The October 2021 issue

This October 2021 issue contains two technical papers, two educational contributions, and one editorial note.

The first technical paper, When Latency Matters: Measurements and Lessons Learned, by Marco Iorio and colleagues, evaluates the “latency argument” for edge computing, i.e., that placing elastic computing and storage platforms in close proximity to end-users makes sense for latency-critical applications. The paper evaluates several sources of latency, including latency induced by core network routing inefficiencies, wired and wireless access network, transport protocol and application protocol. The paper concludes that moving data-centers close to the users is only a small part of the latency problems, and that solving it requires a more careful coordination of efforts across the network stack.

The second technical paper, REDACT: Refraction Networking from the Data Center, by Arjun Devraj and colleagues, extends the concept of refraction networking by assigning the edge router of a cloud datacenter the role of a decoy router.

The first educational contribution, Machine learning-based Analysis of COVID-19 Pandemic Impact on US Research Networks, by Mariam Kiran and colleagues, sheds light on the performance of a large network throughout the COVID-19 pandemic. Extensive traces are studied and analyzed, with a number of interesting findings using various statistical techniques.

The second educational contribution, An educational toolkit for teaching cloud computing, by Cosimo Anglano and colleagues, proposes the creation of a software layer to hide the specifics of the underlying cloud platforms from students, enabling them to perform their assignments atop a general API. The proposed approach is an innovative idea to improve the educational experience of students on cloud platforms.

Finally, we have an editorial note. Data-driven Networking Research: models for academic collaboration with industry (a Google point of view), by Jeffrey C. Mogul and his colleagues, describes collaboration models aimed at stimulating data-driven networking research. The authors describe specific areas where they would welcome proposals to work within those models.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online ( or by email at ccr-editor at

The July 2021 issue

This July 2021 issue contains one technical paper, two educational contributions, as well as four editorial notes.

The technical paper, NemFi: Record-and-replay to emulate WiFi, by Abhishek kumar Mishra and colleagues, proposes a trace-driven WiFi emulator called NemFi, that allows modeling of transmission opportunities of uplink and downlink directions, packet loss, frame aggregation, and media access control behavior. The latter two concepts are unique for WiFi when compared with other similar tools that have been built for cellular networks.

The first educational contribution, The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks, by Jose Suarez-Varela and colleagues, proposes a ”challenge” to teach students about applications of AI/MLin computer networks. The authors describe a process to select a dataset, and a competition-based approach where participants must design a neural-network-based approach to infer properties of the dataset with as much accuracy as possible.

The second educational contribution, P4Pi: P4 on Raspberry Pi for Networking Education, by Sandor Laki and colleagues, presents a novel platform for networking education based on a Raspberry Pi, allowing students to program P4.

Then, we have four editorial notes. The first one, Limited Domains Considered Useful, by Brian Carpenter and his colleagues, argues not only that limited domains have been with us from the very beginning of the Internet but also that they have been shaping innovation of Internet technologies ever since, and will continue to do so.

The second editorial note, Collaboration in the IETF: An Initial Analysis of Two Decades in Email Discussions, by Michael Welzl and his colleagues, discusses the following question: when big players follow such a “shoot first, discuss later” approach, is IETF collaboration still “real”, or is the IETF now being (mis-)used to approve protocols for standardization when they are already practically established, without really actively involving anyone but the main proponents?

The third editorial note, Workshop on Overcoming Measurement Barriers to Internet Research (WOMBIR 2021) Final Report, by kc claffy and her colleagues, reports on the Workshop on Overcoming Measurement Barriers to Internet Research (WOMBIR), held earlier in 2021.

The fourth and last editorial note, A Square Law Revisited, by Brian Carpenter, revisits the approximate apparent growth of the globally addressable Internet in proportion to the square root of the host count.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online ( or by email at ccr-editor at

The April 2021 issue

The April 2021 issue contains one technical paper as well as five editorial notes.

The technical paper, Surviving switch failures in cloud datacenters, by Rachee Singh and her colleagues, examines the nature of switch failures in the datacenters of a large commercial cloud provider. This work studies a cohort of over 180,000 switches with a variety of hardware and software configurations.

Then, we have five editorial notes. The first one, The Netivus Manifesto: Making Collaborative Network Management Easier for the Rest of Us, by Joseph Severini and his colleagues, studies operational issues faced by Small and Medium Enterprise (SME) network owners.

