Effects of real-time and potential traffic congestion on network throughput
TL;DR
The main contribution of this work is to reveal the impacts of real-time and potential congestion on network throughput, clearly establishing their primary-secondary relationship in throughput control, and providing empirical guidelines and parameter-tuning recommendations for hybrid routing designs that combine immediate responsiveness with predictive awareness.
Abstract
To reduce traffic congestion and improve network throughput, this study focuses on the dynamic routing process by constructing a unified path-cost function that integrates both a real-time traffic congestion index (derived from instantaneous node queue lengths) and a potential traffic congestion index (estimated from the expected number of future paths traversing each node based on active routing decisions). Through extensive simulations on Barabási–Albert scale-free network and Erdös–Rényi random network, we systematically investigate how these two types of congestion information affect network throughput. The results reveal that real-time traffic congestion is more significant than potential traffic congestion in improving network throughput. By comparison with five routing algorithms, it is found that the routing strategy considering both real-time and potential traffic congestion are more efficient. The main contribution of this work is to reveal the impacts of real-time and potential congestion on network throughput, clearly establishing their primary-secondary relationship in throughput control, and providing empirical guidelines and parameter-tuning recommendations for hybrid routing designs that combine immediate responsiveness with predictive awareness.