A novel approach for message scheduling

In time critical system, message scheduling plays an important role to arbitrate fair service among all competing messages, where messages are conditioned on different timing constraints. There already exist many algorithms, including static and dynamic scheduling, to resolve these problems. In this paper, we extend our recent work by presenting a fuzzy inference system (FIS) as a message classifier for scheduling. The FIS with adaptation strategy of parameter adaptation and structure identification can always result in smaller untimely service ratios (USRs) and small number of fuzzy rules, especially when the traffic load is heavy. Moreover, the resulting FIS model would not remain a black box. Instead, the implicit scheduling knowledge can be interpreted in terms of linguistic fuzzy sets. The efficiency of the proposed method is further examined by comparing with several traditional scheduling methods. Simulation results confirm our claims consistently.