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1. Experience-Driven Research on Programmable Networks

   Hyojoon Kim, Xiaoqi Chen (January 2021, Computer communication review)

   null (Ed.)

   Many promising networking research ideas in programmable networks never see the light of day. Yet, deploying research prototypes in production networks can help validate research ideas, improve them with faster feedback, uncover new research questions, and also ease the subsequent transition to practice. In this paper, we show how researchers can run and validate their research ideas in their own backyards—on their production campus networks—and we have seen that such a demonstrator can expedite the deployment of a research idea in practice to solve real network operation problems. We present P4Campus, a proof-of-concept that encompasses tools, an infrastructure design, strategies, and best practices—–both technical and non-technical–—that can help researchers run experiments against their programmable network idea in their own network. We use network tapping devices, packet brokers, and commodity programmable switches to enable running experiments to evaluate research ideas on a production campus network. We present several compelling data-plane applications as use cases that run on our campus and solve production network problems. By sharing our experiences and open-sourcing our P4 apps [28], we hope to encourage similar efforts on other campuses. 

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2. Debugging SDN in HPC Environments

   https://doi.org/10.1145/3219104.3229277  

   Hayashida, Mami; Rivera, Sergio; Griffioen, James; Fei, Zongming; Song, Yongwook (July 2018, PEARC '18 Proceedings of the Practice and Experience on Advanced Research Computing)

   HPC networks and campus networks are beginning to leverage various levels of network programmability ranging from programmable network configuration (e.g., NETCONF/YANG, SNMP, OF-CONFIG) to software-based controllers (e.g., OpenFlow Controllers) to dynamic function placement via network function virtualization (NFV). While programmable networks offer new capabilities, they also make the network more difficult to debug. When applications experience unexpected network behavior, there is no established method to investigate the cause in a programmable network and many of the conventional troubleshooting debugging tools (e.g., ping and traceroute) can turn out to be completely useless. This absence of troubleshooting tools that support programmability is a serious challenge for researchers trying to understand the root cause of their networking problems. This paper explores the challenges of debugging an all-campus science DMZ network that leverages SDN-based network paths for high-performance flows. We propose Flow Tracer, a light-weight, data-plane-based debugging tool for SDN-enabled networks that allows end users to dynamically discover how the network is handling their packets. In particular, we focus on solving the problem of identifying an SDN path by using actual packets from the flow being analyzed as opposed to existing expensive approaches where either probe packets are injected into the network or actual packets are duplicated for tracing purposes. Our simulation experiments show that Flow Tracer has negligible impact on the performance of monitored flows. Moreover, our tool can be extended to obtain further information about the actual switch behavior, topology, and other flow information without privileged access to the SDN control plane. 

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3. BOARD # 355: EAGER GERMINATION: TRANSPIRE – Mentoring Postdocs via a transdisciplinary dialogic pedagogy for conceptualizing research questions with transformative potential

   https://doi.org/10.18260/1-2--55725  

   Vigdor, Linda; Wesson, Rosemarie; BRUMBERG, JOSHUA (June 2025, ASEE Conferences)

