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Edge Computing Group


Mission Statement

  • This OpenInfra Edge Computing Group’s objective is to define infrastructure systems needed to support applications distributed over a broad geographic area, with potentially thousands of sites, located as close as possible to discrete data sources, physical elements or end users. The assumption is that network connectivity is over a WAN.
  • The OpenInfra Edge Computing Group will identify use cases, develop requirements, and produce viable architecture options and tests for evaluating new and existing solutions, across different industries and global constituencies, to enable development activities for Open Infrastructure and other Open Source community projects to support edge use cases.

Group Resources


  • Mondays at 6am PDT / 1300 UTC
    • Calendar file is available here.

Next meeting: Monday (June 05), 6am PST / 1300 UTC

Call details

  • Join at: https://zoom.us/j/5495195296?pwd=L3NycXhBRys3UEpOc2JzZjZuM25JUT09
    • Meeting ID: 549 519 5296
    • Passcode: openstack
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        • Meeting ID: 549 519 5296
    • Find your local number: https://zoom.us/u/ad8zlEN7JW

Action item registry

  • Find industry experts to invite to the conversations


Please feel free to add your topic to the agenda. Please add your name as well so we know on the meeting who to ping. Notes: https://etherpad.opendev.org/p/oif-edge-computing-group

  • Action items
  • Generative DevOps threatens Clouds Providers by Rob Hirschfeld
    • We spend a lot of time talking about how generative AI transforms application coding and technical debt, but we haven't discussed whether these same techniques can be applied to DevOps. Operations work requires significant expertise to maintain automation against incredibly complex infrastructure. Yet that could be significantly impacted by the capabilities of generative AI to build automation and manage systems. It is easy to imagine that the expertise moats defending cloud providers have in running and maintaining infrastructure could quickly fail. With Generative DevOps, individual people or companies could maintain their own systems. That would drastically change the ROI for buying versus renting.
  • Events
  • AoB

Upcoming Topics

  • May 29, 2023 - Canceled due to Memorial Day in the US
  • June 05 - Generative DevOps threatens Clouds Providers by Rob Hirschfeld
  • June 12, 2023 - Canceled due to colliding with the OpenInfra Summit

Meeting Logs


Private Mobility - Part 2 Session Recording, April, 2023

Private Mobility Session Recording, April, 2023

Virtual PTG Recording, March 2023

Virtual PTG Recording, October, 2022

Virtual PTG Recordings, April, 2022

Orchestration, Day-2 Operations and StarlingX session

Cheops project update from Inria

The Industry IoT Consortium (IIC) Edge Computing Efforts presentation by Chuck Byers

Networking and IPv6 discussion with Ed Horley

Networking and DNS discussion with Cricket Liu and Andrew Wertkin

Smart Edge presentation and discussion with Neal Oliver, November 15, 2021

Digital Rebar presentation and discussion with Rob Hirschfeld, November 8, 2021

Virtual PTG Recordings, October, 2021

CHI@Edge - recording of the session with the Chameleon project, September 13, 2021

Virtual PTG Recordings, April, 2021

Open Geospatial Consortium presentation, November 16, 2020

Virtual PTG Recordings, October, 2020

Password: ptg2020!

Virtual PTG Recordings, June, 2020

Archive - Weekly Call Logs


Working Group Activities


Use cases

Minimal Reference Architectures




Work items for testing

Hacking days

OpenStack Activities


StarlingX Activities

Adjacent Projects and Communities



  • Life-cycle Management. A virtual-machine/container/bare-metal manager in charge of managing machine/container lifecycle (configuration, scheduling, deployment, suspend/resume, and shutdown). (Current Projects: TK)
  • Image Management. An image manager in charge of template files (a.k.a. virtual-machine/container images). (Current Projects: TK)
  • Network Management. A network manager in charge of providing connectivity to the infrastructure: virtual networks and external access for users. (Current Projects: TK)
  • Storage Management. A storage manager, providing storage services to edge applications. (Current Projects: TK)
  • Administrative. Administrative tools, providing user interfaces to operate and use the dispersed infrastructure. (Current Projects: TK)
  • Storage latency. Addressing storage latency over WAN connections.
  • Reinforced security at the edge. Monitoring the physical and application integrity of each site, with the ability to autonomously enable corrective actions when necessary.
  • Resource utilization monitoring. Monitor resource utilization across all nodes simultaneously.
  • Orchestration tools. Manage and coordinate many edge sites and workloads, potentially leading toward a peering control plane or “selforganizing edge.”
  • Federation of edge platforms orchestration (or cloud-of-clouds). Must be explored and introduced to the IaaS core services.
  • Automated edge commission/decommission operations. Includes initial software deployment and upgrades of the resource management system’s components.
  • Automated data and workload relocations. Load balancing across geographically distributed hardware.
  • Synchronization of abstract state propagation Needed at the “core” of the infrastructure to cope with discontinuous network links.
  • Network partitioning with limited connectivity New ways to deal with network partitioning issues due to limited connectivity—coping with short disconnections and long disconnections alike.
  • Manage application latency requirements. The definition of advanced placement constraints in order to cope with latency requirements of application components.
  • Application provisioning and scheduling. In order to satisfy placement requirements (initial placement).
  • Data and workload relocations. According to internal/external events (mobility use-cases, failures, performance considerations, and so forth).
  • Integration location awareness. Not all edge deployments will require the same application at the same moment. Location and demand awareness are a likely need.
  • Dynamic rebalancing of resources from remote sites. Discrete hardware with limited resources and limited ability to expand at the remote site needs to be taken into consideration when designing both the overall architecture at the macro level and the administrative tools. The concept of being able to grab remote resources on demand from other sites, either neighbors over a mesh network or from core elements in a hierarchical network, means that fluctuations in local demand can be met without inefficiency in hardware deployments.