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DropTail Based ConEx Applied to Video Streaming

Authors:
Ali Sanhaji
Philippe Niger
Philippe Cadro
André-Luc Beylot

Keywords: ConEx; ECN; Congestion; policing; YouTube;

Abstract:
With Internet traffic ever increasing, network congestion should occur more and more frequently. During congestion periods, some users contribute more than others to the congestion in the network. It might be interesting for a network operator to differentiate between users proportionally to the congestion they induce, but the necessary information for this purpose is not available at the network layer, and is exchanged at the transport layer (e.g., Transmission Control Protocol (TCP) acks). This led the Internet Engineering Task Force (IETF) to design Congestion Exposure (ConEx), a new mechanism to expose to the network the amount of congestion a user is responsible for. However, ConEx needs other mechanisms such as Random Early Detection (RED), Explicit Congestion Notification (ECN) and a number of modifications to the senders and receivers to be fully operational. Nonetheless, it is deployable with few modifications by relying only on loss information in DropTail queues to improve the fairness between users. The aim of this paper is to provide a comparison between the performance in terms of fairness improvement provided by ConEx with few modifications and by ConEx with complete modifications. Firstly, we will see that despite the limited accuracy due to the few changes, ConEx still provides good fairness improvement between users. Secondly, we will discuss the weaknesses ConEx presents with regard to short-lived flows. Finally, we will show how ConEx can help during congestion periods to enhance the Quality of Experience (QoE) of video streaming users (based on a YouTube traffic model).

Pages: 3 to 10

Copyright: Copyright (c) IARIA, 2015

Publication date: May 24, 2015

Published in: conference

ISSN: 2308-4006

ISBN: 978-1-61208-404-6

Location: Rome, Italy

Dates: from May 24, 2015 to May 29, 2015