Analysis of the increase and decrease algorithms for congestion avoidance in computer networks
Computer Networks and ISDN Systems
Introduction to algorithms
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Experimental queueing analysis with long-range dependent packet traffic
IEEE/ACM Transactions on Networking (TON)
End-to-end Internet packet dynamics
SIGCOMM '97 Proceedings of the ACM SIGCOMM '97 conference on Applications, technologies, architectures, and protocols for computer communication
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Self-similarity and heavy tails: structural modeling of network traffic
A practical guide to heavy tails
Using adaptive linear prediction to support real-time VBR video under RCBR network service model
IEEE/ACM Transactions on Networking (TON)
On the propagation of long-range dependence in the Internet
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Measuring link bandwidths using a deterministic model of packet delay
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Sting: a TCP-based network measurement tool
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
Lévy flights and fractal modeling of internet traffic
IEEE/ACM Transactions on Networking (TON)
On fast generation of fractional Gaussian noise
Computational Statistics & Data Analysis
Analysis of prediction performance of training-based models using real network traffic
International Journal of Computer Applications in Technology
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Analytical and empirical studies have shown that self-similar traffic can have detrimental impact on network performance including amplified queuing delay and packet loss ratio. On the flip side, the ubiquity of scale-invariant burstiness observed across diverse networking contexts can be exploited to better design resource control algorithms. In this paper, we explore the issue of exploiting the self-similar characteristics of network traffic in TCP congestion control. We show that the correlation structure present in long-range dependent traffic can be detected on-line and used to predict the future traffic. We then devise a novel scheme, called TCP with traffic prediction (TCP-TP), thal exploits the prediction result to infer, in the context of AIMD steady-state dynamics, the optimal operational point at which a TCP connection should operate. Through analytical reasoning, we show that the impact of prediction errors on fairness is minimal. We also conduct ns-2 simulation and FreeBSD 4.1-based implementation studies to validate the design and to demonstrate the performance improvement in terms of packet loss ratio and throughput attained by connections.