Tag Archives: educational

An educational toolkit for teaching cloud computing

Cosimo Anglano, Massimo Canonico, Marco Guazzone

Abstract

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

Abstract

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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P4Pi: P4 on Raspberry Pi for networking education

Sándor Laki, Radostin Stoyanov, Dávid Kis, Robert Soulé, Péter Vörös, Noa Zilberman

Abstract

High level, network programming languages, like P4, enable students to gain hands-on experience in the structure of a switch or router. Students can implement the packet processing pipeline themselves, without prior knowledge of circuit design. However, when choosing a P4 programmable target for use in the classroom, instructors face a lack of options. On the one hand, software solutions, such as the behavioral model (BMv2) switch, are overly simplified and offer low performance. On the other hand, existing hardware solutions are closed source and expensive.

In this paper, we present P4Pi, a new, low-cost, open-source hardware platform intended for networking education. P4Pi allows students to design and deploy P4-based network devices using the Raspberry Pi board, which has a price tag of less than many academic textbooks. We describe the high-level design of the P4Pi platform, offer some suggestions for how P4Pi could be used in the classroom, and present some additional use-cases for applications and functionality that could be developed using P4Pi.

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The graph neural networking challenge: a worldwide competition for education in AI/ML for networks

José Suárez-Varela, Miquel Ferriol-Galmés, Albert López, Paul Almasan, Guillermo Bernárdez, David Pujol-Perich, Krzysztof Rusek, Loïck Bonniot, Christoph Neumann, François Schnitzler, François Taïani, Martin Happ, Christian Maier, Jia Lei Du, Matthias Herlich, Peter Dorfinger, Nick Vincent Hainke, Stefan Venz, Johannes Wegener, Henrike Wissing, Bo Wu, Shihan Xiao, Pere Barlet-Ros, Albert Cabellos-Aparicio

Abstract

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the “ITU AI/ML in 5G challenge”, an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the “Graph Neural Networking Challenge 2020”. We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

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COSMOS educational toolkit: using experimental wireless networking to enhance middle/high school STEM education

P. Skrimponis, N. Makris, S. Rajguru, K. Cheng, J. Ostrometzky, E. Ford, Z. Kostic, G. Zussman, T. Korakis

Abstract

This paper focuses on the educational activities of COSMOS – __C__loud enhanced __O__pen __S__oftware defined __MO__bile wireless testbed for city __S__cale deployment. The COSMOS wireless research testbed is being deployed in West Harlem (New York City) as part of the NSF Platforms for Advanced Wireless Research (PAWR) program. COSMOS’ approach for K–12 education is twofold: (i) create an innovative and concrete set of methods/tools that allow teaching STEM subjects using live experiments related to wireless networks/IoT/cloud, and (ii) enhance the professional development (PD) of K–12 teachers and collaborate with them to create hands-on educational material for the students. The COSMOS team has already conducted successful pilot summer programs for middle and high school STEM teachers, where the team worked with the teachers and jointly developed innovative real-world experiments that were organized as automated and repeatable math, science, and computer science labs to be used in the classroom. The labs run on the COSMOS Educational Toolkit, a hardware and software system that offers a large variety of pre-orchestrated K–12 educational labs. The software executes and manages the experiments in the same operational philosophy as the COSMOS testbed. Specifically, since it is designed for use by non-technical middle and high school teachers/students, it adds easy-to-use enhancements to the experiments’ execution and the results visualization. The labs are also supported by Next Generation Science Standards (NGSS)-compliant teacher/student material. This paper describes the teachers’ PD program, the NGSS lessons created and the hardware and software system developed to support the initiative. Additionally, it provides an evaluation of the PD approach as well as the expected impact on K–12 STEM education. Current limitations and future work are also included as part of the discussion section.

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Open Educational Resources for Computer Networking

Olivier Bonaventure, Quentin De Coninck, Fabien Duchêne, Anthony Gego, Mathieu Jadin, François Michel, Maxime Piraux, Chantal Poncin, Olivier Tilmans

Abstract

To reflect the importance of network technologies, networking courses are now part of the core materials of Computer Science degrees. We report our experience in jointly developing an open-source ebook for the introductory course, and a series of open educational resources for both the introductory and advanced networking courses. These ensure students actively engage with the course materials, through a hands-on approach; and scale to the larger classrooms and limited teaching staff, by leveraging open-source resources and an automated grading platform to provide feedback. We evaluate the impact of these pedagogical innovations by surveying the students, who indicated that these were helpful for them to master the course materials.

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Preprint

An Open Platform to Teach How the Internet Practically Works

Thomas Holterbach, Tobias Bü, Tino Rellstab, Laurent Vanbever

Abstract

Each year at ETH Zurich, around 100 students collectively build and operate their very own Internet infrastructure composed of hundreds of routers and dozens of Autonomous Systems (ASes). Their goal? Enabling Internet-wide connectivity. We find this class-wide project to be invaluable in teaching our students how the Internet infrastructure practically works. Among others, our students have a much deeper understanding of Internet operations alongside their pitfalls. Besides students tend to love the project: clearly the fact that all of them need to cooperate for the entire Internet to work is empowering. In this paper, we describe the overall design of our teaching platform, how we use it, and interesting lessons we have learnt over the years. We also make our platform openly available.

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