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Application of improved ant colony algorithm in load balancing of software-defined networks

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Abstract

Software-defined networking (SDN) separates the forwarding plane and control plane of the network equipment, adopts a centralized control mode to simplify network deployment and improve network management efficiency and realizes the network flexible control and management of traffic through programmable open interfaces. At present, it has been widely used in domestic and international data centre networks. With the explosive growth of the scale of data centres and the increase in user requirements for service quality, load balancing and congestion control of data centres have become significant issues in current research. After studying and analysing data centre SDN architecture and load balancing problems in detail, certain experts proposed an SDN load balancing algorithm, based on improved ant colony optimization-load balancing (IACO-LB). Firstly, the overall framework of SDN load balancing in data centres is studied, which is mainly divided into three parts: basic network equipment, OpenFlow protocol and controller. Among them, the controller constitutes the core of the entire load balancing system, including four modules: network topology awareness, status collection, the core of the load balancing algorithm and the flow table distribution. Then, an SDN load balancing algorithm, based on the improved ant colony optimization (IACO) is proposed to achieve dynamic load balancing of the SDN. The algorithm fully considers the performance parameters of network links and servers, and its design is based on the principle of selecting links and servers with low utilization. The evaluation methods of server module and link module are designed and the ant colony algorithm is used to find the global optimal solution. In order to prevent the algorithm from falling into local optimum, the Kent chaotic model is adopted to disturb the transition probability of the ant colony, by improving the basic ant colony algorithm. Finally, a network topology model was established in MATLAB to carry out simulation experiments. The results show that, compared with the equivalent multi-path algorithm and path server traffic scheduling algorithm, IACO-LB can effectively solve the load balancing problem of SDN and can dynamically adjust the routing scheme, according to the changes in network link traffic and server utilization. The algorithm converges quickly and can achieve a better global load balancing scheme.

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Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.

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Acknowledgements

This work was supported in part by special funds from the School of Electronic Information within the national double high programme at Dongguan Polytechnic (no.ZXD202213), in part by special projects in key fields of scientific research projects in colleges and universities of Guangdong Provincial Department of Education (no.2022ZDZX1074), in part by Dongguan Science and Technology Ombudsman Project (no.20221800500812), in part by Key scientific research platform project for colleges and universities of Guangdong Provincial Department of education(no.2021gczx016), in part by Guangdong Natural Science Youth Fund under grant (no.2020A1515110162), in part by key projects relating to social science and technology development in Dongguan under grant (no.2020507156156). The study was also supported in part by the special fund for science and technology innovation strategy of Guangdong Province under grant (no.pdjh2020a1261).

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Authors and Affiliations

  1. School of Electronic Information, Dongguan Polytechnic, Dongguan, 523808, China

    Huijun Zheng, Jianlan Guo & Yong Peng

  2. Information and Educational Technology Center, Dongguan Polytechnic, Dongguan, 523808, China

    Qin Zhou

  3. School of Artificial Intelligence, Dongguan Polytechnic, Dongguan, 523808, China

    Yuqiang Chen

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  1. Huijun Zheng
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  2. Jianlan Guo
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Correspondence to Huijun Zheng.

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Zheng, H., Guo, J., Zhou, Q. et al. Application of improved ant colony algorithm in load balancing of software-defined networks. J Supercomput 79, 7438–7460 (2023). https://doi.org/10.1007/s11227-022-04957-8

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