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LF Edge

EdgeX Foundry – Delivering choice, flexibility, and collaboration at the IOT Edge

By Blog, EdgeX Foundry

Written by Keith Steele, CEO of IOTech Systems

After 3 years, I step down as the chair of the EdgeX Foundry Technical Steering Committee later this month. When I took on the role, I believed Open Software Platforms were fundamental to the success of edge computing and supported that view by backing EdgeX Foundry and starting IOTech. Today, with more than 6 million downloads, and many customer deployments later, both EdgeX and IOTech are strong. As we enter the next phase, here are my reflections and thoughts about where we go next…

From little acorns…..EdgeX Foundry

EdgeX Foundry was launched at Hannover Messe in April 2017 as an open source project hosted by The Linux Foundation. With this as a backdrop, we held our first project meeting in Boston in June 2017 and companies were invited to participate in creating EdgeX as an industrial IoT global edge software platform standard.

There were about 60 of us who showed up in person from all around the world, and even more joined by phone. We set our vision at this meeting and formed the EdgeX Technical Steering Committee to deliver that vision.

For the first 2 years, the team set about turning what was effectively a platform demonstrator project into a deployable product, including a complete rewrite of the code from Java into GO and C to reduce footprint and improve latency, critical requirements for edge-based systems. This culminated in the Edinburgh release in June 2019, when we announced to the world, we were satisfied the quality was sufficient to support deployed systems. Up to that point we had promoted EdgeX for use for pilot only implementations.

The Fundamentals

There are several fundamental technical and business tenets we set out so solve with EdgeX Foundry:

Open vs. Proprietary – The idea behind EdgeX is to maximize choice so users do not have to lock themselves into proprietary technologies that, by design, limit choice. Given the implicit heterogeneity at the edge, ‘open’ at a minimum means the EdgeX platform must be silicon, hardware, operating system, software application and cloud agnostic.

Secure, Pluggable and Extensible Software Architecture – To offer choice and flexibility to our users the EdgeX team chose a modern, distributed, microservices based software architecture, which we believe supports the inherent complexities at the edge.

Edge Software Application ‘Plug and Play’ – EdgeX provides a standard open framework around which an ecosystem can emerge. It facilitates interoperable plug-and-play software applications and value add services providing users with real choice, rather than having to deal with siloed applications, which may potentially require huge system integration efforts to deliver and end to end IoT Edge solution.

Time-Critical Performance and Scalability – Edge applications need access to ‘real-time’ data e.g. millisecond or even microsecond response times, often with absolute real-time predictability requirements. Access to real time data is a fundamental differentiator between the edge and cloud computing worlds. EdgeX addresses round trip response time requirements in the milliseconds with target operating environments are server and gateway class computers running standard Windows or Linux operating systems.

The EdgeX project decided to leave it to the ecosystem to address Time-Critical edge systems, which require ultra-low footprint, microsecond performance and even hard real time predictability.  IOTech, like many other EdgeX Foundry contributors, has created a commercial solution for EdgeX Foundry – called Edge XRT. It is important real-time requirements are understood in full, as decisions taken can significantly impact success or failure of edge projects.

Connectivity and Interoperability- A major difference between the edge and cloud is inherent heterogeneity and complexity at the edge.  The edge is where the IT computer meets the OT ‘thing’ and there is a multitude of ‘connectivity’ protocols at or close to real time. EdgeX provides reference implementations of some key protocols along with SDKs to readily allow users to add new protocols. The commercial ecosystem also provides many additional connectors, making connectivity a configuration versus a programming task. Likewise, Northbound there are multiple cloud and other IT endpoints and EdgeX provides flexible connectivity to and from these different environments. EdgeX is cloud agnostic coming with many standard integrations.

LF Edge launches

Equally as important as the technical deliverables for an open source project is the ecosystem of companies which support it. The EdgeX ecosystem was greatly enhanced in January 2019 when EdgeX Foundry became one of five founding projects in LF Edge, an umbrella organization created by The Linux Foundation that “aims to establish an open, interoperable framework for edge computing independent of hardware, silicon, cloud, or operating system.” LF Edge’s objective is to bring together complementary open source edge technologies.

As one of the Stage 3 projects under LF Edge, EdgeX Foundry momentum increased with additional global collaboration. The increased amplification and support across LF Edge projects, community and members has helped turn EdgeX into a high velocity project.

Delivering  complementary products and solutions

EdgeX is a framework around which a global ecosystem of vendors can emerge to offer complementary edge products and services including commercial support, training and customer pilot programs, which add value to the baseline product.

Here are a few examples:

EdgeX in Retail

A great example of how companies deliver solutions around EdgeX is Intel’s Open Retail Initiative (ORI), a collaborative effort led by Intel and top technology companies. The goal of ORI is to accelerate the scalable deployment of data-rich solutions optimized for in-store retail, from the edge to the cloud. ORI leverages EdgeX, alongside vendor proprietary solutions, to deliver use cases based on ecosystem components (or “ingredients”) having a common, open framework EdgeX, which enables an ecosystem of interchangeable components and accessible data—a future-ready platform for innovation. Members of the ORI community include some of the industry’s leading device makers, independent software vendors, system integrators, and consultants.

Remote monitoring telemetry of energy supply networks

In another application, one of the world’s leading energy infrastructure operators collaborated with a Global SI on several Industrial IoT projects aimed at digitalizing its main assets and energy distribution processes.

The SI helped the customer to implement a real-time monitoring system for several gas compression and storage injection stations, using the EdgeX platform for data collection and aggregation, and the at-the-edge calculation of relevant performance metrics.

This application provides the customer with a real-time overview of station operational conditions, and an historical view of data trends to more deeply understand the critical information needed by future analytics to improve Operational & Management activities

The business value to the client included: maintenance activities improved by real-time remote monitoring; data from different machines analyzed on the edge and gathered in a central platform, remote monitoring enabling new users’ education and system knowledge.

Underlying software infrastructure for an AI Edge Platform

Accenture, the world’s largest Systems Integrator has based the Edge offering for its Applied Intelligence Platform+ (AIP+) on EdgeX. AIP+ is marketed globally, and includes a collection of modular, pre-integrated AI services and capabilities to accelerate and scale new outcomes. The AIP+ Edge Agent includes tools and software to support the deployment and management of machine-learning and analytics-based intelligence on devices.

Accenture was particularly attracted by the EdgeX open ecosystem: open-source and container-based pedigree, no lock-in (cloud/ chipset/ OS agnostic), and the potential of strategic partnerships to complement Accenture’s AIP+ focus.

IOTech’s Contribution to the Ecosystem

In each of the above situations the participating companies all benefited from the choice, flexibility, and the collaboration EdgeX enables at the IoT edge. All of the above examples also exploited IOTech’s contributions to the EdgeX ecosystem using our hardened, extended and fully supported licensed version of EdgeX, Edge Xpert.

