Leveraging SDN for Load Balancing on Campus Network (CN)

Karnani, Suruchi; Shakya, Harish Kumar

doi:10.1109/cicn51697.2021.9574640

Cited by 2 publications

(1 citation statement)

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“…In a research article [31], the authors implemented the dynamic and static load-balancing methods and drew their fruitful effects. In a research article [32,33], the authors implemented load balancing with SDN on Campus Networks (CN).…”

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Enhancing QoS of Telecom Networks through Server Load Management in Software-Defined Networking (SDN)

Mehmood,

Atiq,

Hussain

2023

Sensors

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In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users’ hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased.

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confidence: 99%