Packet rank-aware active queue management for programmable flow scheduling

Li, Ziyong; Hu, Yuxiang; Tian, Le; Zhao, Liang

doi:10.1016/j.comnet.2023.109632

Cited by 11 publications

(1 citation statement)

References 16 publications

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“…Another line of work focuses on reducing latency by avoiding large queuing delays, especially those caused by the phenomenon known as bufferbloat [32], where large unmanaged packet buffers fill up, causing large and unnecessary delays for all traffic. Several works [33][34][35] attempt to rectify this problem by utilizing various Active Queue Management (AQM) algorithms on nodes inside the network to prevent large queues from building up. Other research instead attempts to minimize the queuing latency by developing better congestion control algorithms [36,37] at the end hosts, preventing packets that cause queue buildups from being sent in the first place.…”

Section: Introductionmentioning

confidence: 99%

Towards Ubiquitous and Continuous Network Latency Monitoring

Sundberg

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The Internet plays an important role in modern society, and its network performance impacts billions of users every day. For many network applications, network latency has a large impact on the quality of experience for the end user. Due to a lack of extensive network latency monitoring, the observability of network latency in real networks is often limited. This poses a problem for understanding network latency on the Internet today, and for assessing the impact various solutions that aim to reduce network latency have once they are deployed in the wild. This thesis addresses shortcomings with current solutions for monitoring network latency, in particular the performance of passive monitoring solutions on general-purpose commodity hardware, aiming to enable more ubiquitous latency monitoring and ultimately provide a comprehensive view of real-world network latency. We utilize the recently emerging eBPF technology to implement passive network latency monitoring inside the Linux kernel. Through experiments on a testbed, we show that our solution can monitor packets at over an order of magnitude higher rates than comparable previous solutions, allowing it to successfully monitor the latency for multi-gigabit traffic on general-purpose commodity hardware. Additionally, we demonstrate the feasibility of continuously monitoring network latency by deploying our solution inside an Internet Service Provider and monitoring the network latency for all customer traffic. Through an extensive analysis of the collected latency data, we show large differences in how network latency is distributed across different parts of the network.

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