As mentioned earlier IOTech is also delivering a time critical version of the platform named XRT, and an extensive range of industrial grade north and southbound connectors.

The scale, complexity, and variability of systems at the edge means the management of edge-based systems can be challenging. Today’s enterprise management and deployment systems work very well in enterprise / cloud environments but are not well suited to edge deployments.  Resource constraints, intermittent connectivity of the edge, and the large variety of OT protocols are just some of the issues that present additional challenges to managing and deploying edge solutions. Also, local security constraints may mean that access to and from the cloud is severely curtailed or not permitted, so cloud-based management will not be possible.

What’s Next

The project has 170 code contributors. EdgeX Foundry just completed its sixth release (Geneva), and in just twelve months since the first official ‘ready for primetime’ V1 release back in June 2019, has achieved millions of container downloads world-wide.

The EdgeX Foundry project goes is strong with huge momentum behind its V1 Release.  We are now moving forward with EdgeX 2.0, which will see increased focus on outreach, including greater attention given to EdgeX users as well as developers.  Indeed, this is where I will be concentrating my efforts going forward after stepping down as TSC Chair in June.

I am very proud of what the team has collectively achieved with EdgeX in 3 years from our first meeting in Boston. We have taken EdgeX from a Dell CTO led project to be the premier open edge software platform across the world.

From a personal standpoint, I would offer these key observations and takeaways as to why EdgeX has been successful:

  • Create a real problem to solve that can unify a community around solving it and provide real business value to those who contribute.
  • Collaboration is key with consistency of participation, maintaining key technical community leadership and contributors for at least a couple of years is very important. Projects like EdgeX take time, the problem is complex and there is no short cut to delivering quality.
  • Do not boil the ocean, as the diversity of your project increases look for alignment and complementary projects that can move your forward. EdgeX security is a prime example of this.
  • Understand your objectives and design the project accordingly, our objective in EdgeX was to grow a global standard. We took a decision in the early days to focus on quality, testing and API consistency to ensure what we delivered was of product quality, this has ensured we have a supportable and evolvable product that users can rely on.
  • Set the expectations of your target users. The team worked hard to make the 4th release (Edinburgh release) ‘deployment ready’ and, once we announced this, our downloads increased and we’re now heading to mass adoption on a global scale.

Most importantly, EdgeX Foundry has been a great team effort and a lot of fun! I am very much looking forward to the next phase of success.

Some Closing Thoughts

The full promise of IoT will be achieved when you combine the power of cloud and edge computing: delivering real value that allows businesses to analyze and act on their data with incredible agility and precision, giving them a critical advantage against their competitors.

The key challenges at the edge related to latency, network bandwidth, reliability, security, and OT heterogeneity cannot be addressed in cloud-only models – the edge needs its own answers.

EdgeX Foundry and the LF Edge ecosystem offer an answer, maximizing user choice and flexibility and enabling effective collaboration across multiple vertical markets at the edge helping to power the next wave of business transformation.

To learn more, please visit the EdgeX website and LF Edge website and get involved!

 

State of the Edge and Edge Computing World Present the Second Annual Edge Woman of the Year Award

By Announcement, State of the Edge

Edge Computing Industry Seeks to Recognize Women Shaping the Future of Edge and Invites Nominations for 2020

AUSTIN, Texas – July 1, 2020 – State of the Edge, an open source project under the LF Edge umbrella dedicated to publishing free research on edge computing, and Edge Computing World, an event that brings together the entire edge ecosystem, have announced they are accepting nominations for the Second Annual Edge Woman of the Year Award 2020. The award recognizes leaders who have been impacting their organization’s strategy, technology or communications around edge computing, edge software, edge infrastructure or edge systems. The organizers encourage industry participants to nominate their colleagues for qualified women to nominate themselves. The “Top Ten Women in Edge” finalists will be selected by the organizers and the final winner will be chosen by a panel of industry judges. Finalists will be announced at Edge Computing World, being held virtually October 12-15, 2020.

“By honoring the innovative women pushing the edge computing industry forward, we acknowledge the importance of their work and the continued need for diversity in a burgeoning and innovative field,” said Candice Digby, Partnerships and Events Manager at Vapor IO. “We are thrilled to host the second annual Edge Woman of the Year award program and look forward to honoring this year’s leader.”

State of the Edge and Edge Computing World are proud to sponsor the second annual Edge Woman of the Year Award, presented to outstanding female and non-binary professionals in edge computing for exceptional performance in their roles elevating Edge. This award highlights the growing importance of the contributions and accomplishments of women in this innovative industry. Nominations are now being accepted, and can be entered here.

Nominees will be evaluated on the following criteria:

  • Career contributions and involvements (ex. industry associations, open-source contributions, etc.)
  • Overall involvement in greater technology industry and demonstration of leadership qualities
  • Specific contributions to edge computing (team projects and collaborations admissible)
  • Contributions and involvement need not be technical; the award may be given to those in functions that include senior leadership, sales, marketing, etc.

Advisory Board of the 2020 Edge Woman of the Year Award include:

  • Nadine Alameh, CEO, Open Geospatial Consortium
  • Samantha Clarke, Director of Business Development, Seagate Technology
  • Michelle Davis, Manager, DoD/IC Specialist SA team, Red Hat
  • Eliane Fiolet, Co-Founder, Ubergizmo
  • Janet George, GVP Autonomous Enterprise, Oracle Cloud
  • Maribel Lopez, Founder and Principal Analyst, Lopez Research
  • Maemalynn Meanor, Senior PR and Marketing Manager, The Linux Foundation
  • Carolina Milanesi, Founder, The Heart of Tech
  • Molly Wojcik, Director of Education & Awareness, Section

“It was an honor to acknowledge an exceptionally strong group of nominees last year, and we look forward to again recognizing those iterating on the edge computing technology in exceptionally creative ways this year,” said Gavin Whitechurch of Topio Networks and Edge Computing World. “It is imperative we take note of and acknowledge our colleagues leading the edge computing revolution, and we look forward to doing that with this year’s Edge Woman of the Year award.”

For more information on the Women in Edge Award, please visit http://www.edgecomputingworld.com/edgewomanoftheyear.

About State of the Edge

State of the Edge is an open source project under the LF Edge umbrella that publishes free research on edge computing. It is a Stage 2 project (growth) under LF Edge and is divided into three working groups: Open Glossary of Edge Computing, the Edge Computing Landscape and the State of the Edge reports. All State of the Edge research is  offered free-of-charge under a Creative Commons license, including the landmark 2018 State of the Edge report, the 2019 Data at the Edge report and, most recently, the 2020 State of the Edge report.

About Edge Computing World

Edge Computing World is the only event that brings together users and developers with the entire edge ecosystem to accelerate the edge market & build the next generation of the internet. For 2020 the virtual event focuses on expanding the market, with new features including the Free-to-Attend Edge Developers Conference & the Free-to-End Users Edge Executive Conference.

