Girma M. Yilma, Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez Abstract Large Language Models (LLMs) have immense potential to transform the telecommunications industry. They could help professionals understand complex standards, generate code, and accelerate development. However, traditional LLMs struggle with the precision and source verification essential for telecom work. To address this, specialized LLM-based solutions […]| Computer Communication Review
Fan Gabriella Xue, Matthew Caesar Abstract An increasing number of students are becoming interested in learning about the Internet of Things (IoT) space. However, today, we lack scalable and efficient ways to bring hands-on IoT learning to many due to hardware accessibility, system complexity, and deployment environment constraints. This paper presents ThingVisor, an IoT learning […]| Computer Communication Review
Nik Sultana, Hyunsuk Bang, Elena Yulaeva, Ricky K. P. Mok, Kc Claffy, Richard Mortier Abstract 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, […]| Computer Communication Review
This July 2024 issue contains one technical paper, one educational paper, and one editorial note. The technical paper, A Survey on Packet Filtering, by Nick Sultana and colleagues, was originally submitted as an editorial. Given that CCR does not usually consider survey papers, it went through a thorough reviewing process. Given its value to the […]| Computer Communication Review
Fabián E. Bustamante, John Doyle, Walter Willinger, Marwan Fayed, David L. Alderson, Steven Low, Stefan Savage, Henning Schulzrinne Abstract On November 28–29, 2023, Northwestern University hosted a workshop titled “Towards Re-architecting Today’s Internet for Survivability” in Evanston, Illinois, US. The goal of the workshop was to bring together a group of national and international experts […]| Computer Communication Review
Alexander Dietmüller, Romain Jacob, Laurent Vanbever Abstract Machine learning (ML) is a powerful tool to model the complexity of communication networks. As networks evolve, we cannot only train once and deploy. Retraining models, known as continual learning, is necessary. Yet, to date, there is no established methodology to answer the key questions: With which samples […]| Computer Communication Review
This April 2024 issue contains two technical papers and one editorial note. The first technical paper, This Is a Local Domain: On Amassing Country-Code Top-Level Domains from Public Data, by Raffaele Sommese and colleagues, presents a measurement study that investigates ccTLD coverage using public data sources. Domain lists such as Alexa and Tranco are crucial […]| Computer Communication Review
Raffaele Sommese, Roland van Rijswijk-Deij, Mattijs Jonker Abstract Domain lists are a key ingredient for representative censuses of the Web. Unfortunately, such censuses typically lack a view on domains under country-code top-level domains (ccTLDs). This introduces unwanted bias: many countries have a rich local Web that remains hidden if their ccTLDs are not considered. The […]| Computer Communication Review
Kenichi Yasukata Abstract This paper presents iip, an integratable TCP/IP stack, which aims to become a handy option for developers and researchers who wish to have a high-performance TCP/IP stack implementation for their projects. The problem that motivated us to newly develop iip is that existing performance-optimized TCP/IP stacks often incur tremendous integration complexity and […]| Computer Communication Review
Changgang Zheng, Mingyuan Zang, Xinpeng Hong, Liam Perreault, Riyad Bensoussane, Shay Vargaftik, Yaniv Ben-Itzhak, Noa Zilberman Abstract In-network machine learning inference provides high throughput and low latency. It is ideally located within the network, power efficient, and improves applications' performance. Despite its advantages, the bar to in-network machine learning research is high, requiring significant expertise…| Computer Communication Review