Distance-Dependent RED Policy (DDRED)

International audienceThe network quality of service (QoS) and the congestion control of the transport protocol are important parameters for the performance of a network data transfer. To this end, routers use various queue policies for packet dispatching, and all of them must deal with packet drop. We propose a new algorithm for packet drop in routers. Given that a packet drop wastes all the network resources it has already used, we propose a new policy which favors packets with higher distance from source. It can be simply integrated on top of tail drop or RED (with or without ECN) queue policies. Simulations with NS2 show that long flows are indeed favored compared to short flows, and lead to higher overall resource utilisation without sacrificing TCP fairness

Adaptive Explicit Congestion Notification (AECN) for Heterogeneous Flows

Previous research on ECN and RED usually considered only a limited traffic domain, focusing on networks with a small number of homogeneous flows. The behavior of RED and ECN congestion control mechanisms in TCP network with many competing heterogeneous flows in the bottleneck link, hasn’t been sufficiently explored. This thesis first investigates the behavior and performance of RED with ECN congestion control mechanisms with many heterogeneous TCP Reno flows using the network simulation tool, ns-2. By comparing the simulated performance of RED and ECN routers, this study finds that ECN does provide better goodput and fairness than RED for heterogeneous flows. However, when the demand is held constant, the number of flows generating the demand has a negative effect on performance. Meanwhile, the simulations with many flows demonstrate that the bottleneck router's marking probability must be aggressively increased to provide good ECN performance. Based on these simulation results, an Adaptive ECN algorithm (AECN) was studied to further improve the goodput and fairness of ECN. AECN divides all flows competing for a bottleneck into three flow groups, and deploys a different max for each flow group. Meanwhile, AECN also adjusts min for the robust flow group and max to get higher performance when the number of flows grows large. Furthermore, AECN uses mark-front strategy, instead of mark-tail strategy in standard ECN. A series of AECN simulations were run in ns-2. The simulations show clearly that AECN treats each flow fairer than ECN with the two fairness measurements: Jain’s fairness index and visual max-min fairness. AECN has fewer packet drops and alleviates the lockout phenomenon and yields higher goodput than ECN