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- Haile, Habtegebreil Kassaye, et al.
(author)
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WIP: Leveraging QUIC for a Receiver-driven BBR for Cellular Networks
- 2021
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In: 2021 IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665422635 ; , s. 252-255
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Conference paper (peer-reviewed)abstract
- Cellular networks are continuously evolving to allow improved throughput and low latency performance for applications. However, it has been shown that, due to buffer over-provisioning, TCP’s standard loss-based congestion control algorithms (CCAs) can cause long delays in cellular networks. The QUIC transport protocol and the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control are both proposed in response to shortcomings observed in TCP and loss-based CCAs. Despite its notable advantages, BBR can experience suboptimal delay performance in cellular networks due to one of its underlying design choices: the maximum bandwidth filter at the sender. In this work, we leverage QUIC’s extensibility to enhance BBR. Instead of using the ACK rate observed at the sender side, we apply a more fitting delivery rate calculated at the receiver. Our 5G-trace-based emulation experiments in CloudLab suggest that our modified QUIC could significantly improve latency without any notable effect on the throughput: In particular, in some of our experiments, we observe up to 39% reduction of the round-trip time (RTT) with a worst case throughput reduction of 2.7%.
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- Sundberg, Simon, 1995-, et al.
(author)
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Passive Monitoring of Network Latency at High Line Rates
- 2022
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Conference paper (peer-reviewed)abstract
- Network latency plays a crucial role for many applications and their perceived quality of experience. With an increasing focus on high network speeds and real time, interactive applications relying on reliable and low latency, the ability to effectively monitor latency is becoming more important than ever. While many available tools rely on active monitoring, this approach relies on traffic injection in the network, which can be a source of latency in itself and have a negative overall network performance impact. This paper presents evolved Passive Ping (ePPing), a tool that leverages eBPF to passively monitor latency of existing network traffic. Preliminary evaluation shows that ePPing delivers RTT reports more reliably and at a lower overhead than other state-of-the-art tools, such as PPing.
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- Sundberg, Simon, 1995-
(author)
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Towards Ubiquitous and Continuous Network Latency Monitoring
- 2024
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Licentiate thesis (other academic/artistic)abstract
- 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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