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The fifth generation (5G) of mobile telecommunication network is designed with an ambition to be a network faster, stronger, better and smarter than its predecessor. With the digital transformation, all industry sectors will develop new applications with new requirements regarding telecommunication networks that 5G should be able to meet. To meet the requirement of future 5G use cases and applications, it is crucial to study the complexity of such network system by distinguishing different parts, layers, components as well as their interdependencies. This paper describes the 5G networks from an End-to-End perspective (device, radio network, core network, data network) and from a multi-layer perspective (orchestration, virtualisation/containerization and infrastructure) to show how this system (or system of systems) is complex, especially when we address resilience challenges. Resilience requirements and challenges are further explained by proposing relevant scenarios and use cases. In this paper, we mainly intend to highlight 5G network complexity and open a discussion on methodologies to model such complex network for its resilience study with the hope that this paper could inspire the future study of researchers in the related field.

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The advent of 5G has enabled a wide variety of devices to access the network. With the digitization of industry, more and more vertical services, such as smart cities, remote health, and autonomous driving, rely on 5G networks for communication. These verticals bring new challenges to the telecommunication network resilience. Among them, sudden traffic change seems to be a critical challenge that impacts the resilience performance of 5G networks. This paper presents a network model for future 5G infrastructures based on Petri nets by taking into consideration the particularities of network virtualization and softwarization. This work also seeks to analyze the effectiveness of microservice-level autoscaling and network isolation by using discrete event simulation. The results suggest that both autoscaling and network isolation could increase network resilience when network traffic changes abruptly.