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Dagstuhl Seminar 21243

Compute-First Networking

( Jun 13 – Jun 16, 2021 )

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Computing and Networking are normally conceived as related but technically and culturally different areas. Distributed computing is typically implemented in overlays, treating the network as a pipe for shifting bits between computers. Networking, on the other hand, is typically oblivious to application and distributed system requirements, which often leads to suboptimal performance, robustness, and limited flexibility.

Computing and Networking are normally conceived as related but technically and culturally different areas. Distributed computing is typically implemented in overlays, treating the network as a pipe for shifting bits between computers. Networking, on the other hand, is typically oblivious to application and distributed system requirements, which often leads to suboptimal performance, robustness, and limited flexibility.

This Dagstuhl Seminar is intended to explore the potential of integrating computing and networking that we call Compute-First-Networking (CFN), because computation intercepts the data before it transits the next hop in the network. One of the CFN promises could be to overcome the performance and data privacy/security limitations of current cloud-based distributed applications. In the CFN vision, “compute” will become integrated into the network and storage fabric: network nodes will provide secure processing and storage for third party application and network functions, using a “Functions as a Service” or “serverless” programming paradigm readily accessible to application developers and third-party service providers.

Potentially, this approach can enable a new level of permissionless innovation where application providers and users can re-purpose and extend the available infrastructure without barriers, by developing and deploying new functions and applications in very short turn-around times. This seminar aims to bring together researchers and engineers from multiple relevant domains, including networking, distributed computing, security, and economics. The goal of the seminar is to begin a dialogue between the communities, to explore the potential as well as potential issues of a synthesis, and to formulate a research agenda that can help to investigate and validate the “Compute-First Networking” idea in follow-up research.

Our plan is that the format of the seminar will allow both in-person and on-line participation. We will take advantage of global participation by having several “time hubs” so that work can roll from one “time hub” to the next during the seminar’s 3 days.

Copyright Jon Crowcroft, Philip Eardley, Dirk Kutscher, and Eve M. Schooler


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.

Copyright Dirk Kutscher

  • Chris Adeniyi-Jones (ARM Ltd. - Cambridge, GB)
  • Laura Al Wardani (FH Emden, DE)
  • Uthra Ambalavanan (Robert Bosch GmbH - Renningen, DE)
  • Gianni Antichi (Queen Mary University of London, GB) [dblp]
  • Roberto Bifulco (NEC Laboratories Europe - Heidelberg, DE) [dblp]
  • Olivier Bonaventure (UC Louvain, BE) [dblp]
  • Kenneth L. Calvert (University of Kentucky - Lexington, US) [dblp]
  • Jon Crowcroft (University of Cambridge, GB) [dblp]
  • Philip Eardley (BT Applied Research - Ipswich, GB) [dblp]
  • T M Rayhan Gias (FH Emden, DE)
  • Tim Harris (Microsoft Research - Cambridge, GB) [dblp]
  • Jianfei He (City University - Hong Kong, HK)
  • Michio Honda (University of Edinburgh, GB)
  • Jussi Kangasharju (University of Helsinki, FI) [dblp]
  • Teemu Kärkkäinen (TU München, DE) [dblp]
  • Namseok Ko (ETRI - Daejeon, KR)
  • Michal Król (City - University of London, GB) [dblp]
  • Ike Kunze (RWTH Aachen, DE)
  • Dirk Kutscher (FH Emden, DE) [dblp]
  • Julie McCann (Imperial College London, GB) [dblp]
  • Jag Minhas (Sensing Feeling - London, GB)
  • Marie-Jose Montpetit (Concordia University - Montreal, CA) [dblp]
  • Naresh Nayak (Robert Bosch GmbH - Stuttgart, DE)
  • Erik Nordmark (Zededa - SanJose, US) [dblp]
  • David Oran (Network Systems Research & Design - Cambridge, US) [dblp]
  • Jörg Ott (TU München, DE) [dblp]
  • Andy Reid (BT - Ipswich, GB)
  • Eve M. Schooler (Intel - Santa Clara, US) [dblp]
  • Peer Stritzinger (Peer Stritzinger GmbH - Maisach, DE)
  • Christian Tschudin (Universität Basel, CH) [dblp]
  • Klaus Wehrle (RWTH Aachen, DE) [dblp]
  • Cedric Westphal (Futurewei - Santa Clara, US) [dblp]
  • Peter Willis (BT - Ipswich, GB)
  • Chenren Xu (Peking University, CN) [dblp]
  • Noa Zilberman (University of Oxford, GB) [dblp]

  • data structures / algorithms / complexity
  • networks

  • networking
  • distributed systems
  • in-network computing
  • edge-computing