Category Archives: CCR October 2021

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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Machine learning-based analysis of COVID-19 pandemic impact on US research networks

Mariam Kiran, Scott Campbell, Fatema Bannat Wala, Nick Buraglio, Inder Monga


This study explores how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-at-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.

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REDACT: refraction networking from the data center

Arjun Devraj, Liang Wang, Jennifer Rexford


Refraction networking is a promising censorship circumvention technique in which a participating router along the path to an innocuous destination deflects traffic to a covert site that is otherwise blocked by the censor. However, refraction networking faces major practical challenges due to performance issues and various attacks (e.g., routing-around-the-decoy and fingerprinting). Given that many sites are now hosted in the cloud, data centers offer an advantageous setting to implement refraction networking due to the physical proximity and similarity of hosted sites. We propose REDACT, a novel class of refraction networking solutions where the decoy router is a border router of a multi-tenant data center and the decoy and covert sites are tenants within the same data center. We highlight one specific example REDACT protocol, which leverages TLS session resumption to address the performance and implementation challenges in prior refraction networking protocols. REDACT also offers scope for other designs with different realistic use cases and assumptions.

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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

When latency matters: measurements and lessons learned

Marco Iorio, Fulvio Risso, Claudio Casetti


Several emerging classes of interactive applications are demanding for extremely low-latency to be fully unleashed, with edge computing generally regarded as a key enabler thanks to reduced delays. This paper presents the outcome of a large-scale end-to-end measurement campaign focusing on task-offloading scenarios, showing that moving the computation closer to the end-users, alone, may turn out not to be enough. Indeed, the complexity associated with modern networks, both at the access and in the core, the behavior of the protocols at different levels of the stack, as well as the orchestration platforms used in data-centers hide a set of pitfalls potentially reverting the benefits introduced by low propagation delays. In short, we highlight how ensuring good QoS to latency-sensitive applications is definitely a multi-dimensional problem, requiring to cope with a great deal of customization and cooperation to get the best from the underlying network.

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