The power of fuzzy logic appears when we begin to analyze a system using linguistics . For example, let's say that we're building a QoS (Quality of Service) algorithm that manages the output of packets over a particular link. The purpose of the QoS algorithm is to provide a given application a consistent amount of bandwidth of the link. If the application tries to use too much of the link's bandwidth, we must reduce the rate that packets are emitted for the given application. From a control perspective, we have three elements. The first is the packet arrival rate from the application, the second is the measured utilization of the link, and the third is the gate that controls the flow of packets between the application and the link (see Figure 9.1).
The purpose of the gate is to control when and how many packets are permitted to pass given the bandwidth allocated to the particular application. When we think about this problem, we consider it in terms of linguistics. For example:
If the application utilization of the link is high, then reduce the rate of flow of packets through the gate for the application.
Conversely,
If the application utilization of the link is low, then increase the rate of flow of packets through the gate for the application.
These rules imply that there exists a dead-zone between the high and low rates that is "about right" for the application
The question is now what is "high" and "low" for the application rate and what is "about right"? Fuzzy logic determines this using membership functions (see Figure 9.2).