November 20 – 23 , 2022, Dagstuhl Seminar 22471

Towards More Flexible and Automated Communication Networks


Rui Aguiar (Institute of Telecommunications – Aveiro, PT)
Artur Hecker (Huawei Technologies – München, DE)
Stefan Schmid (TU Berlin, DE)
Henning Schulzrinne (Columbia University – New York, US)

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Christina Schwarz for administrative matters

Michael Gerke for scientific matters

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Viewed from a user perspective, communication networks work just fine. They, for example, deliver videos and other contents seamlessly and more or less reliably to end users. Furthermore, new technologies such as programmable networks and virtualization propel the potential of communication networks to a new level. Their behavior can now be programmed on-demand through software, and functions/services can be flexibly deployed at suitable locations in the network through network function virtualization. However, it has proven highly challenging to fully exploit this potential. Multiple virtual networks need to be operated on the same physical infrastructure without disturbing each other, realizing so-called network slices. Within some of the slices, strict performance guarantees are required (e.g., ultra-reliable low latency operation in some industrial environments).

Managing and operating such networks becomes increasingly challenging and calls for new approaches that can tackle the inherently increasing complexity. In particular, communication networks should increasingly operate autonomously without manual intervention. This also requires appropriate monitoring and data analysis approaches, as well as methods to describe and handle the “intent”, how the network should behave in order to fulfill user and service requirements. Generally, a more flexible usage of costly network infrastructures could help radically cut the service provisioning time while keeping the total cost of ownership low.

Besides traditional networking techniques, aspects of distributed systems become increasingly important, e.g., with respect to runtime, non-local resource scheduling. Moreover, modelling such complex networks in closed forms (e.g., queueing theory) appears to be increasingly less promising. Focusing, for example, on single TCP connections in the context of congestion control in such emerging complex and more and more automated communication environments appears to have strong limits. Therefore, machine learning recently also gets more attention in the networking community.

Consequently, autonomously operating and self-driving communication networks could highly profit from an interdisciplinary approach, e.g., including “classical” networking, distributed systems and machine learning. Our Dagstuhl Seminar will serve this interdisciplinary purpose. It will bring together experts from these disciplines including industry as well as academia. More specifically, synergies among the following aspects of automation in communication systems are considered: deployment and dynamic adjustment of (virtualized) network and upper-layer; services including demand-driven relocation of functionalities and services; robust and performant control planes in highly dynamic and autonomous communication; systems that share common networking resources potentially also with the data plane; network debugging and diagnostics (e.g., automated detection of routing failures or DDoS attacks).

We are also open to different types of networks and consider the entire spectrum, including wired/wireless/cellular/hybrid networks. This way, we may be able to discover new synergies and application domains within the Dagstuhl Seminar. This follows also the observation that the network landscape is becoming increasingly more diverse considering both, the technological as well as the administrative domain. So, this calls in general for concepts that can deal with this increasing diversity.

Motivation text license
  Creative Commons BY 4.0
  Rui Aguiar, Artur Hecker, Stefan Schmid, and Henning Schulzrinne


  • Networking And Internet Architecture


  • Software-defined networking
  • Self-driving networks
  • Automation
  • Machine learning


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