   Our project addresses a training gap in preparing emerging researchers for independent research career paths. We piloted and evaluated TRANSPIRE as a theory of change (TOC) model (De Silva et al., 2014). TRANSPIRE was motivated by a reality wherein the postdoc path tends to insufficiently prepare researchers in conceptual skills that ground impactful research careers, how to conceptualize transformative research questions that would frame or motivate their research, or to foreground the potential impacts (scientific &/or societal) of the research when devising a project. TRANSPIRE is based on ideas that a matrix of epistemologies, pragmatics, and values are needed to conceptualize and solve increasingly intractable problems Flyvbjerg (2001). We drew on an Aristotelian idea that places ¬practical wisdom on the same plane as epistemology and technical know-how. Specifically, three theories of learning frame our project: Scaffolding (Vygotsky, 1978), Reflective practice (Alvesson et al., 2017; Schön, 1991), and transdisciplinary learning (Mezirow, 1997). Scaffolding involves both peers and experts guiding learners to progress beyond their current zones of comfort or expertise. Reflective practice helps students focus on both processes/outcomes and the potential societal significance of their work. Transdisciplinary learning opens a space for “reflexive analysis and discussion of values and interests” (p.3), which grounds content in authentic problems–essential for adult learners. The program included short writing assignments, developing research statements for a job application, and writing white papers for a grant proposal. These were discussed in weekly dialogues with their postdoctoral peers, faculty mentors, and the PI/facilitator. Data gathered includes participant observations, recordings of weekly meetings, interviews and focus groups, pre/post surveys, and postdocs’ work products. Our cohorts over two years were small and thus, we employed a qualitative analysis, which integrates inductive category development and directed content analysis. Our poster will describe program specifics and findings about the postdocs’ engagement and learning, perspectives from faculty mentors, and data that supports our proposed Theory of Change. Briefly, most postdocs found the program beneficial and a first experience in reflecting deeply on the transformative potential of research questions. Most gained new understandings of transformative research and felt that the program would help them be more successful as they pursued independent research careers. Some significant challenges identified include a reality wherein postdocs tend to be used more as employees rather than trainees and participating in the program was an add-on to already burdened schedules. Similarly, many postdocs’ supervisors either misunderstood the program’s purpose or felt it would interfere with their postdoc’s responsibilities. The faculty fellows/mentors reported that they gained a great deal from participating in the weekly meetings, noting they now have a far better understanding of what is meant by transformative research and that they learned new mentoring approaches that they intend to bring to mentoring their own students or postdocs. A critical challenge identified is the need for substantive support for such a program by both postdocs’ supervisors and by the institution. Based on our data, we also propose ideas for incorporating aspects of our program in NSF’s mentoring plans. 

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4. Graphery : interactive tutorials for biological network algorithms

   https://doi.org/10.1093/nar/gkab420  

   Zeng, Heyuan; Zhang, Jinbiao; Preising, Gabriel A; Rubel, Tobias; Singh, Pramesh; Ritz, Anna (May 2021, Nucleic Acids Research)

   null (Ed.)

   Abstract Networks have been an excellent framework for modeling complex biological information, but the methodological details of network-based tools are often described for a technical audience. We have developed Graphery, an interactive tutorial webserver that illustrates foundational graph concepts frequently used in network-based methods. Each tutorial describes a graph concept along with executable Python code that can be interactively run on a graph. Users navigate each tutorial using their choice of real-world biological networks that highlight the diverse applications of network algorithms. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Graphery is available at https://graphery.reedcompbio.org/. 

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5. Neural Network Approximators for Marginal MAP in Probabilistic Circuits

   https://doi.org/10.1609/aaai.v38i10.28966  

   Arya, Shivvrat; Rahman, Tahrima; Gogate, Vibhav (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Probabilistic circuits (PCs) such as sum-product networks efficiently represent large multi-variate probability distributions. They are preferred in practice over other probabilistic representations, such as Bayesian and Markov networks, because PCs can solve marginal inference (MAR) tasks in time that scales linearly in the size of the network. Unfortunately, the most probable explanation (MPE) task and its generalization, the marginal maximum-a-posteriori (MMAP) inference task remain NP-hard in these models. Inspired by the recent work on using neural networks for generating near-optimal solutions to optimization problems such as integer linear programming, we propose an approach that uses neural networks to approximate MMAP inference in PCs. The key idea in our approach is to approximate the cost of an assignment to the query variables using a continuous multilinear function and then use the latter as a loss function. The two main benefits of our new method are that it is self-supervised, and after the neural network is learned, it requires only linear time to output a solution. We evaluate our new approach on several benchmark datasets and show that it outperforms three competing linear time approximations: max-product inference, max-marginal inference, and sequential estimation, which are used in practice to solve MMAP tasks in PCs. 

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