This July 2026 issue contains three technical papers. The first paper, The Carrier Pigeon Internet Protocol: An Algorithmic (and Light-hearted) Perspective by Matthias Bentert, Shay Kutten, Darya Melnyk, Tijana Milentijević, and Stefan Schmid is an amusing read that investigates protocols and complexities of communication networks based on carrier pigeons. While lighthearted in nature, the paper is technically grounded in classic mathematical techniques used to analyze modern computer networks. This creative paper is both entertaining and informative—in fact, the paper proposes follow-up work that I'd love to see.
We present a large-scale measurement of emerging Artificial Intelligence (AI)-oriented web permission and descriptor mechanisms across approximately 4M domains. Beyond traditional robots.txt files, we analyze the adoption, structure, and interactions of newer files, including the permission-oriented ai.txt and the descriptor-oriented llms.txt and llms-full.txt. While robots.txt remains the dominant mechanism for signaling crawler permissions, adoption of AI-specific permission files is emerging, especially among technology-focused domains. Directive usage shows a strong emphasis on restricting AI agents, with prevalent disallow rules and relatively few AI-specific permission controls. Crossfile comparisons reveal that conflicts between robots.txt and ai.txt are infrequent, while some llms.txt link targets point to content blocked by access-control files.
An artifact is a resource essential to the complete understanding of a scientific article, extending beyond the manuscript itself. As more conferences allow authors to submit artifacts alongside their articles, different review processes, badge types are emerging, and badge-acquisition requirements. In this paper, we describe the process for conducting artifact evaluation that has been implemented in the Brazilian community. Our discussion focuses on the two largest Brazilian networking and security venues, where we discuss the challenges we encountered during our interactions and the solutions we ultimately adopted to improve the review process.
On April 28th, 2025, a major power outage caused a disruption of electric service across Spain and Portugal, lasting about ten hours in most regions and even longer in some areas. We analyze the impact of the power outage, showing how it led to a near-instantaneous 33–70% drop in traffic at 4 major Iberian IXPs, while core infrastructure remained largely functional. We show that the Internet was impacted by a second event: a partial outage in a data center that resulted in a second drop of traffic and BGP sessions.
The theoretical model behind the pigeon post as a link layer in a communication network was introduced by Shannon (under the guise of studying One-Time Pads for cryptography). That is, to send a one-hop message to v, a node u needs a mail pigeon bred and raised at v. When sending a message using a pigeon to v, node u loses the pigeon. To send another message to v, node u needs another pigeon of v. It has been demonstrated that the communication bandwidth achievable with pigeon post can exceed that of networks using other media. This has already motivated the introduction of Internet standards that allow the use of pigeons as Internet link-layer media. In this paper, we begin to fill in the missing piece: designing algorithms for breeding and scheduling pigeons to meet a given communication demand efficiently, minimizing the number of pigeons required. We consider singlehop, 2-hop, and multihop pigeon use. While the singlehop variant admits a simple characterization, both the 2-hop and the multihop variants are NP-hard. For the latter variants, we present a polynomial-time algorithm based on demand aggregation that achieves a 2-approximation for the number of pigeons used. We believe that this pigeon-based perspective offers both amusing and instructive insights into network design and hopefully, into ornithology.
Multiple studies have shown the benefits of industry-academia collaborations. In this editorial, we discuss the industry-academic research collaboration from the perspective of SIDN, a Netherlandsbased privately held small tech company with a public-interest mission, which operates the .nl top-level domain. We present and compare five models for industry-academia collaboration and show how each has produced deliverables that benefit not only SIDN and its partners involved but also the broader community, including the SIGCOMM's. We also share lessons learned from building and running an in-house research team. Our goal is to encourage other industry players to pursue open, collaborative research that serves academia, industry, and society alike.
This July 2025 issue contains two technical papers and one editorial note. Before introducing the contents of this issue, I want to make an announcement: I will be stepping down as of January 2026. The new CCR editor will be Robert Soulé from Yale University. It has been a pleasure and a privilege to serve the SIGCOMM community as CCR editor since January 2020. However, it is now time to bring new blood and fresh ideas into CCR.
Low-Earth Orbit (LEO) satellite mega-constellations which offer broad coverage and low latencies offer a revolutionary new connectivity option. However, they also have complex orbital dynamics leading to continuous latency changes and frequent satellite hand-offs. We are building a globally spanning testbed, LEOScope 1 , to quantify the performance opportunities and bottlenecks of such networks. This paper discusses the unique features we incorporated into LEOScope to adapt it to the dynamics of LEO networks and to enable experimentation on volunteer nodes. To demonstrate the broad utility of the testbed, we report Starlink performance across countries using LEOScope. Our primary aim is to describe the testbed and announce its availability to the SIGCOMM community for non-commercial research activities.
With IP Multicast, a source can efficiently send the same information to a set of receivers attached to a multicast tree. Unfortunately, when distributing live video or large files, some receivers might be unable to join the multicast tree. Applications willing to use multicast for efficiency must also support unicast to reach all their receivers. Given the complexity of mixing unicast and multicast, most popular applications only use unicast protocols. The large deployment of QUIC, a secure and flexible transport protocol that runs above UDP, allows for reconsidering multicast at the transport layer. We design and implement Flexicast QUIC , an extension of Multipath QUIC that enables applications to use multicast where and when it works efficiently and seamlessly fall back on unicast otherwise. Our in-lab performance evaluation shows that a Flexicast QUIC source can sustain up to 1000 receivers for an aggregated traffic of more than 80 Gbps, more than 4 times what we achieve with (unicast) QUIC in the same setup. We also show that Flexicast QUIC can easily distribute a video stream and recover from transient failures on the underlying multicast tree while maintaining excellent quality of experience.
