A distributed end-to-end fair bandwidth allocation algorithm for multi-path networks

Multi-path transmission significantly improves network performance, yet it complicates the problem of fair resource allocation. While traditional fair bandwidth allocation schemes thrive in single-path settings, they often falter when applied to multi-path environments, highlighting the challenge of...

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Veröffentlicht in:Computers & electrical engineering Jg. 120; S. 109635
Hauptverfasser: Wang, Haitao, Song, Lihua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2024
Schlagworte:
Fair bandwidth allocation
Max–min fairness
Multi-path network
Queuing packet
Max–min fairness
Multi-path network
Fair bandwidth allocation
Queuing packet
ISSN:0045-7906
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Zusammenfassung:Multi-path transmission significantly improves network performance, yet it complicates the problem of fair resource allocation. While traditional fair bandwidth allocation schemes thrive in single-path settings, they often falter when applied to multi-path environments, highlighting the challenge of achieving fair bandwidth sharing in such networks. To tackle this issue, the concept of "max-min similarity" of queuing delays has been introduced based on insight into the intrinsic interactions between queuing packets, delays, and bandwidth allocation, which leads to a formal definition of max-min fair bandwidth allocation for multi-path environments. Theoretical analysis shows that the max-min similarity of queuing delays is a sufficient condition for max-min fair bandwidth allocation in single bottleneck environments. A novel distributed end-to-end fair bandwidth allocation algorithm, named DMFBA, is then proposed, which separates the control into flow-level and transmission path-level. In achieving max-min similarity in queuing delays by dynamically adjusting the distribution of flows’ queuing packet quotas across paths it achieves the goal of max-min fair bandwidth allocation. Two sets of numerical simulation experiments were conducted and the results show that DMFBA has less overhead and faster convergence than the traditional utility fair algorithms.
ISSN:0045-7906
DOI:10.1016/j.compeleceng.2024.109635