A novel method to find the best path in SDN using firefly algorithm

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2022-01-01 DOI:10.1515/jisys-2022-0063
Tameem Hameed Obaida, Hanan Abbas Salman
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引用次数: 2

Abstract

Abstract Over the previous three decades, the area of computer networks has progressed significantly, from traditional static networks to dynamically designed architecture. The primary purpose of software-defined networking (SDN) is to create an open, programmable network. Conventional network devices, such as routers and switches, may make routing decisions and forward packets; however, SDN divides these components into the Data plane and the Control plane by splitting distinct features away. As a result, switches can only forward packets and cannot make routing decisions; the controller makes routing decisions. OpenFlow is the communication interface between the switches and the controller. It is a protocol that allows the controller to identify the network packet’s path across the switches. This project uses the SDN environment to implement the firefly optimization algorithm to determine the shortest path between two nodes in a network. The firefly optimization algorithm was implemented using Ryu control. The results reveal that using the firefly optimization algorithm improves the selected short path between the source and destination.
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一种利用萤火虫算法寻找SDN中最佳路径的新方法
在过去的三十年里,计算机网络领域从传统的静态网络发展到动态设计的体系结构,取得了长足的进步。软件定义网络(SDN)的主要目的是创建一个开放的、可编程的网络。传统的网络设备,如路由器和交换机,可以做出路由决定并转发数据包;然而,SDN通过分离不同的特性将这些组件划分为数据平面和控制平面。因此,交换机只能转发数据包,不能做出路由决策;控制器做出路由决策。OpenFlow是交换机和控制器之间的通信接口。它是一种协议,允许控制器识别网络数据包在交换机之间的路径。本项目使用SDN环境实现萤火虫优化算法,确定网络中两个节点之间的最短路径。萤火虫优化算法采用Ryu控制实现。结果表明,采用萤火虫优化算法可以提高源和目标之间选择的短路径。
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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