New Training Course Aims to Make it Easy to Get Started with EdgeX Foundry

By Announcement, EdgeX Foundry, Training

Course explains what EdgeX Foundry is, how it works, how to use it in your edge solutions, leveraging the support of LF Edge’s large ecosystem 

SAN FRANCISCO, July 1, 2020 – The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced the availability of a new training course, LFD213 – Getting Started with EdgeX Foundry.

LFD213, was developed in conjunction with LF Edge, an umbrella organization under The Linux Foundation that aims to establish an open, interoperable framework for edge computing independent of hardware, silicon, cloud, or operating system. The course is designed for IoT and/or edge software engineers, system administrators, and operation technology technicians that want to assemble an edge solution.

The course covers how EdgeX Foundry is architected, how to download and run it, and how to configure and extend the EdgeX framework when needed. The four chapters of the course, which take approximately 15 hours to complete, provide a basic overview, a discussion of device services, which connect physical sensors and devices to the rest of platform, application services, how to send data from EdgeX to enterprise applications, cloud systems, external databases, or even analytics packages, and more.

Hands-on labs enable students to get and run EdgeX and play with some of its important APIs, as well as create a simple service (either device or application service) and integrate it into the rest of EdgeX.

EdgeX Foundry is an open-source, vendor-neutral, hardware- and OS-agnostic IoT/edge computing software platform that is a Stage 3 (Impact) project under LF Edge. In the simplest terms, it is edge middleware that sits between operational technology, physical sensing “things” and information technology systems. It facilitates getting sensor data from any “thing” protocol to any enterprise application, cloud system or on-premise database. At the same time, the EdgeX platform offers local/edge analytics to be able to offer low latency decision making at the edge to actuate back down onto sensors and devices. Its microservice architecture and open APIs allow for 3rd parties to provide their own replacement or augmenting components and add additional value to the platform. In short, EdgeX Foundry provides the means to build edge solutions more quickly and leverage the support of a large ecosystem of companies that participate in edge computing.

“EdgeX Foundry is on a phenomenal growth trajectory with multiple releases and millions of container downloads,” said Jim White, EdgeX Foundry Chair of the Technical Steering Committee and CTO of IOTech Systems.  “Given the scale of the adopting community and ecosystem, it is critical that there is proper training available to allow new adopters and prospective users to learn how to get started. The new training, created by the architects of EdgeX Foundry and managed by The Linux Foundation, will allow developers exploring EdgeX a faster and better path to understand and work with EdgeX while also accelerating our project’s adoption at scale.”

The course is available to begin immediately. The $299 course fee provides unlimited access to the course for one year including all content and labs. Interested individuals may enroll here.

About the Linux Foundation

Founded in 2000, the Linux Foundation is supported by more than 1,000 members and is the world’s leading home for collaboration on open source software, open standards, open data, and open hardware. Linux Foundation’s projects are critical to the world’s infrastructure including Linux, Kubernetes, Node.js, and more. The Linux Foundation’s methodology focuses on leveraging best practices and addressing the needs of contributors, users and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org.

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see its trademark usage page: www.linuxfoundation.org/trademark-usage. Linux is a registered trademark of Linus Torvalds.

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EdgeX Foundry Kubernetes Installation

By Blog, EdgeX Foundry

Written by Jason Bonafide, EdgeX Foundry Contributor and Principal Software Engineer at Dell Technologies

In an ever-growing world of connected devices, there is plenty of opportunity in edge computing. While devices are getting smaller and smarter, there is always a need to share data. With that, I have just the platform for you!

EdgeX Foundry is a vendor-neutral open source project hosted by LF Edge building a common open-framework for IoT edge computing. EdgeX Foundry offers a set of interoperable plug-and-play components which aim to satisfy IoT solutions of all variations.

The goal of this blog is to walk through techniques which can be used in deploying EdgeX Foundry to a Kubernetes cluster. Establishing a foundation for deploying EdgeX in Kubernetes is the main takeaway from this tutorial.

Tools and technologies used

Why Deploy EdgeX to a Kubernetes cluster?

Kubernetes provides the following feature set:

  • Service Discovery and load balancing
  • Storage orchestration
  • Automated roll-outs and rollbacks
  • Automatic bin packing
  • Self-healing
  • Secret and configuration management

EdgeX Foundry is built on micro-service architecture. The micro-service architecture is powerful, but it can make deploying and managing an application more complex because of all the components. Kubernetes makes deploying micro-service applications more manageable.

Glossary

Kubernetes

  • Affinity: Act of constraining a Pod to a particular Node.
  • ConfigMap: An API object used to store non-confidential data in key-values pairs.
  • Deployment: Provides declarative updates for Pods and ReplicaSets.
  • Ingress: Exposes HTTP and HTTPS routes from outside the cluster to Services within the cluster.
  • kubelet: Primary “node agent” that runs on each Node. It works in terms of a PodSpec.
  • LivenessProbe: The means used in a Kubernetes Deployment to check that a Container is still working and to determine whether or not it needs to be restarted.
  • Node: Virtual or physical machine in which Pods are run.
  • PersistentVolume: A piece of storage in the cluster that has been provisioned by an administrator or dynamically provisioned using StorageClasses.
  • PersistentVolumeClaim: A request for storage by a user.
  • Pod: The smallest deployable unit of computing that can be created and managed in Kubernetes.
  • PodSpec: A YAML or JSON object that describes a Pod.
  • ReadinessProbe: The means used in a Kubernetes Deployment to check when a Container is ready to start accepting traffic.
  • Secret: An object that contains a small amount of sensitive data such as a password, a token or a key.
  • Service: An abstract way to expose an application running on a set of Pods as a network service.
  • StartupProbe: The means used in a Kubernetes Deployment to check whether or not the Container’s application has started. If such a probe is configured, it disables liveness and readiness checks until it succeeds, making sure those probes don’t interfere with the application startup.
  • StorageClass: Provides a way for administrators to describe the “classes” of storage they offer.
  • Volume: A directory, possibly containing some data which can be accessed by a Container.
  • Volume Mount: A property for a Container in which a Volume is bound the Container.

Helm

  • Chart: An artifact which contains to a collection of Kubernetes resource files.
  • Named Template: A template which has an identifying name. Named-templates are also referred to as “partials”. Named-templates are similar to that of a programming language’s function which can be used to re-use code (or in this case YAML configuration).
  • Template: A file that can hold placeholders {{}} and are interpolated by specified values
  • yaml: A configuration file within Helm which enables abstraction of configurable items which can be applied to templates.

