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