The second editorial note, Revitalizing the Public Internet By Making it Extensible, by Hari Balakrishnan and his colleagues, argues for the creation of an Extensible Internet that supports in-network services that go beyond best-effort packet delivery.

The third editorial note, Workshop on Internet Economics (WIE 2020) Final Report, by kc claffy and David Clark, reports on the 11th interdisciplinary Workshop on Internet Economics (WIE).

The fourth editorial note, SatNetLab: A call to arms for the next global Internet testbed, by Ankit Singla, lays out a case for networking researchers to collaboratively undertake the construction of SatNetLab, a research platform that enables experimentation across upcoming satellite-based networks.

The fifth editorial note, Great Educators in Computer Networking: Bruce Davie, by Matthew Caesar and Bruce Davie, is an interview, part of a series on Great Educators in Computer Networking, where some of the most impactful and skilled educators in our field are interviewed.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online ( or by email at ccr-editor at

The January 2021 issue

This January 2021 issue contains three technical papers as well as two editorial notes.

The first technical paper, Distrinet: a Mininet Implementation for the Cloud, by Giuseppe Di Lena and his colleagues, proposes Distrinet, a distributed implementation of Mininet over multiple hosts, based on LXD/LXC, Ansible, and VXLAN tunnels. Distrinet is compatible with Mininet programs, generic and can deploy experiments on Linux clusters as well as on the Amazon EC2 cloud platform. Given how popular Mininet is for SDN evaluation, this contribution potentially provides a lot of value to our research community.

The second technical paper, Experience-Driven Research on Programmable Networks, by Hyojoon Kim and colleagues, presents a proof-of-concept to help researchers run experiments against their programmable network idea, in their own network. The authors present several data-plane applications as use cases that run on their campus and solve production network problems. While not fully reproducible, this paper is a good step towards encouraging similar efforts in our community.

Our third paper, The Case for Model-Driven Interpretability of Delay-based Congestion Control Protocols, by Muhammad Khan and his colleagues, presents a study of different delay-based congestion control algorithms for TCP. The proposed framework is flexible and allows to model delay-based protocols, by simplifying a congestion control protocol’s response into a guided random walk over a two-dimensional Markov model. The model is evaluated against actual traces collected in 3G/4G networks, and allows to get the intuition of which regime the congestion control loop is spending most of the time.

Then, we have two editorial notes. The first one, Italian Operators’ Response to the COVID-19 Pandemic, by Massimo Candela and Antonio Prado, reports on the actions undertaken by network operators in Italy in response to COVID-19. The second editorial note, What do Information Centric Networks, Trusted Execution Environments, and Digital Watermarking have to do with Privacy, the Data Economy, and their future?, by Nikolaos Laoutaris and Costas Iordanou, discusses how ICNs combined with trusted execution environments and digital watermarking can be combined to build a personal data overlay inter-network that has a plethora of desirable properties for end-users.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online ( or by email at ccr-editor at

The October 2020 issue

This October 2020 issue contains five technical papers, the third paper of our education series, as well as three editorial notes.

The first technical paper, Partitioning the Internet using Anycast Catchments, by Kyle Schomp and Rami Al-Dalky, deals with anycast, one of the core operational strategies to improve service performance, availability and resilience. Anycast is widely used by cloud providers, content delivery networks (CDNs), major DNS operators and many more popular Internet services. However, anycast comes with limited visibility in how traffic will be distributed among the different server locations. The authors of this paper paper propose a technique for partitioning the Internet using passive measurements of existing anycast deployments, such that all IP addresses within a partition are routed to the same location for an arbitrary anycast deployment.

The second technical paper, LoRadar: LoRa Sensor Network Monitoring through Passive Packet Sniffing, by Kwon Nung Choi and colleagues, moves us to a very different topic, in the area of IoT, and in particular Low Power WAN technologies (LPWANs) such as Long Range (LoRa). This paper develops a software tool, LoRadar, to monitor LoRa’s medium access control protocol on commodity hardware via passive packet sniffing.

Our third paper, A first look at the IP eXchange Ecosystem, by Andra Lutu and her colleagues, deals with the very important topic of the IPX Network, which we use every time we roam with our smartphones and interconnects about 800 Mobile Network Operators (MNOs) worldwide. Despite its size, neither its organisation nor its operation are well known within our community. This paper provides a first analysis of the IPX network, which we hope will be followed by other works on this under-studied topic.

