FUZZY LOGIC CONTROL IN TCP/IP NETWORKS
C. Chrysostomou, G. Hadjipollas and A. Pitsillides
Department of Computer Science, University of Cyprus
P.O. Box 20537, 1678 Nicosia, Cyprus
Tel.: +357-22892700; Fax: +357-22892701
e-mail: {cchrys, hpollas, andreas.pitsillides}@ucy.ac.cy
Keywords: Fuzzy Logic, Active queue management, TCP/IP networks, Differentiated Services, Quality of Service.
Abstract - This paper presents a new active queue
management (AQM) scheme - Fuzzy Explicit Marking
(FEM) - supporting explicit congestion notification
(ECN), to provide congestion control in TCP/IP networks
using a fuzzy logic control approach. The proposed
scheme is implemented within both best-effort and
differentiated services (Diff-Serv) environments,
providing quality of service (QoS). While many AQM
mechanisms have recently been proposed, these require
careful configuration of non-intuitive control parameters,
and show weaknesses to detect and control congestion
under dynamic traffic changes, and a slow response to
regulate queues. Furthermore, the provision of QoS in a
Diff-Serv environment requires an adequate
differentiation between assured and best-effort classes of
service in the presence of congestion, giving priority to
assured-tagged traffic. For this purpose, a two-class FEM
controller, called FEM In/Out (FIO), is also presented.
The proposed fuzzy logic approach for congestion control
allows the use of linguistic knowledge to capture the
dynamics of nonlinear probability marking functions,
uses multiple inputs to capture the (dynamic) state of the
network more accurately, and can offer effective
implementation. A simulation study over a wide range of
traffic conditions shows that both FEM and FIO
controllers outperform a number of representative AQM
schemes in terms of queue fluctuations and delays, packet
losses, and link utilization.
I. INTRODUCTION
The rapid growth of the Internet and increased demand
to use the Internet for time-sensitive applications
necessitate the design and utilization of effective
congestion control algorithms. As a result, the
differentiated services (Diff-Serv) architecture was
proposed (S. Blake et al. 1998) to deliver (aggregated)
quality of service (QoS) in IP networks. Recently, many
active queue management (AQM) schemes have been
proposed to provide high network utilization with low
loss and delay by regulating queues at the bottleneck
links in TCP/IP networks, including random early
detection (RED) (Floyd and Jacobson 1993), adaptive
RED (A-RED) (Floyd et al. 2001), proportional-integral
(PI) controller (Hollot et al. 2002), and random
exponential marking (REM) (Athuraliya et al. 2001).
Also, RIO (Clark and Fang 1998) was proposed for Diff-
Serv control, to preferentially drop packets.
The AQM approach can be contrasted with the “Tail
Drop” (TD) queue management approach, employed by
common Internet routers, where the discard policy of
arriving packets is based on the overflow of the output
port buffer. Contrary to TD, AQM mechanisms (Braden
et al. 1998) start dropping packets earlier in order to be
able to notify traffic sources about the incipient stages of
congestion. AQM allows the router to separate policies of
dropping packets from the policies for indicating
congestion. The use of Explicit Congestion Notification
(ECN) (Ramakrishnan and Floyd 2001) was proposed in
order to provide TCP an alternative to packet drops as a
mechanism for detecting incipient congestion in the
network. The ECN scheme requires both end-to-end and
network support. An AQM-enabled gateway can mark a
packet either by dropping it or by setting a bit in the
packet’s header if the transport protocol is capable of
reacting to ECN. The use of ECN for notification of
congestion to the end-nodes generally prevents
unnecessary packet drops.
In this paper, a fuzzy logic based approach for
delivering an improved and more predictable congestion
control implementation in TCP/IP networks is presented.
We use fuzzy logic techniques to develop a new AQM
scheme, Fuzzy Explicit Marking (FEM), to provide
congestion control in TCP/IP networks, implemented in
both best-effort and Diff-Serv environments.
Fuzzy logic control is a widely used computational
intelligence technique for dealing with “soft” information
processing (Brown and Harris 1994; Passino and
Yurkovich 1998). It provides a system for designing
feedback control algorithms in such cases where the
system to be controlled is too complex to employ
classical control methods. Fuzzy logic becomes especially
useful on capturing human expert or operator’s
qualitative control experience into the control algorithm.
This algorithm is represented typically in the form of
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