Online Social Networks (OSNs) are popular tools for billions of people around the globe to communicate with each other. With the popularity of mobile devices and ubiquitous network connectivity, global Internet traffic, including OSN and video streaming traffic, has grown rapidly. Many OSNs recoup their operational costs through advertising and data analytics, which raises concerns about what user-level information is collected by these sites, and where such information is sent. Understanding the network traffic generated by OSNs provides better insight into these services, and their performance.

In this dissertation, we use active and passive measurement techniques to study the network traffic generated by OSNs on a large campus edge network and use the resulting insights for analysis and characterization of these applications.

We designed and implemented MoVIE (Mobile Video Information Extraction), an active measurement and video streaming measurement tool, that provides visibility into the network traffic generated from a smartphone under test. We used our tool to investigate several OSN sites (e.g., Instagram, WeChat, Snapchat), as well as free live streaming (FLS) providers that share their content through online discussion social networks such as Reddit.

We conducted passive measurements on a large campus edge network to analyze the properties of OSNs and characterize the traffic of these applications at scale. We identified the key characteristics of Instagram and four popular instant messaging apps on a large campus edge network. The main observations from our study indicate a rich ecosystem of online social apps, many of which exhibit strong diurnal patterns, complex user interactions, and heavy-tailed distributions for connection durations and transfer sizes. Instagram exceeded 1 TB of daily traffic volume on our campus network and the four IM apps contributed about 650 GB per day.

By analyzing and characterizing the network traffic on a campus network, this dissertation provides a better understanding of OSN applications and their possible future traffic demands.