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Development of a growth plan (to include both roadmap of projected feature sets as well as overall community growth/project maturity), to be done in conjunction with their project mentor(s) at the TAC.
Fledge Growth Plan
The Fledge Project and Community growth plans are for the industrial market. The industrial market has a long history of using sensors, actuators, proprietary networks and proprietary systems to automate operations. The founding of the ISA was April 1945. A successful edge computing solution must integrate with the systems and processes and "edges" that already exist in factories, mines and plants in order to be accepted and "grow" the Fledge project and community. From that position, modern IIoT, Fog, cloud, ML services can evolve. Competing with SCADA, DCS, PLC and the rest of the "active" ISA95 stack is out of scope and would likely retard growth.
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- Adding 100s of new southbound plugins supporting the many protocols, data mappings and standards that encourage collecting machine and sensor data (legacy and emerging)
- Growing the community of interested parties to build these plugins.
- Growing the community of Industrial System Integrators Integrators
- Support all major cloud IIoT onboarding APIs
- Google is our first major win. Google contributed the Fledge GC North plugin
- We would like to work with IBM on Watson and their focus on machines
- Support the major suppliers of ISA95 data managements services
- OSIsoft is our first major win (level 2-3) having 22,000 installs in process manufacturing.
- Kafka to Oracle is our second (level 4) and was coded by an Industrial SI.
- Support the major ML toolkits being designed for machine and process automation - edge based.
- TensorflowLite - done
- OpenVino (Intel’s) - 2020 complete
- Watson?
- Support for unique unique industrial data workloads where edge processing processing is required like - digital signal processing.
- Improved UI. Industrials demand significant productization even for PoCs. This is a challenge for many open source initiatives. Fledge includes a UI and will have many enhancements that help visualize/understand context context between sensor, machine, data streams/Fledge instance, component and Fledge hierarchies. (OpenFog Reference Architecture)
The Fledge roadmap also includes the integration and and testing with systems and subsystems lower in the stack. This is an immediate opportunity opportunity for cross project cooperation.
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Fledge has been commercially deployed in Industrial Use Cases since early 2019 including the 3 public use cases/deployments below. The Fledge Training Lab has been attended by more than 150 Industrial companies. The Core Fledge Package has been downloaded by >1000 unique industrial users who have registered for code and updates. As Github Projects realize, counting source code downloads is misleading. Fledge has had 100,000s of downloads downloads but counting unique users is not a Github feature.
Today Fledge has 3 public use cases.
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The Fledge project is exclusively focused on the industrial market. From a SWOT (Strength-Weakness-Opportunity-Threat) perspective, this limits the audience of potential committers since consumer, home, retail, gaming etc developers are not a fit. An additional challenge in this market is the lack of open source culture with industrials, equipment manufacturers and industrial system integrators. The Fledge project has the goal of changing this culture. Of the current 46 Git Authors roughly 80% are from the industrial market - exclusively.
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Fledge and Eve were the first two projects to engage in joint interoperability and compatibility engineering and testing. The effort started with FogLAMP in late 2018 before the formation of LF Edge. It was due to the efforts of Zededa Zededa and the Linux Foundation that encouraged Dianomic and OSIsoft to become founding board members. During that process, in December of 2019, a request was made to contribute FogLAMP to LF Edge.
When the board asked for its first joint project demo, EVE and Fledge stepped up and produced a model wind turbine and an industrial Flir Infrared camera demonstration. Fledge looks forward to more extensions with Akraino, Open Horizon and Intel SDO integrations in 2020.
Fledge has a unique architecture that enables rapid south and northbound protocols and data mappings to be written (w/o a new micro service). This code can be written in any language. Using Python a relatively junior engineer can be very productive (minutes to hours for data mappings, days new protocols). Most industrials have modest development development expertise. This feature is a major source of our community activity and addresses a critical pain point in the industrial market.
A recent request was made to leverage southbound connectivity across LF Edge Projects. Fledge is in several PoCs where Fledge has been used used to enable commercial platforms south connections. AWS Greengrass being one of them. We could imagine doing a similar northbound integration with the EdgeX Project if desired.