SDN-Enabled Data Offloading and Load Balancing In WLAN and Cellular Networks

Networking is an interesting field that is always evolving as new technologies that connect the world are adopted. There are many distinct types of networks, each of which is classified in a different way. One categorization is based on cellular wireless network generations, which have progressed from 1G to 5G. Several additional interconnected technologies have emerged as networking has progressed. SDN (Software Defined Networking) is a significant networking technology that represents a new paradigm. SDN distinguishes between the data and control planes and allows software-controlled networking for a wide range of applications. Cellular networks are critical for sending digital data from mobile or stationary senders to mobile or stationary receivers in wireless networks. Cellular networks are currently experiencing a data explosion because of the ever-increasing bandwidth demands of today's mobile applications. This has resulted in traffic congestion and a scarcity of resources. The network must handle a high volume of traffic and serve many customers, which may result in poor service quality for users. A single access network struggles to handle such a tremendous volume of traffic. Operators of cellular networks are attempting to alleviate the problem by offloading mobile data from cellular networks to complementary networks like Wi-Fi. However, without centralized control, traffic offloading may not significantly improve overall network load balancing, network usage, or users' quality of experience. This dissertation proposes a traffic offloading and load balancing algorithm between cellular and Wi-Fi networks, to enhance the overall utilization of cellular network. The proposed algorithm uses an SDN controller for making decisions of offloading users from a cellular network to a Wi-Fi network and to balance the load across access points. The algorithm makes use of the SDN controller's view in making decisions. Simulation results obtained show that the proposed data offloading scheme improves load distribution and throughput.

Grantor

Department of Electrical Engineering

Year dc:date.issued

2022

Author dc:creator

• Ashipala, Aili Kanee

Advisor dc:contributor.advisor

• Falowo, Olabisi

• Electrical Engineering

Handle dc:identifier.uri

http://hdl.handle.net/11427/36973

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/36973