Upcoming meetings:
- September 30th at 8am PT (1500 UTC)
- Join Zoom Meeting
https://zoom.us/j/95227175653
- Join Zoom Meeting
- Meeting cadence: every Friday until November 15 (ONE Summit). After ONE Summit, we'll meet first Friday of every month at 8am PT
Past meetings:
- September 23rd
Meeting notes: https://docs.google.com/document/d/1WKILSmnbys-Oog23K8-mGKrc8YkgkmX9ZU8jWX5oLH0/edit
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https://zoom.us/rec/share/1-5RDopItOgUQFRofLlMj9oFa5_Nhi2I9XZP1pUmuTrivjGhfxicxJxVfP5eJPy2.uOk5XML3jzkVn2AE?startTime=1654793820000
Objectives of LF Edge E2E Showcase:
The LF Edge E2E Showcase was initiated by LF Edge SPC, and is currently being lead by LF Edge TAC with the support and participation of LF Edge staff and community.
The objectives of the program are:
To provide examples of on how to use LF Edge projects to solve real world problems
To help identify solution gaps for the LF Edge developer community and seek support
- To help project developers build a stronger business case for their sponsor's investment in their project
- To provide inspiration for more innovative projects under LF Edge
- To help increase user adoption for LF Edge projects
Requirements of E2E Showcase:
In order to ensure quality and consistency of each E2E showcase, please provide the following required content in order to be qualified as "In operation (ready to show) "showcase.
For those showcases that are not ready (don't have sufficient content for the following requirements, please put your showcase under the "In pipeline" area, and we'd be happy to move your showcase whenever you're ready.
- Title of the showcase
- Owner/Leader(s) of the showcase
- LF Edge projects involved (minimum 1 LF Edge project need to be in the showcase)
- Description (1. What real world problem this solution is designed to solve. 2) Solution architecture, and any additional technical information that you want to provide)
- How to install/use
- Current Adopters (minimum 1 user endorsement. Pls provide the enterprise or organization name. Alternatively, you can provide a description of the user if the user doesn't want to publish their company name. Example - A telco in UK, )
- Video/presentation (provide a video or a .ppt of the demo)
- Related talks/links
- Call to action (if you need more developers, resources, user endorsement, etc. please leave contact information)
In operation (ready to show):
LF Edge Cross Project Collaboration (Upstream project EdgeGallery + LF Edge Fledge + eKuiper + Akraino)
Owner:
khemendra kumar <khemendra.kumar13@gmail.com>
LF Edge project(s) involved:
EdgeGallery?, Fledge, eKuiper, Akraino
Description:
EALTEdge (Enterprise applications on lightweight 5G telco edge) BP from Akraino, which integrates various open source projects to build a MEC based edge computing platform. EALTEdge BP alongs with its upstream project EdgeGallery, providing an IOT stack which leverages Fledge(for IOT protocol and data collection) and eKuiper(Data Filter).
In this demo, we use a sample simulated IOT device. Data from Device is processed in pipeline in multiple stages like data collection from devices, data filtration and transformation then store in DB for offline scenarios.
Now IOT applications can access this data. It support http exporter to get data by application. Application like grafana can get data from DB as well.
In this Demo, we are using simulated MQTT device which produce readings every seconds and processed data is visualise in Grafana to monitor the device.
How to install/use:
Current Adoptors
Video/presentation:
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Related talks/links:
Call to action:
Please see more details at “Open Experience Lab” of EALTEdge (LF Edge end-to-end show-case)
Robotics
Owners:
Fukano Haruhisa <fukano.haruhisa@fujitsu.com>, Inoue Reo<inoue.reo@fujitsu.com>, Jeff Brower <jbrower@signalogic.com>
LF Edge project(s) involved:
Akraino
Description:
Enterprise robotics use cases in manufacturing, production, agriculture, and retail are emerging rapidly due to macro economic pressures, including cost of labor, manpower shortages, and legal/liability issues. In these use cases, functionality is most important, followed by reduced SWaP (size, weight, and power consumption), employee safety, data privacy, and cloud independence. To achieve these objectives requires progress in key areas of underlying robotics technology:
- Fusion of sensor touch and tactile data, combined with AI in order to handle objects of various shapes and friction coefficients, and in variable circumstances
- Computer vision. In addition to detecting and recognizing people, enterprise robots also must identify dangerous situations, for example leaning or unstable objects (such as a leaning pallet in a warehouse), incorrect lighting, slippery floors, foreign objects on a conveyor belt, etc.
- Speech recognition. First and foremost, enterprise robots need to recognize "immediate and urgent" voice commands in order to prioritize human safety; for example if someone shouts "Stop Now" the robot must stop - regardless of who is the speaker, level of background noise, or other circumstance. Second, enterprise robots need to accept verbal instructions, rather than programming interfaces (e.g. keyboard, app) inconvenient for rugged, wet, and fast-paced environments
- Data privacy. Enterprise operations do not trust public clouds with video and audio that may contain sensitive and/or proprietary information. Training for deep learning purposes must be handled on-premise or otherwise trusted manner
Video/presentation:
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Call to action:
How to install/use:
Current Adopters (or vaguely describe the adopter such as a major telco provider):
Related talks/links:
Clean Energy
Owners:
Mathew Yarger, Kathy Giori
LF Edge project(s) involved:
EVE, Project Alvarium
Description:
The growth in edge solutions has created a seismic shift in the ability to have a detailed understanding of data such as; where it comes from, who has access to it, how it’s been processed, and how it can be trusted. By combining edge solutions with scalable and efficient distributed ledger technologies, this level of understanding also comes with a high level of transparency which can provide a new level of confidence in how things are monitored, measured, reported, verified and utilized by applications. Project Alvarium has taken these technologies and created novel data confidence fabrics that allow all stakeholders to have up to date data that can be measured, annotated and disseminated efficiently, while also quantifying the confidence in the data based on built in methodologies that are being standardized by the industries the capabilities are being piloted with. In this use case, Alvarium has utilized the IOTA Tangle to provide transparency in the monitoring, reporting and verification process of clean energy solutions with support of partners ClimateCHECK, Dell Technologies, and Environment and Climate Change Canada (Canadian Government). This use case enables real time confidence in good and clean data, while also signifying which data is more inclined to be faulty through a lower confidence score. This helps to combat garbage data in problems while addressing concerns of greenwashing, and ensuring that innovations in clean energy are accurately reporting the impact they’re creating.
