Characterizing and Modeling Traffic of Communication and Collaboration Apps Bloomed With COVID-19 Outbreak

Abstract

In this work, we address the characterization and modeling of the network traffic generated by communication and collaboration apps which have been the object of recent traffic surge due to the COVID-19 pandemic spread. In detail, focusing on five of the top popular mobile apps (collected via the MIRAGE architecture) used for working/studying during the pandemic time frame, we provide characterization at trace and flow level, and modeling by means of Multimodal Markov Chains for both apps and related activities. The results highlight interesting peculiarities related to both the running applications and the specific activities performed. The outcome of this analysis constitutes the stepping stone toward a number of tasks related to network management and traffic analysis, such as identification/classification and prediction, and modern IT management in general.