A per-Application Account of Bufferbloat: Causes and Impact on Users
Résumé
We propose a methodology to gauge the extent
of queueing delay (aka bufferbloat) in the Internet, based on
purely passive measurement of TCP traffic. We implement our
methodology in Tstat and make it available as open source
software. We leverage Deep Packet Inspection (DPI) and behavioral classification of Tstat to breakdown the queueing delay
across different applications, in order to evaluate the impact
of bufferbloat on user experience. We show that there is no
correlation between the ISP traffic load and the queueing delay,
thus confirming that bufferbloat is related only to the traffic of
each single user (or household). Finally, we use frequent itemset
mining techniques to associate the amount of queueing delay seen
by each host with the set of its active applications, with the goal
of investigating the root cause of bufferbloat.