Setting up manifests directory

Create the directory structure below on your machine. The folders will be used and populated throughout this tutorial.

project-root/
edgex/
templates/
edgex-redis/
edgex-core-metadata/
edgex-core-data/
edgex-core-command/

Before we create definition files

Kubernetes provides a recommended set of labels. These labels provide a grouping mechanism which facilitate management of Kubernetes resources which are bound to an application. Below is Kubernetes’ recommended set of labels:

app.kubernetes.io/name: <application name>
app.kubernetes.io/instance: <installation>
app.kubernetes.io/version: <application version>
app.kubernetes.io/component: <application component>
app.kubernetes.io/part-of: <organization>
app.kubernetes.io/managed-by: <orchestrator>

A concrete example of this would be:

app.kubernetes.io/name: edgex-core-metadata
app.kubernetes.io/instance: edgex
app.kubernetes.io/version: 1.2.1
app.kubernetes.io/component: api
app.kubernetes.io/part-of: edgex-foundry
app.kubernetes.io/managed-by: Kubernetes

With the above label structure, and kubectl’s support of filtering resources by labels, we’ve created enough labels which give us plenty of flexibility when searching for specific resources. An example of this would look like:

$ kubectl get pod -l app.kubernetes.io/name=edgex-core-metadata

In a cluster with many applications, the above command will result in selecting only edgex-core-metadata pods.

On to our first application – edgex-redis

EdgeX Foundry supports Redis as a persistent datastore. Due to the fact that Containers are ephemeral, data created within a Container will live only as long as the Container. In order to keep data around past the life its Container, we will need to use a PersistentVolume.

Lets say that we want to create a PersistentVolume with the following in mind:

  • Our cluster contains a StorageClass named hostpath.
  • Our use-case requires 10Gi of storage (not to be confused with GB – Kubernetes has its own Resource Model).
  • Redis data needs to be stored on a cluster node’s file-system at /mnt/redis-volume. It is worth noting that hostPath storage plugin is not recommended in production. If your cluster contains multiple nodes, you will need to ensure that the edgex-redis Pod is scheduled on the same node. This is to ensure that edgex-redis references the same hostPath storage each time the Pod is scheduled. Please refer to Assigning Pods to Nodes documentation for more information.
  • We anticipate many nodes having Read and Write access to the PersistentVolume.

With the above requirements, our PersistentVolume definition in templates/edgex-redis/pv.yaml should be updated to look like this.

Although the PersistentVolume has not been created yet, we’ve defined a resource that will make the storage we defined in the PersistentVolume definition available for binding. Next, we will need to create a resource that will assign a PersistentVolume to an application. This is accomplished using a PersistentVolumeClaim.

The pattern used in this tutorial establishes a one-to-one relationship between application and a PersistentVolume. That means, for each PersistentVolume, there will exist a PersistentVolumeClaim for an application. With that in mind, our claim for storage capacity, access modes, and StorageClass should match the PersistentVolume we defined earlier.

Lets go over the requirements for our storage use-case:

  • 10Gi storage.
  • Read and Write access by many nodes.
  • A StorageClass named hostpath exists in our cluster.

Given the above requirements, the PersistentVolumeClaim definition file at templates/edgex-redis/pvc.yaml should be updated to look like this.

Now that the PersistentVolume and PersistentVolumeClaim have been defined, lets move on to the Deployment.

Similar to what was done with the PersistentVolume and PersistentVolumeClaim, lets list things that describe the Redis application:

  • For simplicity sake, lets say we only create 1 instance (replica).
  • When a new version is rolled out, we want to kill all of the old pods before bringing up new pods. This is referred to as a Recreate deployment strategy.
  • The Deployment consists of a single process (Container).
  • The Container image will come from Dockerhub’s redis image of latest
  • The Container will listen to connections on port 6379.
  • The application will write data to /var/lib/redis. The Container’s data directory will be mounted to the PersistentVolume via the PersistentVolumeClaim named edgex-redis which we created earlier. The Volume mapped to the PersistentVolumeClaim will be named redis-data and can be mounted as Volume for the Container.
  • The application is in a Started state when a connection can be successfully established over TCP on port 6379. This check will be tried every 10 On the first success, the Container will be in a Started state, however, on the 5th failure to obtain a connection, the Container will be killed. When this StartupProbe is enabled, LivenessProbe and ReadinessProbe are disabled until StartupProbe succeeds.
  • Every 30 seconds, only after a 15 second delay, the Deployment will ensure that the Container is alive by establishing a connection over TCP on port 6379. On the first success, the Container will be in a Running state, however, on 3 failures, the Container will be restarted.
  • Every 30 seconds, only after a 15 second delay, the Deployment will try to determine if the Container is ready to accept traffic. Over TCP, the Deployment will attempt to establish a connection on port 6379. On 3 failures, the pod is removed from Service load balancers.

Given the above requirements, we can define our Deployment in templates/edgex-redis/deployment.yaml should be updated to look like this.

Lastly, for edgex-redis, we will need to establish network access to our Pod within the Kubernetes cluster. This is accomplished by defining a Service.

As for our Service requirements:

  • selector should only match the labels defined in the edgex-redisDeployment’sPodSpec.
  • Map external port 6379 to Container port 6379 with a name of port-6739. Each port requires a name.

On to the next application – edgex-core-metadata

EdgeX application supports the concept of externalized configuration. This is a neat feature enabling the platform’s portability. For our Deployment, we will define our configuration as a Secret. Normally configuration would be defined in a ConfigMap, however, since Redis credentials reside in our configuration file, we will stash the entire configuration file in a Secret.

We are going to leverage the default application configuration and make just a few just a few modifications:

  • Core Metadata Service Host property has been updated to listen on 0.0.0 which ensures that the Container is listening on all network interfaces.
  • Core Data Service Host property has been updated to edgex-core-data Later on when edgex-core-data is installed, a Service will expose edgex-core-data as the hostname of edgex-core-data.
  • Database Host property has been updated to edgex-redis Service

Feel free to refer to the reference project configuration files here.

Lets create the secret by performing the following steps:

  • Download https://github.com/jbonafide623/edgex-lf-k8s/blob/master/secrets/edgex-core-metadata-secret or create your own.
  • Execute $ kubectl create secret generic edgex-core-metadata –from-file=configuration.toml=edgex-core-metadata-secret (where edgex-core-metadata-secret points to the file downloaded in the previous step).

Now for the edgex-core-metadata Deployment. Lets list out some of the requirements:

  • We will create only 1 instance (replica).
  • When a new version is rolled out, we want to kill all of the old pods before bringing up new pods. This is referred to as a Recreate deployment strategy.
  • The Deployment consists of a single process (Container).
  • The Container image will come from Dockerhub’s edgexfoundry/docker-core-metadata-go image of 2.1 tag.
  • Override the Dockerfile’s Entrypoint so that –confdir points to /config.
  • Disable security via EDGEX_SECURITY_STORE environment variable. This can be done by setting the flag to “false”.
  • The Container will listen to HTTP requests on port 48081.
  • The application is in a Started state when a n HTTP GET request to /api/v1/ping results in a 200 response. This check will be tried every 10 On the first success, the Container will be in a Started state, however, on the 5th failure to obtain a connection, the Container will be killed. When this StartupProbe is enabled, LivenessProbe and ReadinessProbe are disabled until StartupProbe succeeds.
  • Every 30 seconds, only after a 15 second delay, the Deployment will ensure that the Container is alive by sending an HTTP GET request to /api/v1/ping. On the first success, the Container will be in a Running state, however, on 3 failures, the Container will be restarted.
  • Every 30 seconds, only after a 15 second delay, the Deployment will try to determine if the Container is ready to accept traffic. Over TCP, the Deployment will attempt to send an HTTP GET request to /api/v1/ping. On 3 failures, the pod is removed from Service load balancers.
  • Establish a limit on cpu usage of 1 and request a starting allocation of 5 cpus.
  • Establish a limit on memory usage of 512Mi and request a starting allocation of 256Mi memory.
  • Mount the configuration Secret with name edgex-core-metadata to the Container’s path /config. Recall /config is the path that we supply as a –confdir override to the application’s image.

