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NETWORK CONGESTION ARBITRATION AND SOURCE PROBLEM PREDICTION USING NEURAL NETWORKS.pdf
Smart Engineering System Design, vol. 4:243-252, 2002
NETWORK CONGESTION ARBITRATION AND SOURCE PROBLEM
PREDICTION USING NEURAL NETWORKS
J. ALAN BIVENS BOLESLAW K. SZYMANSKI
Computer Science Computer Science
Rensselaer Polytechnic Institute Rensselaer Polytechnic Institute
Troy, New York 12 180-3590 Troy, New York 12 180-3590
bivenj @ szymansk@
MARK J. EMBRECHTS
Decision Sciences And Engineering Systems
Rensselaer Polytechnic Institute
Troy, New York 12 180-3590
EMBREM@RPI.EDU
ABS TRA CT
Rapidly growing needs for networking in the Internet and in intranets make network
management increasingly important in todays computer world. We propose using
learning techniques to predict network congestion problems before they start impacting
the performance of services. In this paper we focus on using a simple feedforward neural
network to predict severe congestion in a network. We also use neural networks to
predict the source or sources responsible for the congestion, and we design and apply a
simple control method for limiting the rate of the offending sources so that congestion
can be avoided. Unlike the usual TCP/IP flow control, the proposed method is applied
only to selected nodes and converges to the final rate faster. The described techniques set
the stage for a new wave of network managers that are capable of preventing networking
problems instead of repairing them.
KEYWORDS: congestion control, neural networks, weight pruning, network problem
prediction, flow rate control
INTRODUCTION
In the growing world of networking, more and more emphasis is being
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