Title
Understanding flow performance in the wild
Abstract
Recent Internet studies have reported on continued traffic growth and popularity of web-based applications. Any adverse impact that these observed trends may have on Internet traffic flows can result in sub par performance, which in turn results in unsatisfactory user experience. Leveraging data collected at a major content distribution network (CDN), we investigate flow-level performance in the wild. We observe that packet losses differ widely across flows of different sizes, and even for flows of similar size. To shed light on these observations, we rely on a controlled testbed setup with advanced instrumentation via NetFPGA cards. We highlight the key factors which can degrade flow-performance across different network loads and flow-size distributions. We find that packet losses do not affect all flows similarly. Depending on the network load, some flows either suffer from significantly more drops (unhappy flows) or significantly less drops than the average loss rate (happy flows). Very few flows actually observe a loss rate similar to the average loss rate. Therefore, any single flow is very unlikely to observe the global packet loss process. Furthermore, we find that some flows are burstier than others as indicated by their average congestion window.
Year
DOI
Venue
2013
10.1109/GLOCOM.2013.6831271
Global Communications Conference
Keywords
Field
DocType
Internet,telecommunication traffic,CDN,Internet traffic flows,NetFPGA cards,average congestion window,average loss rate,content distribution network,flow-level performance,flow-size distributions,global packet loss process,network loads,packet losses,unhappy flows,unsatisfactory user experience,wild
Traffic generation model,Internet transit,User experience design,Computer science,Internet traffic engineering,Computer network,Real-time computing,Internet studies,Network traffic control,Internet traffic,The Internet
Conference
ISSN
Citations 
PageRank 
2334-0983
1
0.35
References 
Authors
16
4
Name
Order
Citations
PageRank
Muhammad Amir Mehmood1202.86
Nadi Sarrar237723.12
Steve Uhlig32209108.27
Anja Feldmann44935596.02