Given the above requirements, we can define our Deployment definition file at templates/edgex-core-metadata/deployment.yaml and it should look like this.

Lastly, lets expose the application so that it can be accessed from within and outside of the cluster. For our Service lets define the some requirements:

  • selector should only match the labels defined in the edgex-core-metadataDeployment’sPodSpec.
  • Map external port 48081 to Container port 48081 with a name of port-48081. Each port requires a name.
  • Expose the application outside of the cluster via NodePort type on port 30801

Given the above requires we can define our Service definition file at templates/edgex-core-metadata/service.yaml and it should look like this.

Incorporating Helm

The decision to include Helm in the container-orchestration stack is based on the following Helm features:

  • Helm Facilitates a single responsibility which is to manage Kubernetes resources.
  • Enables easy application installation and rollback.
  • Supports ordered creation of resources via hooks.
  • Provides access to installations of popular applications via Helm Hub.
  • Encourages code-reuse.

Each Helm chart contains values.yaml and Chart.yaml files. In the root of the project directory, lets go ahead and create these files by executing:

touch values.yaml
touch Chart.yaml

Chart.yaml contains information about the chart. With that in mind, lets add the following content to Chart.yaml:

apiVersion: v2
name: edgex
description: A Helm chart for Kubernetes

# A chart can be either an ‘application’ or a ‘library’ chart.
#
# Application charts are a collection of templates that can be packaged into versioned archives
# to be deployed.
#
# Library charts provide useful utilities or functions for the chart developer. They’re included as
# a dependency of application charts to inject those utilities and functions into the rendering
# pipeline. Library charts do not define any templates and therefore cannot be deployed.
type: application

# This is the chart version. This version number should be incremented each time you make changes
# to the chart and its templates, including the app version.
# Versions are expected to follow Semantic Versioning (https://semver.org/)
version: 0.1.0

# This is the version number of the application being deployed. This version number should be
# incremented each time you make changes to the application. Versions are not expected to
# follow Semantic Versioning. They should reflect the version the application is using.
appVersion: 1.2.1

values.yaml allows you to create a YAML object which can be referenced in your chart’s template files.

Lets make use of what values.yaml can offer us.

For edgex-redis, lets list out some properties that might be beneficial to abstract out as a configurable option:

  • Application name: if you noticed, almost every resource uses the name edgex-redis. These resources can be accessed by other applications. For instance, edgex-core-metadata references the Service defined for edgex-redis. If we abstract the name out to yaml, the application name will only need to change in a single place (values.yaml).
  • Deployment strategy: If there is a particular environment where you are concerned with high-availability, you may want to leverage RollingUpdate In smaller environments, you may or may not care about a RollingUpdate strategy and would chose Recreate in effort to conserve computing resources.
  • Image name and tag: There exist scenarios where artifacts/dependencies are vetted and kept “in-house”. Scenarios like these include artifact/image repositories of their own. With that in mind, having the ability to switch the image registry during installation can be a huge benefit.
  • Port: The port at which the Container listens on is something that can easily be referenced in many places within a single chart. As you define various network components such as Ingresses, Services, and even the Container port, it would be nice to refer back to a single place.
  • Replicas: Depending on the environment’s resources, the number of replicas may change.
  • StorageClassName: There may exist a scenario where the StorageClass may completely vary from cluster to cluster or even within a single cluster.

Considering the above, we can define our edgex-redis configuration object like this:

edgex:
redis:
name: edgex-redis
deployment:
strategy: Recreate
image:
name: redis
tag: latest
port: 6379
replicas: 1
storageClassName: hostpath

We can apply the same pattern with a few adjustments for edgex-core-metadata:

edgex:
metadata:
name: edgex-core-metadata
deployment:
strategy: Recreate
image:
name: edgexfoundry/docker-core-metadata-go
tag: 1.2.1
port: 48081
replicas: 1
resources:
limits:
cpu: 1
memory: 512Mi
requests:
cpu: 0.5
memory: 256Mi

In the configuration object for edgex-core-metadata we added a resources object which allows us to adjust limits and requests during installation.

Remember earlier when we talked about recommended labels and how these labels apply to each of our resources? With Helm, we can create a named-template and include the named-template in each place where the labels are referenced.

Here, in the file templates/_labels.tpl, we are creating a named-template named edgex.labels. This named-template takes in two arguments a ctx (context) and AppName (application’s name).

{{/*
Define a standard set ouf resource labels.

params:
(context) ctx – Chart context (scope).
(string) AppName – Name of the application.
*/}}
{{ define “edgex.labels” }}
app.kubernetes.io/app: {{ .AppName }}
app.kubernetes.io/instance: {{ .ctx.Release.Name }}
app.kubernetes.io/version: {{ .ctx.Chart.AppVersion }}
app.kubernetes.io/component: api
app.kubernetes.io/part-of: edgex-foundry
app.kubernetes.io/managed-by: {{.ctx.Release.Service }}
helm.sh/chart: {{ .ctx.Chart.Name }}-{{ .ctx.Chart.Version | replace “+” “_” }}
{{ end }}

Now that we’ve defined configuration in values.yaml and created edgex.labels named template, lets apply them to a simple definition file in templates/edgex-redis/service.yaml.

apiVersion: v1
kind: Service
metadata:
name: {{ .Values.edgex.redis.name }}
labels:
{{- include “edgex.labels” (dict “ctx” . “AppName” $.Values.edgex.redis.name) | indent 4 }}
spec:
selector:
{{- include “edgex.labels” (dict “ctx” . “AppName” $.Values.edgex.redis.name) | indent 4 }}
ports:
– port: {{ .Values.edgex.redis.port }}
name: “port-{{ .Values.edgex.redis.port }}”

When the template is rendered, placeholder values will be interpolated by either properties specified in the values file or via –set flag. When helm install is executed with no -f flag, values.yaml in the root of chart is used by default. If we want to override values during installation, the property can be overridden using –set flag.

For an application as portable as EdgeX is, tying in configuration overrides can tremendously speed up deployments.

Finalizing the chart using a reference project

Up to this point, we have:

  • Created raw Kubernetes YAML files for edgex-redis and edgex-core-metadata.
  • Explained usage of yaml in Helm chart.
  • Adjusted edgex-redis Service definition file to reference Helm values and named-templates.

