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CIENA booth 2344

SC2022 Workshop and Technical program presentations & involvement by UvA group members.

  • In the INDIS 2022: Annual International Workshop on Innovating the Network for Data-Intensive Science (INDIS 2022),
    INDIS Workshop Chairs: Mariam Kiran - Lawrence Berkeley National Laboratory (LBNL), Anu Mercian - Google LLC
    • Panel: 8:45am - 10am CST in room C156: "Network Research Exhibition – The Future of Networking and Computing with Big Data Streams"
    • In this panel session selected NRE demonstrators at SC22 will give their view on the the future of research networking in combination with demanding scientific applications. In a panel the researchers will debate with the public on their views and answer questions.
      • moderators: Cees de Laat, Scott Kohler.
    • Paper presentation: "11:40am - 12pm CST, Paper 2: "User-Driven Path Control through Intent-Based Networking"
      Presenters: Leonardo Boldrini, Anne-Ruth Meijer, Ralph Koning, Paola Grosso.
  • Network Research Exibition team member (CdL): The NRE contribution booths are 1600 (DoE), 2344 (CIENA), 2820 (CALTECH), 2847 (StarLight), 3247 (NICT) and 3824 (UTD).

Demos:

1

Scientific research achievements of Data Logistic for Logistic Data.

Smart Contract controlled networks at SC22

Multi-domain applications are characterized by applications such as workflows that cross domain boundaries. The motivation for such applications is in mutual benefit for all parties to collaborate. For example airline industries, healthcare, smart cities. The new set of challenges that this setup introduces revolve mainly around enforcement of agreed multilateral contracts and minimizing risks due to exposure. In this work we propose to encode the application agreement as a smart contract using Petrinet as a model to track state changes.

Location: booth 2344

More material:
2

User-Driven Path Control through Intent-Based Networking.

The UPIN (User-driven Path verification and con- trol in Inter-domain Networks) project aims to implement a mechanism for a user to control the way data are traversing the network. Here we investigates the possibilities and limita- tions of Intent-Based Networking (IBN) for user-driven path control. Exploring several intent translation techniques allows us to define four main factors that influence the design of an Intent-Based Networking approach. The level of control, level of required knowledge, type of language, and network all influence the design. Based on the UPIN project demo, we design two approaches: technical-centric, which focuses on enabling Intent- Based Networking, and human-centric, which focuses on relieving restrictions from the expression methods for the user. Rasa is used to create a chatbot interface for the human-centric approach. We experiment with several configurations to discover the optimal pipeline for our training and testing data. Results indicate an 85 percent accuracy of intent recognition and 93 percent accuracy of entity extraction. Although the accuracy is not high enough to allow the Intent-Based Networking implementation to make decisions without supervision, the implementation proves to be a viable method of expressing intents for user-driven path control.

Location: booth 2344

More material:

SCinet contributions, team members: