Hindawi Publishing Corporation
Mathematical Problems in Engineering
Volume , Article ID , pages
http://dx.doi.org/.//
Research Article
Congestion Control Based on Multiple Model Adaptive Control
Xinhao Yang
1
and Ze Li
2
1
Department of Mechanical and Electric Engineering, Soochow University, Suzhou 215006, China
2
College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Correspondence should be addressed to Xinhao Yang; yangxinhao@.com
Received August ; Accepted October
Academic Editor: Baoyong Zhang
Copyright © X. Yang and Z. Li. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e congestion controller based on the multiple model adaptive control is designed for the network congestion in TCP/AQM
network. As the conventional congestion control is sensitive to the variable network condition, the adaptive control method is
adopted in our congestion control. e multiple model adaptive control is introduced in this paper based on the weight calculation
instead of the parameter estimation in past adaptive control. e model set is composed by the dynamic model based on the
uid ow. And three “local” congestion controllers are nonlinear output feedback controller based on variable RTT, H
2
output
feedback controller, and proportional-integral controller, respectively. Ns- simulation results in section indicate that the proposed
algorithm restrains the congestion in variable network condition and maintains a high throughput together with a low packet drop
ratio.
1. Introduction
In recent years, with the rapid growth of network size and
network applications, congestion control has been exposed
as an essential factor in communication network design.
Congestion [] occurs when the aggregate demand for a
resource exceeds the available capacity of the resource, which
may deteriorate the performance and the reliability of the
network. Resulting eects from such congestion include long
delays in data delivery, wasted resources due to lost or
dropped packets, and even possible congestion collapse [],
in which all communications cease in the entire network.
TCP can only provide best eort service, in which the
tracisprocessedasquicklyaspossible,butthereisno
guarantee as to timeliness or actual delivery []. Moreover,
itisdicultforthedatasourcetoperceivethenetwork
condition. When the incoming packet rate is higher than the
router’s outgoing packet rate, the queue size will increase and
eventually give rise to the congestion. e queue management
scheme in router will use queue to smooth spike in the
incoming packet rates. In the Drop Tail (DT) policy which
is the most extensive dropping policy, the packet will be
droppedwhenitarrivesandndsthequeuefull.Ithasbeen
shownthattheDTmechanisminteractsbadlywithTCP’s
congestion control mechanisms and could lead to a poor
performance [].
Inthesametime,ActiveQueueManagement(AQM)is
the early notication of incipient congestion so that TCP
senders can reduce their transmission rate before the queue
overows []. Random Early Detection (RED) [, ]is
an important AQM mechanism, which is recommended
by Internet Engineering Task Force (IETF) []. e basic
idea behind RED queue management is to detect incipient
congestion early and convey congestion notication to the
end hosts, allowing them to reduce their transmission rates
before queues in the network overow and packets are
dropped. To fulll this aim, RED maintains an exponentially
weighted moving average of the queue length which it uses
to detect congestion. RED takes an average measure of the
queue length and randomly drop packets that are within
a threshold between min
th
and max
th
.Asaresult,RED
requires a wide range of parameters to operate correctly
under dierent congestion scenarios. When RED parameters
are not correctly dened, RED may perform even worse than
the traditional tail drop policy [, ].
To solve the problem of the parameter setting in RED,
application of the control theory to solve the congestion
problem has been considered since late s []. In such