This, by no means is an end-to-end deployment. The patterns described above, gives you enough to apply to the remaining pieces you wish to include in your deployment. Here you can refer to the reference project which contains a finalized Helm chart responsible for Deploying: – edgex-redis – edgex-core-metadata – edgex-core-data – edgex-core-command

Following the pattern above, edgex-core-data and edgex-core-command will also require configuration files mounted as Secrets. You can create them by executing:

$ kubectl create secret edgex-core-data generic –from-file=configuration.toml=[path to edgex-core-data’s configuration.toml]

$ kubectl create secret edgex-core-command generic –from-file=configuration.toml=[path to edgex-core-command’s configuration.toml]

All of the Secrets can be accessed here in the reference project.

When your Helm chart is finalized, you can install it by executing:

# From the root of the project

$ helm install edgex –name-template edgex

In a sense, you get a free verification from the Container probes for each application. If your applications successfully start up and remain in a Running state, that is a good sign!

Each application can be accessed at [cluster IP]:[application’s node port]. For example, lets say the cluster IP is 127.0.0.1 and we want to access the /api/v1/ping endpoint of edgex-core-metadata, we can invoke the following curl request:

$ curl -i 127.0.0.1:30801/api/v1/ping

where nodePort 30801 is defined in edgex-core-metadata Service definition.

In closing

In this tutorial, we’ve only scratched the surface with respect to the potential that EdgeX Foundry has to offer. With the components we’ve deployed, the foundation is in place to continuously deploy EdgeX in Kubernetes clusters.

As a member of this amazing community I highly recommend checking out EdgeX Foundry Official documentation. From there you will have access to much more details about the platform.

As for next steps, I highly recommend connecting a Device Service to your EdgeX Core Services environment within Kubernetes. With the plug-and-play nature of the Device Sevice’s configuration, you can certainly have a device up and running within EdgeX intuitively.

This tutorial was built on a foundational project worked on at Dell. I would like to acknowledge Trevor Conn, Jeremy Phelps, Eric Cotter, and Michael Estrin at Dell for their contributions to the original project. I would also like to acknowledge the EdgeX Foundry community as a whole. With so many talented and amazing members, the EdgeX Foundry project is a great representation of the community that keeps it growing!

Visit the EdgeX Foundry website for more information or join Slack to ask questions and engage with community members. If you are not already a member of the community, it is really easy to join. Simply visit the wiki page and/or check out the EdgeX Foundry Git Hub.

Enabling a More Sustainable Energy Model with Edge Computing

By Blog, Open Glossary of Edge Computing, Trend

Written by Molly Wojcik, Chair of the State of the Edge Landscape Working Group and Marketing Director at Section

This blog previously ran on the Section website. For more content like this, click here.

The tech sector has been under mounting scrutiny over the last decade by environmental groups, businesses, and customers for its increasing energy consumption levels. Attention has recently been turning to the ways in which edge computing can help build more sustainable solutions.

The Impact of Coronavirus Lockdowns on Internet Usage

While worldwide energy consumption has significantly dropped as a result of global lockdown restrictions, Internet usage has seen a huge spike.

NETSCOUT, a provider of network and application performance monitoring tools, saw a 25-35% increase in worldwide Internet traffic patterns in mid-March, the time when the shift to remote work and online learning happened for much of the world’s population. That number has stayed pretty consistent. There has been particularly increased use of bandwidth-intensive applications like video streaming, Zoom and online gaming. The spike in Internet use has implications for the sector’s sustainability targets and makes the urgency of a call for change even louder.

Before we look specifically at sustainability in the edge computing field, let’s pull back to take a look at the wider sector.

The Energy Challenge

The information and communication technology (ICT) ecosystem accounts for over 2% of global emissions, putting it on a par with the aviation industry’s carbon footprint, at least according to a 2018 study. Did you know that data centers use an estimated 200 terawatt (Twh) hours each year? This represents around 1% of global electricity demand and data centers contribute around 0.3% to overall carbon emissions.

Where these figures could lie in the future is harder to predict. Widely cited forecasts say the ICT sector and data centers will take a larger slice of overall electricity demand. Some experts predict this could rise as high as 8% by 2030, while the most aggressive models predict that electricity usage by ICT could surpass 20% of the global total by the time a child born today hits his or her teens, with data centers using over one-third of that.

Keeping Future Energy Demand in Check

However, improvements in energy efficiency in the data center field are already making a difference. While the amount of computing performed in data centers more than quintupled between 2010 and 2018, the amount of energy consumed by data centers worldwide only rose by 6% across the same period.

The ICT sector has been hard at work to keep future energy demand in check in various ways. These include:

Streamlining Computer Processes

The shift away from legacy enterprise data centers operated by traditional enterprises such as banks, retailers, and insurance companies, to newer commercially operated cloud data centers has been making a big difference to overall energy consumption.

In a recent company blog, Urs Hölzle, Google’s senior VP technical infrastructure, pointed to a study in Science showing the gains in energy efficiency. Hölzle wrote, “a Google data center is twice as energy efficient as a typical enterprise data center. And compared with five years ago, we now deliver around seven times as much computing power with the same amount of electrical power.”

The study shows how an overall shift to hyperscale data centers has helped reduce the amount of traditional enterprise data center capacity and subsequently reduced overall energy consumption. One of the authors is Jonathan Koomey, a former Lawrence Berkeley National Laboratory scientist, who has been studying the subject of data center energy usage for over two decades. Koomey says that forecasts that focus on data growth projections alone ignore the energy efficiency gains the sector has been making.

Facebook has also been exploring ways to maximise efficiency over the last decade. The social media giant started the Open Compute Project in 2011 to share hardware and software solutions aimed at making computing more energy-efficient.

One recent novel idea is that data centers could act as energy suppliers and sell excess electricity to the grid instead of keeping it as an insurance policy. Kevin Hagen, Iron Mountain’s VP of environment, social and governance strategy, describes backup generators and UPS batteries as “useless capital.”

Instead, he asks, “What would it look like if all that money was actually invested in energy systems that we got to use when we were trying to arbitrage energy during the day and night on a regular basis? What if we share control with the utility, so they can see there’s an asset and use it back and forth?”

New Ways to Cool Data Centers

According to Global Market Insights, cooling systems represent on average 40% of entire data center energy consumption. Data center design specialists have been looking into different ways to approach reducing energy needs specifically for cooling, which have been largely approached in the same way for the last three decades. These include locating data centers in cooler areas, using AI to regulate the data center’s cooling systems to match the weather, warm-water cooling, immersion cooling, and rear door cooling systems.

It’s an important problem to be focused on since data centers worldwide contribute to industry’s consumption of 45% of all available clean water.