The fourth paper, Mobile Web Browsing Under Memory Pressure, by Ihsan Ayyub Qazi and colleagues, investigates the impact of memory usage on mobile devices in the context of web browsing. The authors present a study using landing page loading time and memory requirements for a number of Android-based smartphones using Chrome, Firefox, Microsoft Edge and Brave. The extensive results of this paper cover the effect of tabs, scrolling, the number of images, and the number of requests made for different objects.

The fifth paper, Retrofitting Post-Quantum Cryptography in Internet Protocols: A Case Study of DNSSEC, by Moritz Mueller and his colleagues, analyses the implications of different Post-Quantum Cryptography solutions in the context of Domain Name System Security Extensions. What makes this paper very interesting, is its timeliness, since the networking and security communities are currently investigating suitable alternatives for DNSSEC, and candidate solutions shall be selected by early 2022.

The sixth paper, also our third paper in the new education series, COSMOS Educational Toolkit: Using Experimental Wireless Networking to Enhance Middle/High School STEM Education, by Panagiotis Skrimponis and his colleagues, describes COSMOS, a general-purpose educational toolkit for teaching students about a variety of computer science concepts, including computer networking. The notable aspect of this work is that the COSMOS testbed has already been deployed and used by a large number of students, and has already demonstrated great value to the community.

Then, we have three editorial notes. The first two are coincidentally on the very timely topic of contact tracing. The first one, Coronavirus Contact Tracing: Evaluating The Potential Of Using Bluetooth Received Signal Strength For Proximity Detection, by Douglas J. Leith and Stephen Farrell, reports on the challenges faced when deploying Covid-19 contact tracing apps that use Bluetooth Low Energy (LE) to detect proximity. The second editorial note, Digital Contact Tracing: Technologies, Shortcomings, and the Path Forward, by Amee Trivedi and Deepak Vasisht, investigates the technology landscape of contact-tracing apps and reports on what they believe are the missing pieces. Our third and final editorial note, Using Deep Programmability to Put Network Owners in Control, by Nate Foster and colleagues, share their vision regarding deep programmability across the stack.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online ( or by email at ccr-editor at

The July 2020 issue

This July 2020 issue contains four technical papers, the second paper of our education series, as well as two editorial notes.

The first technical paper, Tracking the deployment of TLS 1.3 on the Web: A story of experimentation and centralization, by Ralph Holz and his colleagues, deals with Transport Layer Security (TLS) 1.3, a redesign of the Web’s most important security protocol. TLS 1.3 was standardized in August 2018 after a four year-long, unprecedented design process involving many cryptographers and industry stakeholders. In their work, the authors track deployment, uptake, and use of TLS 1.3 from the early design phase until well over a year after standardization.

The second technical paper, Does Domain Name Encryption Increase Users’ Privacy?, by Martino Trevisan and colleagues, is on a topic related to the first technical paper. This work shows that DNS over HTTP (DoH) does not offer the privacy protection that many assume. For the purposes of reproducibility, the authors provide the data used under NDA with the institution owning the data. The authors also share config files and ML environment details in the interest of promoting replicability in other environments.

Our third paper, Using Application Layer Banner Data to Automatically Identify IoT Devices, by Talha Javed and his colleagues, is of the “repeatable technical papers” type, which are technical contributions that provide their artefacts, e.g., software, datasets. This paper attempts to replicate a Usenix Security 2018 paper. It describes the efforts of the authors at re-implementing the solution described in the Usenix Security paper, especially the challenges encountered when authors of the original paper are unwilling to respond to requests for artefacts. We hope it will encourage additional reproducibility studies.

The fourth paper, Towards Declarative Self-Adapting Buffer Management, by Pavel Chuprikov and his colleagues, introduces a novel machine learning based approach to buffer management. The idea is to provide a queue management infrastructure that automatically adapts to traffic changes and identifies the policy that is hypothetically best suited for current traffic patterns. The authors adopt a multi-armed bandits model, and given that different objectives and assumptions lead to different bandit algorithms, they discuss and explore the design space while providing an experimental evaluation that validates their recommendations. The authors provide a GitHub repository that allows for the reproducibility of their result through the NS-2 simulator.

The fifth paper, also our second paper in the new education series, Open Educational Resources for Computer Networking, by Olivier Bonaventure and his colleagues, describes an effort to create an online, interactive textbook for computer networking. What distinguishes this textbook from traditional ones is that it not only is it free and available for anyone in the world to use, but also, it is also interactive. Therefore, this goes way beyond what a textbook usually offers: it is an interactive learning platform for computer networking. The authors here report on about ten years of experience with it, that led to some interesting experiences and lessons learned.

