软件定义网络中动态路由的蚁群优化算法

O. Raouf, Heba Askr
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引用次数: 5

摘要

近年来,软件定义网络(SDN)正在以较低的成本取代传统的网络架构管理。SDN旨在引入一个核心控制器中的集中式智能网络。OpenFlow被认为是SDN中最常用的南向API。现有的路由优化算法是有效的,但时间和空间复杂度较高。这种复杂性为研究人员使用启发式技术来优化基于ofs的sdn中的动态路由打开了大门。在SDN控制器层(SDN大脑)引入智能优化路由技术的尝试很少。本文提出了一种改进的蚁群优化算法“ACOSDN”来优化sdn中的动态路由。将该算法与其他相关工作和SDN中的其他路由技术进行了比较,并对其有效性进行了测量,结果表明该算法能够处理动态网络变化,减少网络拥塞,在较低的延迟和丢包率下实现更高的吞吐量。
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ACOSDN-Ant Colony Optimization Algorithm for Dynamic Routing In Software Defined Networking
Recently, Software Defined Networking (SDN) is emerging to replace traditional network architecture management at a reduced cost. SDN aims to introduce a centralized intelligent network in a core controller. OpenFlow (OF) is considered the most commonly used southbound API in SDN. Existing routing optimization algorithms are effective but at high order of time and space complexity. This complexity opens the door for the researchers to use the heuristic techniques to optimize the dynamic routing in OF-based SDNs. There are few attempts to introduce intelligent optimization routing technique at SDN controller layer (SDN brain). This paper suggests a modified Ant Colony Optimization (ACO) algorithm called “ACOSDN” to optimize the dynamic routing in SDNs. The suggested algorithm is compared to other related work and other routing techniques in SDN, the effectiveness is measured and the results show that the algorithm is able to handle dynamic network changes, reduce the network congestion and achieve higher throughput associated with a lower delay and packet loss rates.
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