Abstract

The concept of Software-Defined Networking (SDN) evolves to overcome the drawbacks of the traditional networks with Internet Protocol (I.P.) packets sending and packets handling. The SDN structure is one of the critical advantages of efficiently separating the data plane from the control plane to manage the network configurations and network management. Whenever there are multiple sending devices inside the SDN network, the OpenFlow switches are programmed to handle the limited number of requests for their interface. When the recommendations are exceeded from the specific threshold, the load on the switches also increases. This research article introduces a new approach named LBoBS to handle load balancing by adding the load balancing server to the SDN network. Besides, it is used to maximize SDN’s reliability and efficiency. It also works in coordination with the controller to effectively handle the load balancing policies. The load balancing server is implemented to manage the switches load effectively. Results are evaluated on the NS-3 simulator for packet delivery, bandwidth utilization, latency control, and packet decision ratios on the OpenFlow switches. It has been found that the proposed method improved SDN’s load balancing by 70% compared to the previous state-of-the-art methods.

Highlights

  • In recent years, multimedia technology has shown tremendous growth

  • The load balancing server directly connects with the controller and Software-Defined Networking (SDN) switches in our approach, so the load balancing load is divided

  • This article investigated load balancing in the SDN-based networks where multiple servers are added, and numerous domains maintain them

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Summary

Introduction

This advancement has enabled high-quality video streaming and other applications; they suffer from heavy congestion and load on the network; one specialized server must monitor the load balancing in the network. The use of learning technologies is currently untilized in the network systems to make informed decisions about the load and its balancing and find optimal routes. Such applications do not scale well, and scaling becomes cumbersome due to the non-convergence of optimization paths [2]

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