As more individuals transmit data through a computer network, the quality of service received by the users
begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing.
A more effective method for routing data through a computer network can assist with the new problems being
encountered with today’s growing networks. Effective routing algorithms use various techniques to determine
the most appropriate route for transmitting data. Determining the best route through a wide area network
(WAN), requires the routing algorithm to obtain information concerning all of the nodes, links, and devices
present on the network. The most relevant routing information involves various measures that are often
obtained in an imprecise or inaccurate manner, thus suggesting that fuzzy reasoning is a natural method to
employ in an improved routing scheme. The neural network is deemed as a suitable accompaniment because it
maintains the ability to learn in dynamic situations.
Once the neural network is initially designed, any alterations in the computer routing environment can
easily be learned by this adaptive artificial intelligence method. The capability to learn and adapt is
essential in today’s rapidly growing and changing computer networks. These techniques, fuzzy reasoning
and neural networks, when combined together provide a very effective routing algorithm for computer
networks. Computer simulation is employed to prove the new fuzzy routing algorithm outperforms the
Shortest Path First (SPF) algorithm in most computer network situations. The benefits increase as the
computer network migrates from a stable network to a more variable one. The advantages of applying this
fuzzy routing algorithm are apparent when considering the dynamic nature of modern computer networks.
Applying artificial intelligence to specific areas of network management allows the network engineer
to dedicate additional time and effort to the more specialized and intricate details of the system.
Many forms of artificial intelligence have previously been introduced to network management; however,
it appears that one of the more applicable areas, fuzzy reasoning, has been somewhat overlooked.
Computer network managers are often challenged with decision-making based on vague or partial
information. Similarly, computer networks frequently perform operational adjustments based on this
same vague or partial information. The imprecise nature of this information can lead to difficulties
and inaccuracies when automating network management using currently applied artificial intelligence
techniques. Fuzzy reasoning will allow this type of imprecise information to be dealt with in a
precise and well-defined manner, providing a more flawless method of automating the network
management decision making process.
The objective of this research is to explore the use of fuzzy reasoning in one area of network
management, namely the routing aspect of configuration management. A more effective method for
routing data through a computer network needs to be discovered to assist with the new problems
being encountered on today’s networks. Although traffic management is only one aspect of
configuration management, at this time it is one of the most visible networking issues. This
becomes apparent as consideration is given to the increasing number of network users and the
tremendous growth driven by Internet-based multimedia applications. Because of the number of
users and the distances between WAN users, efficient routing is more critical in wide area
networks than in LANs (also, many LAN architectures such as token ring do not allow any
flexibility in the nature of message passing). In order to determine the best route over the
WAN, it is necessary to obtain information concerning all of the nodes, links, and LANs present
in the wide area network. The most relevant routing information involves various measures
regarding each link. These measures include the distance a message will travel, bandwidth
available for transmitting that message (maximum signal frequency), packet size used to segment
the message (size of the data group being sent), and the likelihood of a link failure. These
are often measured in an imprecise or inaccurate manner, thus suggesting that fuzzy reasoning
is a natural method to employ in an improved routing scheme.
Utilizing fuzzy reasoning should assist in expressing these imprecise network measures; however, there
still remains the massive growth issue concerning traffic levels. Most routing algorithms currently
being implemented as a means of transmitting data from a source node to a destination node cannot
effectively handle this large traffic growth. Most network routing methods are designed to be efficient
for a current network situation; therefore, when the network deviates from the original situation, the
methods begin to lose efficiency. This suggests that an effective routing method should also be capable
of learning how to successfully adapt to network growth. Neural networks are extremely capable of
adapting to system changes, and thus will be applied as a second artificial intelligence technique to
the proposed routing method in this research. The proposed routing approach incorporates fuzzy reasoning
in order to prepare a more accurate assessment of the network’s traffic conditions, and hence provide a
faster, more reliable, or more efficient route for data exchange. Neural networks will be incorporated
into the routing method as a means for the routing method to adapt and learn how to successfully handle
network traffic growth. The combination of these two tools is expected to produce a more effective
routing method than is currently available.
In order to achieve the primary objective of more efficient routing, several minor objectives also need
to be accomplished. A method of data collection is needed throughout the different phases of the study.
Data collection will be accomplished through the use of simulation methods; therefore, a simulation model
must be accurately designed before proceeding with experimenting or analysis. Additional requirements
include building and training the neural network and defining the fuzzy system. The objective of this
research is to demonstrate the effective applicability of fuzzy reasoning to only one area of network
management, traffic routing.