Network Analysis : Simulating Customer Networks with GNS3

In an era where networks have become such complex and large structures, establishments with bigger networks may find it very daunting to make changes directly to their networking devices. This is because any changes done to the physical network equipment directly without any prior testing carries a high risk of fatal consequences due to misconfiguration. This includes commonly occurring issues like security vulnerabilities, suboptimal performance, downtime, and service disruptions. At the same time, the technologies of simulation and emulation reign supreme and are at their height with regard to today's technology. Organizations like Repay Invest AB, a client of Atea, face the issues discussed above. Navigating, testing, and implementing changes within Repay's expansive network, spanning numerous branch offices across Sweden, often feels like a high-stakes gamble. With each adjustment or configuration task, the potential for unforeseen consequences becomes bigger. This thesis embarks on a journey to analyze Repay's physical network and to construct a virtual counterpart using the renowned network emulation tool, GNS3. The reason for simulating Repay’s physical network into GNS3 is that they expressed the need for a safer, more agile approach to network experimentation and configuration testing; their request serves as the impetus for this thesis. The primary objective of this thesis is to conduct an analysis of Repay’s physical network and simulate it using GNS3 with a high level of accuracy. Additionally, the thesis also aims to compare today's modern networks to legacy/traditional network architectures. This comparison serves as background information for discussing the decisive and crucial changes that traditional networks underwent to adapt to the continuously expanding and advancing network structures. Furthermore, the solutions that traditional networks had to utilize during the changes will be brought up, highlighting the importance of software-defined networ