A Focus on Sustainables

Greenpeace has been putting data centers under the spotlight for over a decade, calling for them and other digital infrastructure to become 100% renewably powered. There has been great progress since 2010 when Greenpeace started its ClickClean campaign when IT companies were negligible contributors to renewable-power purchase agreements; today Google is the world’s largest corporate purchaser of renewable energy.

Apple was at the top of Greenpeace’s list of the top greenest tech companies last year. It already boasts 100% renewable energy to power all its global data centers, and is currently focused on cleaning up its supply chain. Greenpeace has recently called out AWS, however, for a lack of renewables in Virginia’s “data center alley”, which it says is powered by “dirty energy.”

Corporate data centers have traditionally been part of the “dirty energy” problem, but are beginning to participate in seeking out renewable energy. Some of the initiatives in this space include DigitalRealty’s announcement on using wind energy in its 13 Dallas, TX data centers and IronMountain’s Green Power Pass specifically for enterprise customers to be able to participate in renewable energy purchase for its growing number of data centers.

The Role of Edge Computing in Working Towards More Efficient Solutions

Edge computing is also increasingly being looked to as an area that can help work towards more efficient solutions. These include:

Energy Resource Efficiencies

technical paper published on Arxiv.org earlier this month lifted the hood on Autoscale, Facebook’s energy-sensitive load balancer. Autoscale reduces the number of servers that need to be on during low-traffic hours and specifically focuses on AI, which can run on smartphones in abundance and lead to decreased battery life through energy drain and performance issues.

The Autoscale technology leverages AI to enable energy-efficient inference on smartphones and other edge devices. The intention is for Autoscale to automate deployment decisions and decide whether AI should run on-device, in the cloud, or on a private cloud. Autoscale could result in both cost and efficiency savings.

Other energy resource efficiency programs are also underway in the edge computing space.

Reducing High Bandwidth Energy Consumption

Another area edge can assist in is to reduce the amount of data traversing the network. This is especially important for high-bandwidth applications like YouTube and Netflix, which have skyrocketed in recent years, and recent months in particular. This is partly due to the fact each stream is composed of a large file, but also due to how video-on-demand content is distributed in a one-to-one model. High bandwidth consumption is linked to high energy usage and high carbon emissions since it uses the network more heavily and demands greater power.

Edge computing could help optimize energy usage by reducing the amount of data traversing the network. By running applications at the user edge, data can be stored and processed close to the end user and their devices instead of relying on centralized data centers that are often hundreds of miles away. This will lead to lower latency for the end user and could lead to a significant reduction in energy consumption.

Smart Grids and Monitoring Can Lead to Better Management of Energy Consumption

Edge computing can also play a key role in being an enabler of solutions that help enterprises better monitor and manage their energy consumption. Edge compute already supports many smart grid applications, such as grid optimization and demand management. Allowing enterprises to track and monitor energy usage in real-time and visualize it through dashboards, enterprises can better manage their energy usage and put preventative measures in place to limit it where possible. This kind of real-time assessment of supply and demand can be particularly useful for managing limited renewable energy resources, such as wind and solar power.

Examples in the Wild

Schneider Electric

Schneider Electric, a specialist in energy management and automation, is beginning to see clients seek more efficient infrastructure and edge computing solutions. This includes helping organizations to digitize their manufacturing processes by using data to produce real-time feedback and alerts to gain efficiencies across the supply chain, including the reduction of waste and carbon emissions. Natalya Makarochkina, Senior VP, International, Secure Power at Schneider Electric, says edge computing is allowing this to happen in a scalable and sustainable way.

Makarochkina highlights two Schneider customers that have used edge computing to improve sustainability and make efficiency savings. The first is a specialty grocer that used edge computing to upgrade IT infrastructure and reduce its physical footprint and consumption requirements, which led to a 35% reduction in engineering cost and a 50% increase in deployment speed for the end user. The second is a co-working space provider that used Schneider’s edge infrastructure options to launch an on-site data center that offers IT infrastructure on demand, offering tenants greater operational efficiencies.

“Edge computing and hybrid cloud play a pivotal role in sustainability because they are designed specifically to put applications and data closer to devices and their users. This helps organisations to better react to changes in consumer demands and improve processes to create more sustainable products.”– Natalya Makarochkina, Senior VP, International, Secure Power at Schneider Electric

Section

At Section, we’re working to provide more accessible options for developers to build edge strategies that deliver on sustainability targets. Our patent-pending Adaptive Edge Engine technology allows developers to piece together bespoke edge networks that are optimized across various attributes, with carbon-neutral underlying infrastructure included in the portfolio of options. Other attributes include cost, performance, and security/regulatory requirements.

Similarly to Facebook’s Autoscale, Section’s Adaptive Edge Engine tunes the required compute resources at the edge in response to, not only the application’s required compute attributes (e.g. PCI compliance or carbon neutrality), but also continuously in response to traffic served and performance. Our ML forecasting engine sits behind the workload placement and decision engine which continuously places workload and scales the underlying infrastructure (up and down) so we use the least amount of compute while providing the maximum availability and performance.

Summary

Not many would argue that technology’s immersion in our day-to-day lives is only expected to expand. Technology providers must work together to create sustainable solutions to meet these growing demands. While not an end-all solution, edge computing has the potential to play a key role in helping to control the negative impacts that accompany rising energy demands.

Section is a LF Edge Member. To learn more about LF Edge or any of our members, visit https://www.lfedge.org/members/.

EdgeX Foundry Welcomes New Contributors!

By Blog, EdgeX Foundry

Written by Aaron Williams, LF Edge Developer Advocate

The EdgeX Foundry community has been blessed with a large number of active contributors over the last three years, with many of those being with us since launch.  But all communities need new members and different perspectives to continue to improve and grow.  Yet, we know that it can be intimidating to post your first contribution to an open source project.  As such, we want to take a moment and thank the following people who posted their first contribution to EdgeX Foundry in Q1 of 2020.

Q1 New Contributors:

  • jluous
  • Aricg
  • jinlinGuan
  • kaisawind
  • venkata-subbareddyK
  • kurokobo
  • venkata-lakshmi-penna
  • worldmaomao
  • DaveZLB

You can find these contributors on github and see what other projects they are working on. We encourage our new contributors to keep up the great work and we look forward to their next contribution.  You are helping to improve and grow EdgeX and our community. Thank you for all your help!

If you would like to learn more about EdgeX Foundry,  the world’s first plug and play ecosystem-enabled open platform for the IoT Edge, visit the Getting Started page. You’ll learn more about the project and get  step-by-step directions about how to start your EdgeX journey.

EdgeX Foundry has global industry support LF Edge members and open source contributors. EdgeX offers the opportunity to collaborate on IoT solutions built using existing connectivity standards combined with their own proprietary innovations.

In fact, EdgeX Foundry recently announced the sixth release in its roadmap – the Geneva release offers simplified deployment, optimized analytics, secure connectivity for multiple devices and more robust security. Learn more about the Geneva release:

Visit the EdgeX Foundry website for more information or join our Slack to ask questions and engage with community members. If you are not already a member of our community, it is really easy to join.  Simply visit our wiki page and/or check out our Git Hub and help us get to the next 6 million and more downloads!

