Fuzzy logic traffic control in broadband communication networks

Abstract

Traffic predictions have been demonstrated with the capability to improve network efficiency and QoS in broadband ATM networks. Recent research shows that fuzzy logic prediction outperforms conventional autoregression predictions. The application of fuzzy logic also has a potential to control traffic more effectively. In this paper, we propose the use of the fuzzy logic prediction on connection admission control (CAC) and congestion control on high speed networks. We first modeled traffic characteristics using an on-line fuzzy logic predictor on CAC. Simulation results show that fuzzy logic prediction improves the efficiency of both conventional and measurement-based CAC. In addition, the measurement-based approach incorporating fuzzy logic inference and using fuzzy logic prediction is shown to achieve higher network utilization while maintaining QoS. We then applied the fuzzy logic predictor to congestion control in which the ABR queue is estimated one round-trip in advance. Simulation results show that the fuzzy logic control scheme significantly reduces convergence time and overall buffer requirements as compared with conventional schemes.