Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
On the equivalent bandwidth of self-similar sources
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on modeling and simulation of communication networks
Modeling and Performance Analysis of Self-Similar Traffic Based on FBM
NPC '07 Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops
Modeling and Performance Analysis of LFSN
NPC '07 Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
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Random early detection (RED) is the most famous active queue management (AQM) algorithm and is widely used in current Internet. But classical RED is based on Poisson model which is short-range dependence. And what is more, RED is sensitive to parameters setting. Current Internet traffic has ubiquitous characteristic of self-similarity or long-range dependence (LRD). It is necessary to recheck the RED with LRD traffic input. In this paper, the issue of RED parameters setting with LRD traffic input is studied. LRD characteristic of the network traffic is taken into account in the setting of the RED parameters. By using auto-correlation function as filter weight of RED, the current average queue length is predicted. The packet dropping/marking probability is computed based on the analytical result of Fractional Brownian Motion (FBM) model. The maximum and minimum queue threshold is derived based on the critical time which makes the queue length to achieve its maximum value in bursty Period. Intensive simulations are performed to validate the accuracy and effectiveness of RED parameters setting scheme, and results indicate that the proposed scheme can well control the average queue length and raise the performance of RED algorithm.