Electronic Thesis and Dissertation Repository

Thesis Format



Master of Science


Computer Science


Haque, Anwar


The International Data Corporation (IDC) estimated that total digital data created, replicated, and consumed was 4.4 Zettabytes (ZB) in the year 2013, 8 ZB in 2015, and predicted to reach 40 ZB by 2020. This massive amount of internet traffic put a great overhead on network capacity which may impact network Quality of Service (QoS) such as latency, jitter, throughput, packet loss, and load balancing. From the Internet Service Provider’s (ISP’s) perspective, understanding the possible impact of the future internet traffic on its network is critical for provisioning their network capacity in a cost-effective manner while meeting network QoS requirements. In order to achieve the above goal, one needs a framework that is capable of taking input from the traffic forecast, assign traffic load over the networks, and then identify the impact on the existing traffic QoS status (latency, jitter, packet loss, throughput, etc. In this paper, we developed a network planning framework namely Network Impact Modelling and Analysis (NIMA) that uses novel methods and techniques to predict the congestion level of the network, alerts network planners on the links that are subject to a high-risk group, indicates the impact on network-wide latency, and finally suggests an optimal routing strategy that can improve the overall network health. As part of this optimal routing task, we used Yen’s algorithm which showed performance improvement when compared with Dijkstra’s algorithm and Suurballe’s k-disjoint algorithm. For simulation purposes, we used Mininet in a combination with a floodlight controller for implementation. The experiments are performed on different sized topologies to test the effectiveness of our proposed framework.

Summary for Lay Audience

The rapid growth in Internet-based services and applications results in a dramatic increase in internet traffic. A huge amount of data in the form of text, video, and real-time streaming results in an excessive increase in network traffic. As a result, congestion occurs over the network which impacts the network’s Quality of Service (QoS). Internet Service Providers (ISPs) need cost-effective models for timely detection of risk-prone network links to handle the congestion in order to maintain network health.

In this thesis, we develop a traffic model to analyze the impact of forecasted traffic on the network’s QoS metrics i.e. network health. Our proposed framework namely Network Impact Modelling and Analysis (NIMA) evaluates and analyses the congestion level of the network due to overlaying forecasted traffic on the existing network traffic. Furthermore, NIMA also evaluates the QoS parameters like Latency, Throughput, Jitter, Packet loss, Utilization, Load Balancing for both current and forecasted traffic. NIMA identifies high-risk (highly utilized) links and alerts the network planners when such links are detected. In this way, NIMA can be used by network planners as an aid in decision making and policy designing. Finally, to improve the overall health of the network, we suggest an optimal routing algorithm.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.