A Social Network Under Social Distancing: Risk-Driven Backbone Management During COVID-19 and Beyond

ABSTRACT

As the COVID-19 pandemic reshapes our social landscape, its lessons have far-reaching implications on how online service providers manage their infrastructure to mitigate risks. This paper presents Facebook's risk-driven backbone management strategy to ensure high service performance throughout the COVID-19 pandemic. We describe Risk Simulation System (RSS), a production system that identifies possible failures and quantifies their potential severity with a set of metrics for network risk. With a year-long risk measurement from RSS we show that our backbone resiliently withstood the COVID-19 stress test, achieving high service availability and low route dilation while efficiently handling traffic surges. We also share our operational practices to mitigate risk throughout the pandemic. Our findings give insights to further improve risk-driven network management. We argue for incorporating short-term failure statistics in modeling failures. Common failure prediction models based on long-term modeling achieve stable output at the cost of assigning low significance to unique short-term events of extreme importance such as COVID-19. Furthermore, we advocate augmenting network management techniques with non-networking signals. We support this by identifying and analyzing the correlation between network traffic and human mobility.