Then, we have two editorial notes. The first, Lessons Learned Organizing the PAM 2020 Virtual Conference, by Chris Misa and his colleagues, reports on the experience from the organizing committee of the 2020 edition of the Passive and Active Measurement (PAM) conference, that took place as a virtual event. It provides important lessons learned for future conferences that decide to go for a virtual event. The second editorial note, Update on ACM SIGCOMM CCR reviewing process: making the review process more open, by the whole CCR editorial board, aims to inform the SIGCOMM community on the reviewing process in place currently at CCR, and to share our plans to make CCR a more open and welcoming venue, adding more value to the SIGCOMM community.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online (https: // or by email at ccr-editor at

The April 2020 Issue

SIGCOMM Computer Communication Review (CCR) is produced by a group of members of our community that spend time to prepare the newsletter that you read every quarter. Olivier Bonaventure served as editor during the last four years and his term is now over. It is my pleasure to now serve the community as the editor of CCR. As Olivier and other editors in the past did, we’ll probably adjust the newsletter to the evolving needs of the community. A first change is the introduction of a new Education series led by Matthew Caesar, our new SIGCOMM Education Director. This series will be part of every issue of CCR, and will contain different types of contributions, not only technical papers as in the current issue, but also position papers (that promote discussion through a defensible opinion on a topic), studies (describing research questions, methods, and results), experience reports (that describe an approach with a reflection on why it did/did not work), and approach reports (that describe a technical approach with enough detail for adoption by others).

This April 2020 issue contains five technical papers, the first paper of our new education series, as well as three editorial notes.

The first technical paper, RIPE IPmap Active Geolocation: Mechanism and Performance Evaluation, by Ben Du and his colleagues, introduces the research community to the IPmap single-radius engine and evaluates its effectiveness against commercial geolocation databases.

It is often believed that traffic engineering changes are rather infrequent. In the second paper, Path Persistence in the Cloud: A Study of the Effects of Inter-Region Traffic Engineering in a Large Cloud Provider’s Network, Waleed Reda and his colleagues reveal the high frequency of traffic engineering activity within a large cloud provider’s network.

In the third paper, The Web is Still Small After More Than a Decade, Nguyen Phong Hoang and his colleagues revisit some of the decade-old studies on web presence and co-location.

The fourth paper, a repeatable paper originated in the IMC reproducibility track, An Artifact Evaluation of NDP, by Noa Zilberman, provides an analysis of NDP (New Data centre protocol). NDP was first presented at ACM SIGCOMM 2017 (best paper award) and proposes a novel data centre transport architecture. In this paper, the author builds the analysis of the artefact proposed by the original authors of NDP, showing how it is possible to carry out research and build new results on previous work done by other fellow researchers.

The Low Latency, Low Loss, Scalable throughput (L4S) architecture addresses this problem by combining scalable congestion control such as DCTCP and TCP Prague with early congestion signalling from the network. In our fifth technical paper, Validating the Sharing Behavior and Latency Characteristics of the L4S Architecture, Dejene Boru Oljira and his colleagues validate some of the experimental result(s) reported in the previous works that demonstrate the co-existence of scalable and classic congestion controls and its low-latency service.

The sixth paper, also our very first paper in the new education series, An Open Platform to Teach How the Internet Practically Works, by Thomas Holterbach and his colleagues, describes a software infrastructure that can be used to teach about how the Internet works. The platform presented by the authors aims to be a much smaller, yet a representative copy of the Internet. The paper’s description and evaluation are focused on technical aspects of the design, but as a teaching tool, it may be more helpful to describe more about pedagogical issues.

Then, we have three very different editorial notes. The first, Workshop on Internet Economics (WIE 2019) report, by kc Klaffy and David Clark, reports on the 2019 interdisciplinary Workshop on Internet Economics (WIE). The second, strongly related to the fourth technical paper, deals with reproducibility. In Thoughts about Artifact Badging, Noa Zilberman and Andrew Moore illustrate that the current badging scheme may not identify limitations of architecture, implementation, or evaluation. Our last editorial note is a comment on a past editorial, “Datacenter Congestion Control: Identifying what is essential and making it practical” by Aisha Mushtaq, et al., from our July 2019 issue. This comment, authored by James Roberts, disputes that shortest remaining processing time (SRPT the crucial factor in achieving good flow completion time (FCT) performance in datacenter networks.

