Traffic Characterization of Social Network Applications

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.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher.institution

Science

Year dc:date.issued

2021

Author dc:creator

• Keshvadi, Sina

Advisor dc:contributor.advisor

• Williamson, Carey

Committee members dc:contributor.committeemember

• Kawash, Jalal
• Henry, Ryan
• Brecht, Tim
• Krishnamurthy, Diwakar

• Social Networks
• Traffic Measurement
• Network Characterization
• Network Performance

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/39236

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/113919