Estimating 5G Network Service Resilience Against Short Timescale Traffic Variation

5G networks are designed to create a new ecosystem for vertical industries such as health care, energy, and public transport. These novel applications, on the other hand, bring new challenges to network resilience. Among them, traffic variation is one of the most vital threats to the 5G network. With tens of thousands of devices connected to the network, network service resilience is threatened by the heavy traffic change induced by the end users or malicious attacks. While long timescale traffic variation can be easily predicted based on historical data, short timescale abnormal traffic is hard to forecast yet can significantly violate the service requirements. The impact of short timescale traffic variation can be mitigated by 5G management and control systems. However, the complexity and dynamics of the virtualized 5G system make it hard to estimate its resilience. This paper provides a 5G network model that captures the data traffic changes and network dynamic management mechanism. The model is able to evaluate the performance of different network services with different requirements under traffic variation events. We analyze the effectiveness of auto-scaling and compare different isolation strategies for traffic congestion. The simulation results on service resilience estimation can become strong supporting information for 5G network deployment and configuration.