發(fā)布時(shí)間：2018-06-17 21:27

  本文選題：軟件定義網(wǎng)絡(luò) + 分布式控制器��； 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：自從2008年以來(lái),SDN在提出之后短短幾年時(shí)間就取得了飛速的發(fā)展,SDN的最大特點(diǎn)就是控制平面和轉(zhuǎn)發(fā)平面相互解耦,通過(guò)一些特殊的手段把網(wǎng)絡(luò)中的控制平面從底層網(wǎng)絡(luò)中解耦出來(lái),通過(guò)可編程,可操控的控制平面取代了傳統(tǒng)的較為封閉的嵌入式控制平面,提取出來(lái)的控制平面將會(huì)集中到一個(gè)集中式控制器中,這樣一來(lái),整個(gè)網(wǎng)絡(luò)就會(huì)由這個(gè)集中式控制器來(lái)負(fù)責(zé),這樣就能夠達(dá)成了動(dòng)態(tài)管理以及靈活調(diào)度網(wǎng)絡(luò)資源的目標(biāo)。隨著社會(huì)發(fā)展,網(wǎng)絡(luò)規(guī)模不可避免的增大,一個(gè)單一的集中式控制器很容易出現(xiàn)過(guò)載或者故障。對(duì)于大規(guī)模的網(wǎng)絡(luò)來(lái)說(shuō),即使是一個(gè)非常強(qiáng)大的控制器,也會(huì)因?yàn)槿狈Ρ匾�的處理器和�?nèi)存容量從而很難保持完整的網(wǎng)絡(luò)狀態(tài),也就無(wú)法對(duì)所有的網(wǎng)絡(luò)事件做出反應(yīng)。如果發(fā)生這種事件,整個(gè)網(wǎng)絡(luò)的服務(wù)性能就會(huì)不可避免的降低,嚴(yán)重情況下甚至?xí)��?dǎo)致網(wǎng)絡(luò)癱瘓,這就需要一個(gè)分布式集群,這個(gè)集群有許多個(gè)SDN控制器構(gòu)成,這樣就能夠解決單一控制器的性能問(wèn)題。現(xiàn)在,為了解決單一控制器所面臨的各種各樣的問(wèn)題,許多研究機(jī)構(gòu)和企業(yè)都在單一的集中式控制器的基礎(chǔ)之上提出新的架構(gòu),主流的的架構(gòu)主要有兩種:一種是以O(shè)NOS控制器為代表的擁有一個(gè)集中式數(shù)據(jù)庫(kù)的分布式控制器,另外一種是以O(shè)NIX控制器為代表的完全分布式控制器,以上兩類控制器都可以避免單一控制器所面臨的性能不足的問(wèn)題,同時(shí)也有著各自獨(dú)有的優(yōu)點(diǎn)和缺點(diǎn)。本文研究了以上兩種分布式控制器,并且在分布式控制器架構(gòu)的基礎(chǔ)上設(shè)計(jì)了資源開銷模型,主要包含存儲(chǔ)開銷和通信開銷這兩種比較重要的開銷。并且在這個(gè)資源開銷模型的基礎(chǔ)之上提出了自己的優(yōu)化算法。本文的優(yōu)化算法主要是在兩種較為主流的控制器架構(gòu)中尋找到一種平衡,進(jìn)一步的在存儲(chǔ)開銷和通信開銷之間尋找平衡,對(duì)控制器的網(wǎng)絡(luò)視圖進(jìn)行從新分配,從而更加合理的使用網(wǎng)絡(luò)中的資源,最終達(dá)到減少總的資源開銷的目的。本文還通過(guò)多組的實(shí)驗(yàn)對(duì)比來(lái)對(duì)算法的結(jié)果進(jìn)行了論證,并且進(jìn)一步探討算法的使用環(huán)境,最終本文對(duì)未來(lái)的研究方向提出了許多理論上的規(guī)劃。
[Abstract]:Since its inception in 2008, SDN has made rapid development in a few years. The biggest characteristic of SDN is that the control plane and the forwarding plane are decoupled. The control plane in the network is decoupled from the bottom network by some special means. The programmable and controllable control plane replaces the traditional closed embedded control plane. The extracted control plane will be centralized into a centralized controller, so that the whole network will be responsible for the centralized controller, which can achieve the goal of dynamic management and flexible scheduling of network resources. With the development of society, the scale of network increases inevitably, and a single centralized controller is prone to overload or failure. For large-scale networks, even a very powerful controller will be unable to respond to all network events because of the lack of necessary processors and memory capacity to maintain a complete network state. If such an event occurs, the service performance of the entire network will inevitably decline, and in serious cases even lead to network paralysis, which requires a distributed cluster, which has many SDN controllers. In this way, the performance of a single controller can be solved. Now, in order to solve the various problems faced by a single controller, many research institutions and enterprises have proposed a new architecture based on a single centralized controller. There are two main architectures: one is a distributed controller with a centralized database represented by an ONOS controller, the other is a fully distributed controller represented by an ONIX controller. These two kinds of controllers can avoid the problem of insufficient performance faced by single controller, and also have their own advantages and disadvantages. This paper studies the above two distributed controllers, and designs a resource overhead model based on the distributed controller architecture, which mainly includes storage overhead and communication overhead. On the basis of the resource overhead model, this paper proposes its own optimization algorithm. The optimization algorithm in this paper is mainly to find a balance between the storage overhead and the communication overhead in the two main controller architectures, and to redistribute the network view of the controller. Thus more rational use of resources in the network, and ultimately achieve the purpose of reducing the total cost of resources. This paper also demonstrates the results of the algorithm through the comparison of many experiments, and further discusses the use environment of the algorithm. Finally, this paper puts forward many theoretical plans for the future research direction.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13;TP273

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