【摘要】：在傳統(tǒng)IP骨干網中一般采取分布式的流量調度方式和靜態(tài)的路由策略。這些方法缺乏全局視角,導致網絡鏈路的利用率較低和網絡擁塞等問題。Google在2012將SDN(Software Defined Network)技術成功引入到IP骨干網中,實現(xiàn)了跨數(shù)據(jù)中心之間廣域網的集中式流量調度機制,使網絡中鏈路利用率提高到95%以上。這為運營商對其電信廣域網的流量調度帶來了新的思路。本文探索將SDN技術引入到IP骨干網的可行性問題,針對IP骨干網的特殊性,設計了基于SDN的流量調度架構和粒度可變的流量調度算法,有效解決傳統(tǒng)IP骨干網中鏈路利用率低與鏈路負載不均衡等問題。本文的主要研究成果如下:(1)分析了將SDN引入到IP骨干網所面臨的五個挑戰(zhàn):控制器與交換機交互壓力成為性能的瓶頸,控制器下發(fā)路由表項的不一致性,控制器獲取網絡信息難度大,底層設備表項容量限制,流量調度算法性能要求高。針對這些挑戰(zhàn),設計了基于路徑標識的集中式IP骨干網流量調度架構。該架構的主要特點:1)預分配流表,將全局路由信息預先下發(fā)到底層設備,減少控制器與交換機交互信息;2)路徑標識,使用全局唯一標識表示全局唯一的路徑,解決表項下發(fā)不一致問題;3)分類非重疊表項,流表項匹配范圍不重疊,并且流表按照匹配規(guī)則進行分類,結合SDN南向接口協(xié)議實現(xiàn)網絡匯聚流監(jiān)測機制;4)匯聚流粒度調整與流量調度交互執(zhí)行,匯聚流粒度調整為流量調度提供匯聚流預處理,將匯聚流粒度控制在合理范圍,避免表項數(shù)量過多,并且提高流量調度執(zhí)行效率,以及匯聚流遷移成功率�；贔loodlight+Mininet平臺,驗證了該架構能夠完整執(zhí)行,并正確做出決策。(2)為解決鏈路擁塞問題,設計了粒度可變的流量調度算法。該算法從調度對象和調度方法兩個方面設計,提出匯聚流粒度調整算法和流量調度算法。匯聚流粒度調整算法的作用是控制匯聚流粒度在合理范圍。為防止匯聚流粒度過細,提出了匯聚流聚合算法和匯聚流調換算法,為防止匯聚流粒度過粗,提出匯聚流拆分算法。流量調度算法的作用是通過匯聚流遷移降低擁塞鏈路負載。本文提出三個算法:1)MILP流量調度算法,以最小化重路由業(yè)務數(shù)量為目標構建混合整數(shù)線性規(guī)劃(MILP)模型;2)分層鏈路動態(tài)上限調度算法,以最小化重路由業(yè)務數(shù)為目標構建MILP模型,以及通過改變鏈路容量上限實現(xiàn)負載均衡;3)組合擁塞鏈路算法,優(yōu)先遷移流經數(shù)條擁塞鏈路的匯聚流,將擁塞鏈路上的數(shù)條匯聚流均衡分配到數(shù)條備選路徑。經仿真驗證,三個算法在不同的性能上有不同的優(yōu)勢。
[Abstract]:Distributed traffic scheduling and static routing strategy are generally adopted in traditional IP backbone networks. These methods lack a global perspective, resulting in low utilization of network links and network congestion. Google successfully introduced SDN (Software Defined Network) technology into IP backbone network in 2012. The centralized traffic scheduling mechanism of WAN across data centers is implemented, and the link utilization rate in the network is increased to more than 95%. This brings a new idea to the traffic scheduling of telecom wide area network (WAN). This paper explores the feasibility of introducing SDN technology into IP backbone network. According to the particularity of IP backbone network, the traffic scheduling architecture based on SDN and the traffic scheduling algorithm with variable granularity are designed. It can effectively solve the problems of low link utilization and unbalanced link load in traditional IP backbone network. The main results of this paper are as follows: (1) five challenges of introducing SDN into IP backbone network are analyzed: the interaction pressure between controller and switch becomes the bottleneck of performance, and the inconsistency of routing table items under controller is analyzed. It is difficult for the controller to obtain network information, the capacity of the underlying equipment table is limited, and the performance of the traffic scheduling algorithm is high. Aiming at these challenges, a centralized IP backbone network traffic scheduling architecture based on path identification is designed. The main features of the architecture are as follows: 1) preassigned flow table, sending global routing information down to the underlying device in advance, reducing the interactive information between controller and switch; 2) path identification, which uses the global unique identity to represent the globally unique path, which solves the problem of inconsistency of table items; 3) Non-overlapping table items are classified, the matching range of stream table items is not overlapped, and the flow table is classified according to matching rules, and the network convergence flow monitoring mechanism is realized with the combination of SDN southward interface protocol. 4) aggregate flow granularity adjustment interacts with traffic scheduling. Convergence flow granularity adjustment provides convergence flow preprocessing for traffic scheduling, controls aggregation flow granularity within a reasonable range, avoids excessive number of table items, and improves the efficiency of traffic scheduling. And the success rate of convergent flow migration. Based on the Floodlight Mininet platform, it is verified that the architecture can be implemented completely and the decision is made correctly. (2) in order to solve the problem of link congestion, a traffic scheduling algorithm with variable granularity is designed. The algorithm is designed from two aspects of scheduling object and scheduling method, and proposes a convergence flow granularity adjustment algorithm and a traffic scheduling algorithm. The function of the convergence flow granularity adjustment algorithm is to control the convergence flow granularity in a reasonable range. In order to prevent the convergence flow granularity from being too fine, the convergence flow aggregation algorithm and the convergent flow exchange algorithm are proposed. In order to prevent the convergence flow from being too coarse, the convergent flow splitting algorithm is proposed. The function of traffic scheduling algorithm is to reduce the congestion link load by converging flow migration. This paper proposes three algorithms: 1) MILP traffic scheduling algorithm to minimize the number of rerouting traffic as the goal to build a mixed integer linear programming (MILP) model; 2) hierarchical link dynamic upper bound scheduling algorithm, aiming at minimizing the number of rerouting services, constructing MILP model, and realizing load balancing by changing the upper limit of link capacity; 3) combining the congestion link algorithm, the convergent flow flowing through several congestion links is transferred first, and several converging flows on the congestion link are equalized to several alternative paths. Simulation results show that the three algorithms have different advantages in different performance.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP393.0

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相關博士學位論文 前1條

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本文編號：2321128