Internet speed tests are an important tool to enable consumers and regulators to monitor the quality of Internet access. However, increased Internet speeds to the home and an increased demand for speed testing pose scaling challenges to providers of speed tests, who must maintain costly infrastructure to keep up with this demand. In recent years, this has led the popular NDT speed test to limit data transfer to a total of 250MB, which comes at the cost of accuracy for high bandwidth speed test clients. In this paper, we observe that the NDT speed test server's congestion control algorithm (BBRv1) is also trying to estimate the capacity of the connection. We leverage this observation and signals from BBR to improve the accuracy and efficiency of speed tests. We first show how leveraging signals from BBR can more than double the accuracy of a 10MB test—from 17% to 43%—for clients with speeds over 400Mbps. We then show how using BBR signals to adaptively end the speed test reduces data transfer by 36% and increased accuracy by 13% for high bandwidth clients, relative to a 100MB fixed length test. Even accounting for clients that never observe enough samples to utilize the BBR signal, this adaptive approach still uses 25% less data than a fixed 100MB test with 37-44% higher accuracy.
This January 2025 issue contains one technical paper and two editorial notes. The technical paper, An Analysis of QUIC Connection Migration in the Wild, by Aurélien Buchet and Cristel Pelsser, provides a comprehensive examination of the support of the QUIC connection migration mechanism over the Internet. The authors perform Internet-wide scans revealing that despite a rapid evolution in the deployment of QUIC on web servers, some of the most popular destinations do not support connection migration yet.
The purpose of this editorial note is to raise awareness about a deeply concerning and yet much-overlooked development in the use of Artificial Intelligence (AI) and Machine Learning (ML) for solving problems in science in general and in networking in particular. To put it simply, in today's age of AI/ML, the much-publicized and well-documented "reproducibility crisis" in science is further compounded by an inconspicuous and rarely mentioned "credibility crisis." More to the point, by focusing on the area of networking research, we provide evidence that among the already small number of reproducible scientific publications that describe AI/ML-based solutions, even fewer, and often none, describe trained AI/ML models that are "credible;" that is, can be trusted to not only perform well in their original training domain but also in new and untested environments. We elaborate on the root cause of this credibility crisis, discuss why the credibility of AI/ML models is of paramount importance for their successful use in practice, and put forward an aggressive but imminently practical proposal for addressing this crisis head-on so as to pave the way for a future where networking research can reap the full benefits of AI/ML.
With the advent of softwarization of digital telephone switches, many dynamic call routing schemes were explored in the 1980s to provide better network performance. In particular, we highlight two learning algorithms for dynamic call routing from that era. We note that while the learning algorithms have the adaptive capability to benefit dynamic call routing performance, they alone cannot address network instabilities in certain network load conditions. Additional controls are necessary. This note is to present an overview of various dynamic call routing schemes, and in particular, learning algorithms for dynamic call routing from yesteryears. We also discuss control mechanisms that were deployed for network stability. Finally, we present lessons learned from this work, which could hopefully be useful in applying artificial intelligence or machine learning (AI/ML) to networking in today's world.
The paper presents a well-executed measurement study on QUIC migration when HTTP3 (H3) is used, offering an important early snapshot of QUIC migration deployment. The study is thorough, and the paper is well-written, with a clear outline of the study's limitations. It effectively separates the measurement results from discussions and assumptions that go beyond what the data can support. Strengths include a clear and structured study process, an insightful presentation of results in Table 1, and the open-sourcing of both tools and data. The paper makes significant progress by addressing earlier concerns, such as clarifying its focus on HTTP3+QUIC, reducing the emphasis on connections without SNI, and ensuring that speculation is clearly separated from concrete results. While there are still some challenges, such as the study of certain QUIC parameters and the simplified handshake diagram in Figure 1, the paper provides a strong foundation for future research. It offers valuable insights into the current state of QUIC migration deployment and sets the stage for further work in this area.
Mobile networks are embracing disaggregation, reflected by the industry trend towards Open RAN. Private 5G networks are viewed as particularly suitable contenders as early adopters of Open RAN, owing to their setting, high degree of control, and opportunity for innovation they present. Motivated by this, we have recently deployed Campus5G, the first of its kind campus-wide, O-RAN-compliant private 5G testbed across the central campus of the University of Edinburgh. We present in detail our process developing the testbed, from planning, to architecting, to deployment, and measuring the testbed performance. We then discuss the lessons learned from building the testbed, and highlight some research opportunities that emerged from our deployment experience.
This October 2024 issue contains three technical papers, one of which is of a rather educational nature and can be considered both an educational contribution and a technical paper.
Packet filtering has remained a key network monitoring primitive over decades, even as networking has continuously evolved. In this article we present the results of a survey we ran to collect data from the networking community, including researchers and practitioners, about how packet filtering is used. In doing so, we identify pain points related to packet filtering, and unmet needs of survey participants. Based on analysis of this survey data, we propose future research and development goals that would support the networking community.
This July 2024 issue contains one technical paper, one educational paper, and one editorial note.
