TBF and SFQ performance analysis using AODV UU on wireless ad-hoc network implementation

Abstract

Several traffic control algorithms on Queueing Discipline (qdisc) has been proposed to improve the ability qdisc in the variety of network applications. This paper has implemented two methods of queueing discipline, which are classless queueing Token Bucket Filter (TBF) and Stochastic Fair Queueing (SFQ). These qdisc were implemented by using Ad hoc On-Demand Distance Vector (AODV) protocol in several static-mobile and hop scenario over wireless ad-hoc network. In addition, we also examined the effect of tuning the parameters of each qdisc to the performance of TBF and SFQ, which perturb and quantum for SFQ, rate and burst for TBF. Based on the gained insight, both TBF and SFQ have better performance in static scenario. Tuning parameters on the value and specific scenario is quite influential in SFQ and TBF performance.