Congestion control in high-speed networks using the probabilistic estimation approach

TL;DR

This paper introduces a probabilistic congestion estimation algorithm for high-speed networks that improves TCP performance by accurately assessing network congestion, leading to better utilization, stability, and fairness.

Contribution

It proposes a novel probabilistic congestion estimation method combining loss and delay metrics, enhancing TCP performance in high-speed networks.

Findings

Improved network utilization and throughput.

Enhanced stability and fairness in congestion control.

Outperforms existing algorithms in simulations.

Abstract

Nowadays, the bulk of Internet traffic uses TCP protocol for reliable transmission. But the standard TCP's performance is very poor in High Speed Networks (HSN) and hence the core gigabytes links are usually underutilization. This problem has roots in conservative nature of TCP, especially in its Additive Increase Multiplicative Decrease (AIMD) phase. In other words, since TCP can't figure out precisely the congestion status of the network, it follows a conservative strategy to keep the network from overwhelming. We believe that precisely congestion estimation in the network can solve this problem by avoiding unnecessary conservation. To this end, this paper proposes an algorithm which considers packet loss and delay information jointly and employs a probabilistic approach to accurately estimation of congestion status in the network. To examine the proposed scheme performance, extensive simulations have been performed in the NS-2 environment. Simulation results reveal that the proposed algorithm has better performance than existing algorithms in terms of bottleneck utilization, stability, throughput and fairness.