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.