In this paper, we present a model for evaluating bandwidth sharing
policies, that can be applied to networks that handle both video streaming
traffic, as well as other traffic. Video streaming is a demanding network
application. In crowded networks, resources need to be properly divided in
order not to diminish the streaming experience. However, in network
deployments with a large number of users, the streaming performance cannot
be obtained straightforwardly from a sharing policy. Therefore, we propose
a Markov model that is compatible with Dynamic Adaptive Streaming over HTTP
(DASH), the major technology for video streaming over the Internet. If DASH
is combined with in-network resource management, its performance can be
significantly improved. Nevertheless, resource sharing policies need to be
configured. This requires evaluating of many different configurations. Real
deployments or network simulations demand many system resources and time.
In contrast, our model can quickly evaluate many configurations, and for
each configuration output the expected video bitrate and number of changes
in video bitrate. These two parameters play an important role in the
Quality of Experience of the viewer. In this paper, we demonstrate how our
model can be used to analyze and optimize resource sharing policies. As
such, our model is a useful tool for network administrators and allows them
to better provision and configure their networks.
%0 Conference Paper
%1 Kleinrouweler2016
%A Kleinrouweler, Jan Willem
%A Cabrero, Sergio
%A van der Mei, Rob
%A Cesar, Pablo
%B 28th International Teletraffic Congress (ITC 28)
%C Würzburg, Germany
%D 2016
%K itc itc28
%T A Markov Model for Evaluating Resource Sharing Policies for DASH
Assisting Network Elements
%U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc28/Kleinrouweler2016.pdf?inline=true
%X In this paper, we present a model for evaluating bandwidth sharing
policies, that can be applied to networks that handle both video streaming
traffic, as well as other traffic. Video streaming is a demanding network
application. In crowded networks, resources need to be properly divided in
order not to diminish the streaming experience. However, in network
deployments with a large number of users, the streaming performance cannot
be obtained straightforwardly from a sharing policy. Therefore, we propose
a Markov model that is compatible with Dynamic Adaptive Streaming over HTTP
(DASH), the major technology for video streaming over the Internet. If DASH
is combined with in-network resource management, its performance can be
significantly improved. Nevertheless, resource sharing policies need to be
configured. This requires evaluating of many different configurations. Real
deployments or network simulations demand many system resources and time.
In contrast, our model can quickly evaluate many configurations, and for
each configuration output the expected video bitrate and number of changes
in video bitrate. These two parameters play an important role in the
Quality of Experience of the viewer. In this paper, we demonstrate how our
model can be used to analyze and optimize resource sharing policies. As
such, our model is a useful tool for network administrators and allows them
to better provision and configure their networks.
@inproceedings{Kleinrouweler2016,
abstract = {In this paper, we present a model for evaluating bandwidth sharing
policies, that can be applied to networks that handle both video streaming
traffic, as well as other traffic. Video streaming is a demanding network
application. In crowded networks, resources need to be properly divided in
order not to diminish the streaming experience. However, in network
deployments with a large number of users, the streaming performance cannot
be obtained straightforwardly from a sharing policy. Therefore, we propose
a Markov model that is compatible with Dynamic Adaptive Streaming over HTTP
(DASH), the major technology for video streaming over the Internet. If DASH
is combined with in-network resource management, its performance can be
significantly improved. Nevertheless, resource sharing policies need to be
configured. This requires evaluating of many different configurations. Real
deployments or network simulations demand many system resources and time.
In contrast, our model can quickly evaluate many configurations, and for
each configuration output the expected video bitrate and number of changes
in video bitrate. These two parameters play an important role in the
Quality of Experience of the viewer. In this paper, we demonstrate how our
model can be used to analyze and optimize resource sharing policies. As
such, our model is a useful tool for network administrators and allows them
to better provision and configure their networks.},
added-at = {2016-08-31T16:30:53.000+0200},
address = {Würzburg, Germany},
author = {Kleinrouweler, Jan Willem and Cabrero, Sergio and van der Mei, Rob and Cesar, Pablo},
biburl = {https://www.bibsonomy.org/bibtex/2a53c2cad1d3042d3f0e0c2d9dc6d312e/itc},
booktitle = {28th International Teletraffic Congress (ITC 28)},
days = {12},
interhash = {ddcf14aafc4c1a1328050faa04ba95d2},
intrahash = {a53c2cad1d3042d3f0e0c2d9dc6d312e},
keywords = {itc itc28},
month = {Sept},
timestamp = {2020-05-26T16:53:35.000+0200},
title = {A Markov Model for Evaluating Resource Sharing Policies for DASH
Assisting Network Elements},
url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc28/Kleinrouweler2016.pdf?inline=true},
year = 2016
}