13. – 16. Juni 2021, Dagstuhl-Seminar 21243

Compute-First Networking


Jon Crowcroft (University of Cambridge, GB)
Philip Eardley (BT Applied Research – Ipswich, GB)
Dirk Kutscher (FH Emden, DE)
Eve M. Schooler (Intel – Santa Clara, US)

Auskunft zu diesem Dagstuhl-Seminar erteilt

Dagstuhl Service Team


Dagstuhl Report, Volume 11, Issue 5 Dagstuhl Report
Gemeinsame Dokumente


Edge- and more generally In-Network Computing are key elements in many traditional content distribution services today, typically connecting cloud-based computing to consumers. The advent of new programmable hardware platforms, research and wide deployment of distributed computing technologies for data processing, as well as new exciting use cases such as distributed Machine Learning and Metaverse-style ubiquitous computing are now inspiring research of more fine-granular and more principled approaches to distributed computing in the "Edge-To-Cloud Continuum".

The Compute-First Networking Dagstuhl seminar has brought together researchers and practitioners in the fields of distributed computing, network programmability, Internet of Things, and data analytics to explore the potential, possible technological components, as well as open research questions in an exciting new field that will likely induce a paradigm shift for networking and its relationship with computing.

Traditional overlay-based in-network computing is typically limited to quite specific purposes, for example CDN-style edge computing. At the same time, network programmability approaches such as Software-Defined Networking and corresponding languages such as P4 are often perceived as too limited for application-level programming. Compute-First Networking (CFN) views networking and computing holistically and aims at leveraging network programmability, server- and serverless in-network computing and modern distributed computing abstraction to develop a new system's approach for an environment where computing is not merely and add-on to existing networks, but where networking is re-imagined with a broader and ubiquitous notion of programmability.

We expect this approach to enable several benefits: it can help to unlock distributed computing from the existing silos of individual cloud and CDN platforms - a necessary condition to enable Keiichi Matsuda's vision of Hyper-Reality and Metaverse concepts where the physical world, human users and different forms of analytics, and visual rendering services constantly engage in information exchanges, directly at the edges of the network. It can also help to provide reliable, scalable, privacy-preserving and universally available platforms for Distributed Machine Learning applications that will play a key role in future large-scale data collection and analytics.

CFN's integrated approach allows for several optimizations, for example a more informed and more adaptive resource optimization that can take into account dynamically changing network conditions, availability of utilization of compute platforms as well as application requirements and adaptation boundaries, thus enabling more responsive and better-performing applications.

Several interesting research challenges have been identified that should be addressed in order to realize the CFN vision: How should the different levels of programmability in todays system be integrated into a consistent approach? How would programming and communication abstractions look like? How do orchestration systems need to evolve in order to be usable in these potentially large scale scenarios? How can be guarantee security and privacy properties of a distributed computing slice without having to rely on just location attributes? How would the special requirements and properties of relevant applications such as Distributed Machine best be mapped to CFN -- or should distributed data processing for federated or split Machine Learning play a more prominent role in designing CFN abstractions?

This seminar was an important first step in identifying the potential and a first set of interesting new research challenges for re-imaging distributed computing through CFN - an exciting new topic for networking and distributed computing research.

Summary text license
  Creative Commons BY 4.0
  Dirk Kutscher


  • Data Structures / Algorithms / Complexity
  • Networks


  • Networking
  • Distributed systems
  • In-network computing
  • Edge-computing


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