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Published August 2021 | public
Journal Article Open

Combating Bufferbloat in Multi-Bottleneck Networks: Theory and Algorithms


Bufferbloat is a phenomenon in computer networks where large router buffers are frequently filled up, resulting in high queueing delay and delay variation. More and more delay-sensitive applications on the Internet have made this phenomenon a pressing issue. Interacting with the Transmission Control Protocol (TCP), active queue management (AQM) algorithms run on routers play an important role in combating bufferbloat. However, AQM algorithms have not been widely deployed due to complicated manual parameter tuning. Moreover, they are often designed and analyzed based on network models with a single bottleneck link, rendering their performance and stability unclear in multi-bottleneck networks. In this paper, we propose a general framework to combat bufferbloat in multi-bottleneck networks. We first present an equilibrium analysis for a general multi-bottleneck TCP/AQM system and provide sufficient conditions for the uniqueness of an equilibrium point in the system. We then decompose the system into single-bottleneck subsystems and derive sufficient conditions for the local asymptotic stability of the subsystems. Using our framework, we develop an algorithm to compute the equilibrium point of the system. We further present a case study to analyze the stability of the recently proposed Controlled Delay (CoDel) in multi-bottleneck networks and devise Self-Tuning CoDel to improve the system stability. Extensive numerical and packet-level simulation results not only verify our theoretical studies but also show that our proposed Self-Tuning CoDel significantly stabilizes queueing delay in multi-bottleneck networks, thereby mitigating bufferbloat.

Additional Information

© 2021 IEEE. Manuscript received April 25, 2020; revised December 23, 2020; accepted February 14, 2021; approved by IEEE/ACM TRANSACTIONS ON Networking Editor C. Joo. Date of publication April 9, 2021; date of current version August 18, 2021. This work was supported in part by the Research Grants Council, Hong Kong, China, under Grant 17204614. A preliminary version of this work was presented in IEEE INFOCOM 2018 [1].

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August 20, 2023
September 8, 2023