Steve Uhlig — CCR Editor

Answering three questions about networking research

Jennifer Rexford, Scott Shenker


Researchers often talk about specific technical trends or research topics. But we rarely talk about how and why we do the research that we do. The process of submitting and reviewing papers puts our ideas through a particular kind of filter that may make all of the research seem like it follows some standard rubric, a SIGCOMM Normal Form if you will. During a panel at HotNets’21, five researchers—Hari Balakrishnan, Jon Crowcroft, Jennifer Rexford, Scott Shenker, and David Tennenhouse—each answered three questions about how they pick their own research topics, what areas they would like to see more research on, and how they evaluate conference papers. Due to the unexpectedly positive response to that panel, CCR will be publishing a series of answers to these three questions, starting with two participants from the panel but reaching out to others to provide answers from a broader cross-section of the SIGCOMM community.

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Important concepts in data communications

Craig Partridge


The data communications field recently marked the 50th anniversary of the start of the ARPANET, which was one of the first and certainly the most influential of the early data communications networks. The anniversary provoked discussions about which concepts or ideas in data communications have proven to be enduring in the evolution of data communications. This paper presents one perspective.

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M-Lab: user initiated internet data for the research community

Phillipa Gill, Christophe Diot, Lai Yi Ohlsen, Matt Mathis, Stephen Soltesz


Measurement Lab (M-Lab) is an open, distributed server platform on which researchers have deployed measurement tools. Its mission is to measure the Internet, save the data and make it universally accessible and useful. This paper serves as an update on the MLab platform 10+ years after its initial introduction to the research community [5]. Here, we detail the current state of the M-Lab distributed platform, highlight existing measurements/data available on the platform, and describe opportunities for further engagement between the networking research community and the platform.

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Roadmap for edge AI: a Dagstuhl perspective

Aaron Yi Ding, Ella Peltonen, Tobias Meuser, Atakan Aral, Christian Becker, Schahram Dustdar, Thomas Hiessl, Dieter Kranzlmüller, Madhusanka Liyanage, Setareh Maghsudi, Nitinder Mohan, Jörg Ott, Jan S. Rellermeyer, Stefan Schulte, Henning Schulzrinne, Gürkan Solmaz, Sasu Tarkoma, Blesson Varghese, Lars Wolf


Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimisation, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.

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Towards client-side active measurements without application control

Palak Goenka, Kyriakos Zarifis, Arpit Gupta, Matt Calder


Monitoring performance and availability are critical to operating successful content distribution networks. Internet measurements provide the data needed for traffic engineering, alerting, and network diagnostics. While there are significant benefits to performing end-user active measurements, these capabilities are limited to a small number of content providers with application control. In this work, we present a solution to the long-standing problem of issuing active measurements from clients without requiring application control, e.g., injecting JavaScript to the content served. Our approach uses server-side programmable features of the Network Error Logging specification that allow a CDN to induce a browser connection to an HTTPS server of the CDN’s choosing without application control.

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Towards retina-quality VR video streaming: 15ms could save you 80% of your bandwidth

Luke Hsiao, Brooke Krajancich, Philip Levis, Gordon Wetzstein, Keith Winstein


Virtual reality systems today cannot yet stream immersive, retina-quality virtual reality video over a network. One of the greatest challenges to this goal is the sheer data rates required to transmit retina-quality video frames at high resolutions and frame rates. Recent work has leveraged the decay of visual acuity in human perception in novel gaze-contingent video compression techniques. In this paper, we show that reducing the motion-to-photon latency of a system itself is a key method for improving the compression ratio of gaze-contingent compression. Our key finding is that a client and streaming server system with sub-15ms latency can achieve 5x better compression than traditional techniques while also using simpler software algorithms than previous work.

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The January 2022 issue

This January 2022 issue contains three technical papers and four editorial notes.

The first technical paper, Zeph & Iris Map the Internet – A resilient reinforcement learning approach to distributed IP route tracing, by Matthieu Gouel and colleagues, proposes to improve topology discovery by optimizing the use of existing probing resources. This can be done by intelligently allocating probing directives to vantage points. The system is based on the inter-working of two components: Iris, which takes care of the route tracing, and Zeph, which coordinates Iris’s measurements. The results in the paper show that Zeph, in combination with Iris, are able to facilitate fast topology measurements from geographically distributed vantage points.

