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Edge Computing Group

Revision as of 16:57, 19 February 2019 by Ildiko (talk | contribs) (Meetings)

Group Resources


Weekly calls in alternating slots:

  • Every first Thursday of the month: 0700 UTC
  • On other weeks Tuesdays at 7am PDT / 1500 UTC

Calendar file is available here.

Next meeting: Tuesday (February 19), 7am PT / 1500 UTC

Call details

Action item registry

  • csatari to send a mail about the Suitable MVP architectures of the Use Cases - done
  • csatari map the user stories to the exact project requirements done
  • we should add the User Stories from the MVP Wiki to the storyboard. - done
  • Sebastian to add the "5G Service Delivery Platform over non-IP SDN-based Core" use case to the use cases wiki - will happen during February (blog posts and edge wiki)
  • csatari to give a try to map the Use Cases to the MVP architectures - done
  • csatari to check the terms used here and synch it to the edge glossary - in progress
  • We should define a next phase for the MVP and start to figure out what implementation is needed for that
  • Add feedback to the UC-s based on https://etherpad.openstack.org/p/BER-edge-use-cases-and-requirements


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.

Meeting Logs


Edge Reference Architectures



Use cases


Weekly calls on Mondays in alternating time slots:

  • Odd weeks: Monday 1pm Pacific Time / 2100 UTC

Next meeting: Monday (February 25) at 1pm Pacific Time / 2100 UTC

  • Setup goals for the group
  • Revisit the identified use cases
    • Do we have more details to either of them?
    • Ongoing work items relevant to either of them?
  • Use cases and MVP mapping
  • New use case ideas

Meeting Logs


Dublin workshop notes

  • Liaison: Gergely Csatari


  • Liaison: Ildiko Vancsa


Next meeting: Thursday (August 9) at 6am PDT / 1300 UTC



Related OSF Projects







IoTronic - unofficial



Liaison: Greg Waines

Adjacent communities


Liaison: Beth Cohen

ONAP Edge Automation Group

Liaison: Prakash Ramchandran

OPNFV Edge Cloud Project

Liaison: Gergely Csatari

Kubernetes IoT Edge Working Group


Use Cases

For specific case studies, see https://wiki.openstack.org/wiki/Edge_Computing_Group/Use_Cases

Data Collection and Analytics

IoT, where data is often collected from a large network of microsites, is an example of an application that benefits from the edge computing model. Sending masses of data over often limited network connections to an analytics engine located in a centralized data center is counterproductive; it may not be responsive enough, could contribute to excessive latency, and wastes precious bandwidth. Since edge devices can also produce terabytes of data, taking the analytics closer to the source of the data on the edge can be more cost-effective by analyzing data near the source and only sending small batches of condensed information back to the centralized systems. There is a tradeoff here—balancing the cost of transporting data to the core against losing some information.


Unfortunately, as edge devices proliferate––including mobile handsets and IoT sensors––new attack vectors are emerging that take advantage of the proliferation of endpoints. Edge computing offers the ability to move security elements closer to the originating source of attack, enables higher performance security applications, and increases the number of layers that help defend the core against breaches and risk.

Compliance Requirements

Compliance covers a broad range of requirements, ranging from geofencing, data sovereignty, and copyright enforcement. Restricting access to data based on geography and political boundaries, limiting data streams depending on copyright limitations, and storing data in places with specific regulations are all achievable and enforceable with edge computing infrastructure.

Network Function Virtualization (NFV)

Network Function Virtualization (NFV) is at its heart the quintessential edge computing application because it provides infrastructure functionality. Telecom operators are looking to transform their service delivery models by running virtual network functions as part of, or layered on top of, an edge computing infrastructure. To maximize efficiency and minimize cost/complexity, running NFV on edge computing infrastructure makes sense.


Real-time applications, such as AR/VR, connected cars, telemedicine, tactile internet Industry 4.0 and smart cities, are unable to tolerate more than a few milliseconds of latency and can be extremely sensitive to jitter, or latency variation. As an example, connected cars will require low latency and high bandwidth, and depend on computation and content caching near the user, making edge capacity a necessity. In many scenarios, particularly where closed-loop automation is used to maintain high availability, response times in tens of milliseconds are needed, and cannot be met without edge computing infrastructure.


Edge computing expands bandwidth capabilities, unlocking the potential of new immersive applications. Some of these include AR/VR, 4K video, and 360° imaging for verticals like healthcare. Caching and optimizing content at the edge is already becoming a necessity since protocols like TCP don’t respond well to sudden changes in radio network traffic. Edge computing infrastructure, tied into real-time access to radio/network information can reduce stalls and delays in video by up to 20% during peak viewing hours, and can also vary the video feed bitrate based on radio conditions.

Network Efficiency

Many applications are not sensitive to latency and do not require large amounts of nearby compute or storage capacity, so they could theoretically run in a centralized cloud, but the bandwidth requirements and/or compute requirements may still make edge computing a more efficient approach. Some of these workloads are common today, including video surveillance and IoT gateways, while others, including facial recognition and vehicle number plate recognition, are emerging capabilities. With many of these, the edge computing infrastructure not only reduces bandwidth requirements, but can also provide a platform for functions that enable the value of the application—for example, video surveillance motion detection and threat recognition. In many of these applications, 90% of the data is routine and irrelevant, so sending it to a centralized cloud is prohibitively expensive and wasteful of often scarce network bandwidth. It makes more sense to sort the data at the edge for anomalies and changes, and only report on the actionable data.

Self-Contained and Autonomous Site Operations

Many environments, even today, have limited, unreliable or unpredictable connectivity. These could include transportation (planes, buses, ships), mining operations (oil rigs, pipelines, mines), power infrastructure (wind farms, solar power plants), and even environments that should typically have good connectivity, like stores. Edge computing neatly supports such environments by allowing sites to remain semi-autonomous and functional when needed or when the network connectivity is not available. The best example of this approach is the need for retail locations to maintain their point of sales (POS) systems, even when there is temporarily no network connectivity.


Enterprises may have needs for edge computing capacity depending on workloads, connectivity limits and privacy. For example, medical applications that need to anonymize personal health information (PHI) before sending it to the cloud could do this utilizing edge computing infrastructure. Another way to look at requirements that would benefit from cloud edge computing is by the type of company that would deploy them. Operator applications are workloads put on edge computing infrastructure that is built and managed by operators—telecommunications companies, for example. Third-party applications are built by organizations to run on existing edge infrastructure, in order to leverage others’ edge computing infrastructure. It is worth noting that any applications could leverage any or all of the capabilities provided by a cloud—compute, block storage, object storage, virtual networking, bare metal, or containers.


  • 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.