起；提出了基于二級域名的緩存內(nèi)容熱度預(yù)測方法，不僅考慮了緩存對象的命中次數(shù)，還考慮了緩存對象的整體特征，將熱度較高的URL遷移出去，從而降低了緩存服務(wù)器的負載。然后，介紹了基于自律計算的分布式代理系統(tǒng)的實現(xiàn)，重點介紹了基于Q學(xué)習(xí)的自律決策模塊，提出了基于BP神經(jīng)網(wǎng)絡(luò)的Q學(xué)習(xí)模型和算法。最后，通過基于Q學(xué)習(xí)的自律決策框架模型和緩存內(nèi)容熱度預(yù)測實驗結(jié)果，表明了分布式代理系統(tǒng)的自律管理機制能夠有效的、靈活的實現(xiàn)系統(tǒng)管理，并提升了系統(tǒng)的整體性能。
[Abstract]:Nowadays, the World wide Web has permeated many aspects of the development of human society and become an indispensable part. With the development of network technology, various kinds of corresponding network services have also appeared. However, large-scale network applications and network services make the traffic in the network increase dramatically, resulting in network traffic congestion, network response delay and other problems. The proportion of network traffic generated by HTTP requests continues to grow. It makes the Web server bear more and more access load and people expect the response speed of HTTP request more and more. Proxy technology is an important measure to lighten the access load of Web server and improve the response speed of HTTP request. Distributed proxy technology can solve the problem of insufficient service capacity of a single proxy server and provide better network quality of service. At the same time, load balancing technology and cache technology are adopted. In order to improve the performance of cache server, this paper proposes a content heat prediction algorithm based on secondary domain name, which is an automatic, flexible and fine-grained distributed agent management system. In this paper, a distributed agent system management mechanism based on autonomous computing is established, in which the idea of autonomous computing and reinforcement learning is used. Firstly, the theory and technology related to this topic are introduced, and the frame structure, components and corresponding functions of the MAPE-K autonomous cycle model are understood. The principle of some popular cache replacement algorithms and the advantages and disadvantages of each algorithm are understood more deeply. The principle of Q learning algorithm and its simple application are understood by examples. Secondly, This paper describes the design of distributed agent system based on autonomic computing, puts forward the framework of distributed agent system, including three modules of load balancing, agent and cache, and puts forward the model of autonomous distributed agent system. Combining autonomous computing technology with distributed agent system, this paper proposes a new method for predicting cache content heat based on secondary domain name, which not only considers the hit times of cache objects, but also takes into account the overall characteristics of cache objects. In order to reduce the load of cache server, the URL with high heat is migrated out. Then, the realization of distributed agent system based on self-discipline computing is introduced, and the autonomous decision-making module based on Q-learning is introduced emphatically. The Q learning model and algorithm based on BP neural network are proposed. Finally, through the self-discipline decision framework model based on Q-learning and the experimental results of cache content heat prediction, it is shown that the autonomous management mechanism of distributed agent system is effective. Flexible implementation of system management, and improve the overall performance of the system.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09

【共引文獻】

相關(guān)碩士學(xué)位論文 前2條

1 王冬;基于自決策的分布式代理緩存技術(shù)研究[D];哈爾濱工業(yè)大學(xué);2013年

2 王文苑;分布式緩存可用性相關(guān)問題研究[D];華中科技大學(xué);2013年

本文編號：1686935