The second technical paper, Towards Retina-Quality VR Video Streaming: 15ms Could Save You 80\% of Your Bandwidth, by Luke Hsiao and colleagues, investigates how to provide retina-quality video streaming in virtual reality (VR). The paper studies the impact of the motion-to-photon latency — the time between a change in the viewer’s gaze and the resulting change in the display’s pixels — on a VR system. This metric is paramount for VR systems since it impacts video compression. The paper shows, experimentally, that a client and streaming server system with sub-15 ms end-to-end motion-to-photon latency benefit from 5x better video compression than in presence of larger latencies. The paper also shows how to build such a low latency system both hardware and software-wise.

The third technical paper, Towards client-side active measurements without application control, by Palak Goenka and colleagues, proposes to harness Network Error Logging (NEL) to enable active client-side measurements (RTT and connection availability) by dynamically modifying the HTTPS endpoint where NEL reports should be uploaded. Network Error Logging (NEL) is a W3C standard which defines how web servers can receive from a browser reports about performance and failures of web requests. The techniques used in the paper enable active client-side measurements in the browser without requiring Javascript code injection, which is the current and more invasive state of the art solution.

Finally, we have four editorial notes. Roadmap for Edge AI: A Dagstuhl Perspective, by Aaron Yi Ding and his colleagues, based on the collective input of Dagstuhl Seminar (21342), presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. Then, M-Lab: User initiated Internet data for the research community, by Phillipa Gill and her colleagues, presents Measurement Lab (M-Lab), an open, distributed server platform on which researchers have deployed measurement tools. Important Concepts in Data Communications, by Craig Partridge, presents one perspective about which concepts or ideas in data communications have proven to be enduring in the evolution of data communications. Finally, Answering Three Questions About Networking Research, by Jennifer Rexford and Scott Shenker, presents the first of a series of answers to three questions that were asked to panelists during HotNets’21, about how they pick their own research topics, what areas they would like to see more research on, and how they evaluate conference papers.

I hope that you will enjoy reading this new issue and welcome comments and suggestions on CCR Online ( or by email at ccr-editor at

Zeph & Iris map the internet: A resilient reinforcement learning approach to distributed IP route tracing

Matthieu Gouel, Kevin Vermeulen, Maxime Mouchet, Justin P. Rohrer, Olivier Fourmaux, Timur Friedman


We describe a new system for distributed tracing at the IP level of the routes that packets take through the IPv4 internet. Our Zeph algorithm coordinates route tracing efforts across agents at multiple vantage points, assigning to each agent a number of /24 destination prefixes in proportion to its probing budget and chosen according to a reinforcement learning heuristic that aims to maximize the number of multipath links discovered. Zeph runs on top of Iris, our fault-tolerant system for orchestrating internet measurements across distributed agents of heterogeneous probing capacities. Iris is built around third party free open source software and modern containerization technology, thereby presenting a new model for assembling a resilient and maintainable internet measurement architecture. We show that carefully choosing the destinations to probe from which vantage point matters to optimize topology discovery and that a system can learn which assignment will maximize the overall discovery based on previous measurements. After 10 cycles of probing, Zeph is capable of discovering 2.4M nodes and 10M links in a cycle of 6 hours, when deployed on 5 Iris agents. This is at least 2 times more nodes and 5 times more links than other production systems for the same number of prefixes probed.

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Data-driven networking research: models for academic collaboration with industry (a Google point of view)

Jeffrey C. Mogul, Priya Mahadevan, Christophe Diot, John Wilkes, Phillipa Gill, Amin Vahdat


We in Google’s various networking teams would like to increase our collaborations with academic researchers related to data-driven networking research. There are some significant constraints on our ability to directly share data, which are not always widely-understood in the academic community; this document provides a brief summary. We describe some models which can work – primarily, interns and visiting scientists working temporarily as employees, which simplifies the handling of some confidentiality and privacy issues. We describe some specific areas where we would welcome proposals to work within those models.

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An educational toolkit for teaching cloud computing

Cosimo Anglano, Massimo Canonico, Marco Guazzone


In an educational context, experimenting with a real cloud computing platform is very important to let students understand the core concepts, methodologies and technologies of cloud computing. However, API heterogeneity of cloud providers complicates the experimentation by forcing students to focus on the use of different APIs, and by hindering the jointly use of different platforms. In this paper, we present EasyCloud, a toolkit enabling the easy and effective use of different cloud platforms. In particular, we describe its features, architecture, scalability, and use in our cloud computing courses, as well as the pedagogical insights we learnt over the years.

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