Video/presentation:
Below presentation includes a case study of a biodigester plant in Molina, Chile.
View file name OSS-LATAM-2022-EVE-KG.pdf height 250
Link to above slides as a video presentation.
More technical details about Alvarium and IOTA are in the slides below.
View file name DigitalMRV for Climate.pdf height 250
Call to action:
How to install/use:
Current Adopters (or vaguely describe the adopter such as a major telco provider):
Related talks/links:
DevOps MEC Infra Orchestration
Owner: Oleg Berzin oberzin@equinix.com
LF Edge project(s) involved:
Akraino
Description:
Public Cloud Edge Interface (PCEI) enables infrastructure orchestration and cloud native application deployment across public clouds (core and edge), edge clouds, interconnection providers and network operators. The notable innovations in PCEI are the integration of Terraform as a microservice to enable DevOps driven Infrastructure-as-Code provisioning of edge cloud resources (bare metal servers, operating systems, networking) public cloud IaaS/SaaS resources, private and public interconnection between edge cloud and public cloud, integration of Ansible as a microservice to enable automation of configuration of infrastructure resources (e.g., servers) and deployment of Kubernetes and its critical components (e.g., CNIs) on the edge cloud, and introduction of a workflow engine to manage the stages and parameter exchange for infrastructure orchestration and application deployment as part of a composable workflow. PCEI helps simplify the process of multi-domain infrastructure orchestration by enabling a uniform representation of diverse services, features, attributes, and APIs used in individual domains as resources and data in the code that can be written by developers and executed by the orchestrator, effectively making the infrastructure orchestration across multiple domains DevOps-driven.
Video/presentation:
Multimedia File MEC-TECH-SERIES_epN - DevOps MEC Infra Orchestration v0.4.mp4 |
Call to action:
How to install/use:
Current Adopters (or vaguely describe the adopter such as a major telco provider):
Related talks/links:
In pipeline:
Smart Edge Open(OpenNESS) for O-RAN
Owner: Keesang Song keesang.song@amd.com
LF Edge project(s) involved:
Magma, ORAN, EVE
Description:
To make available as a Smart Edge Open application a ready-to-use, downloadable O-RAN test / demo
OSC (O-RAN Software Community) is missing certain software components. Possibly Smart Edge Open(OpenNESS) can fill in these components
Currently in OSC there are solid implementations of Near RT RIC, Non RT RIC, and underlying O-Cloud support. However, key network elements are missing open source support, including:
O-CU – Radisys commercial binary in use in OSC F-release
O-DU – Open source O-DU-high is implemented in OSC, but the O-DU-low was using closed-source FlexRAN from Intel from the beginning, Consider OAI (OpenAirInterface 5G) Eurocomm group to make O-DU-Low-PHY stack available from open source library along with other 5G E2E SW stack
O-RU – For O-RU simulator, currently a Viavi TM500 in OSC community lab
To enable end-to-end demo and test/measurement setups, it’s important to figure out a way to have a full, end-to-end O-CU/O-DU/O-RU combination in place.
Video/presentation:
Call to action:
How to install/use:
Current Adopters (or vaguely describe the adopter such as a major telco provider):
Related talks/links:
Delivering, Managing, and Scaling Retail Applications and Analytics Inside Stores and Warehouses with Minimal IT Staff Touchpoints
Owner: Mahesh Dodani, IBM <dodani@us.ibm.com>
LF Edge project(s) involved:
EdgeX Foundry, Open Horizon, SDO (FDO), (incubating under EdgeX) ORRA
Description:
Video/presentation:
Link to demo at ORRA meeting, demo begins at 4:00 mark
Call to action:
How to install/use:
GitHub repo with demo source code - EdgeX Foundry Kamakura w/ Onvif Device Service + Edge Video Analytics + Camera Management Example App
Current Adopters (or vaguely describe the adopter such as a major telco provider):
Related talks/links:
Intel InnovatiON: Scalable Deployment of Edge Solutions with Intel NUC (Session NEC001) -
Brian McCarson, VP & Sr PE, GM of Intel NUC Group , Intel
Ashwin Vijayakumar, Strategic Business Development Manager, Intel
Sanjeev Gupta, Edge Computing Lead, Networking & Edge Computing CTO Group, IBM
Craig Theriac, VP of Product Management, Scale Computing
- Edge Computing Expo North America - Rob High keynote talk
ONE Summit: Rapidly Scaling Actionable Insights at the Point of Interaction - Mahesh Dodani, IBM & Sean Williams, Intel Corporation