Controlling congestion on complex networks: fairness, efficiency and network structure

Buzna, Ľuboš; Carvalho, Rui

Abstract

We consider two elementary (max-flow and uniform-flow) and two realistic (max-min fairness and proportional fairness) congestion control schemes, and analyse how the algorithms and network structure affect throughput, the fairness of flow allocation, and the location of bottleneck edges. The more realistic proportional fairness and max-min fairness algorithms have similar throughput, but path flow allocations are more unequal in scale-free than in random regular networks. Scale-free networks have lower throughput than their random regular counterparts in the uniform-flow algorithm, which is favoured in the complex networks literature. We show, however, that this relation is reversed on all other congestion control algorithms for a region of the parameter space given by the degree exponent γ and average degree 〈k〉. Moreover, the uniform-flow algorithm severely underestimates the network throughput of congested networks, and a rich phenomenology of path flow allocations is only present in the more realistic α-fair family of algorithms. Finally, we show that the number of paths passing through an edge characterises the location of a wide range of bottleneck edges in these algorithms. Such identification of bottlenecks could provide a bridge between the two fields of complex networks and congestion control.

Citation

Buzna, Ľ., & Carvalho, R. (2017). Controlling congestion on complex networks: fairness, efficiency and network structure. Scientific Reports, 7(1), Article 9152. https://doi.org/10.1038/s41598-017-09524-3

• Journal Article Type: Article
• Acceptance Date: Jul 18, 2017
• Online Publication Date: Aug 22, 2017
• Publication Date: Aug 22, 2017
• Deposit Date: Sep 1, 2017
• Publicly Available Date: Sep 1, 2017
• Journal: Scientific Reports
• Publisher: Nature Research
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 1
• Article Number: 9152
• DOI: https://doi.org/10.1038/s41598-017-09524-3