Private LTE/5G ICN Akraino Blueprint

By Akraino Edge Stack, Blog

Written by Amar Kapadia, member of the Akraino Edge Stack community, co-founder at Aarna Networks, Inc. and an ONAP specialist. This blog originally ran on the Aarna Networks blog. You can find more content like this here

As one of the main contributors, I’m thrilled to state that the Private LTE/5G Integrated Cloud Native NFV/App Stack blueprint in LF Edge’s Akraino Edge Stack project got approved last month.

Given the opening up of unlicensed/licensed private spectrum all around the world (e.g. CBRS in the US), Private LTE/5G promises to be very exciting market. Six end users (Airbus, Globe, Orange, Tata Communications, T-Mobile, and Verizon), a number of vendors (such as us), and individuals are collaborating on this blueprint demo which will be created in a completely open manner and will contain, to the degree possible, open source components.

The key components of this blueprint are:

Private LTE/5G ICN Blueprint Software Stack

  • NFVI hardware: Standard server, switch, storage components

  • NFVI software: Kubernetes with OVN (SDN), Virtlet (to run VMs), Multus (for multiple CNI), Istio, and SD-EWAN (to connect an app across clouds). A main component in the NFVI software will also be an open source 5G UPF CNF.

  • Orchestrator: ONAP with AF integration, OpenNESS

  • Workloads (CNFs): Facebook Magma for vEPC, TIP OCN and Polaris for 5GC

  • Workloads (CNAs): We are starting with the applications in the original ICN blueprint, viz. 360° video, EdgeX Foundry, video AI/ML. However, we might change things around to collaborate with other Akraino blueprints such as the 5G MEC blueprint that is working on cloud gaming, HD video, and live broadcasting.

We will first start with Private LTE over CBRS but then quickly move over to Private 5G and edge computing.

As an open source effort, we could always use more help, Please join us if this is interesting!

The LF Edge Interactive Landscape

By Blog, Landscape, LF Edge, State of the Edge

New tool aims to help users understand and navigate the expansive edge computing ecosystem, requesting collaboration from the edge community.

Written by Molly Wojcik, Chair of the State of the Edge Landscape Working Group

A few years ago, the Cloud Native Computing Foundation (CNCF) introduced their CNCF Cloud Native Interactive Landscape, which quickly became a go-to resource for the cloud-native ecosystem. Using this as a guide and framework, the State of the Edge project has been building the LF Edge Interactive Landscape.

The LF Edge Interactive Landscape is dynamically generated from data maintained in a community-supported Github account. Based on user inputs and overseen by the State of the Edge Landscape Working Group, the map categorizes LF Edge projects alongside edge-related organizations and technologies to provide a comprehensive overview of the edge ecosystem.

The State of the Edge Landscape Working Group needs help from the larger edge community to continue to build out and improve this resource. Pull requests and issue submissions are welcome and encouraged, whether for new additions or for edits to existing listings.

How to Add a New Listing to the LF Edge Interactive Landscape

To add a new listing to the LF Edge Interactive Landscape, follow the steps using one of the options below:

Option 1: Submit a PR

  1. Visit the community Github repository at https://github.com/State-of-the-Edge/lfedge-landscape
  2. Open a pull request to add your listing to landscape.yml. Follow formatting of peer listings, making sure to include all required information and logo file:
    1. Name of organization or technology
    2. Homepage url
    3. .svg logo (Important: Only .svg formatted logos are accepted.) – see https://github.com/State-of-the-Edge/lfedge-landscape#logos for help converting/creating proper SVGs
    4. Twitter url (if applicable)
    5. Crunchbase url
    6. Assigned category (Descriptions for categories can be found in the README.md)

Full instructions available at https://github.com/State-of-the-Edge/lfedge-landscape#new-entries

Option 2: Open an issue

  1. Visit the community Github repository at https://github.com/State-of-the-Edge/lfedge-landscape
  2. Open an issue that includes all required information and logo file (reference Option 1).

Option 3: Email

  1. Email glossary-wg-landscape@lists.lfedge.org with all of the required information and logo (reference Option 1).

How to Modify a Listing in the LF Edge Interactive Landscape

To modify or make suggestions on an existing listing in the LF Edge Interactive Landscape, open an issue in the Github repository and be sure to include the following information:

  • Name of organization or technology, as listed in the landscape.
  • Detailed description of the modifications that you are requesting.

For more detailed information and instructions, you may refer to the README.md in the Github repository.

About State of the Edge

Founded in 2017, State of the Edge (recently acquired by LF Edge) provides a vendor-neutral, community-driven platform for open research on edge computing while also seeking to align the market on what edge computing truly is and what’s needed to implement it. State of the Edge publishes free research on Edge Computing, maintains the Open Glossary of Edge Computing and oversees the LF Edge Interactive Landscape. Follow State of the Edge on Twitter via @StateoftheEdge.

Molly Wojcik was recently appointed Chair of the State of the Edge Landscape Working Group. She is the Director of Education & Awareness at Section, an edge compute platform technology provider, an LF Edge member organization. Molly has been involved as an active contributor and facilitator within the Landscape working group since its beginnings with LF Edge in early 2019. If you have questions or would like to be involved int he LF Edge Landscape, feel free to email Molly at molly@section.io.

EdgeX Foundry Use Case: Wipro’s Surface Quality Inspection for a Manufacturing Plant

By Blog, EdgeX Foundry, Use Cases

Written by LF Edge members from Wipro. For more information about Wipro, visit their website.

The use case is “Automated surface quality inspection” for a manufacturing plant that produces “Piston Rods” used in different applications like Utility, Mining, Construction and Earth Moving.

In the manufacturing plant, the production of piston rods goes through multiple stages like induction hardening, friction welding, threading & polishing.  Each of these stages have a quality checkpoint to detect defects in early stages and to ensure that the rods produced are in line with design specifications & function properly.  After it goes through rigorous multi stage process, it reaches final station where surface level quality inspection happens.  At this stage, the quality inspection happens manually, requiring highly skilled & experienced human inspector to look for different types of defects like scratches, material defects & handling defects on the surface of rods.  Based on the final quality inspection results, it goes either to packaging & shipment area or to rework or scrap area.

Quality check at every stage is extremely critical to prevent quality problems down the line leading to recalls & reputational damages.  The problem with manual inspections – they are costly, time consuming and heavily dependent on human intelligence & judgement.  “Automated surface quality inspection” is an image analytics solution based on AI / ML that helps overcome these challenges.  The solution identifies and classifies defects based on image analytics to enable quality assessment and provides real-time visibility to Key Performance Indicators through dashboards to monitor plant performance.

EdgeX architectural tenets, production grade readily available edge software stack, visibility of long term support with bi-annual release roadmap and user friendly licensing for commercial deployments made us adopt EdgeX as the base software stack.

The readily available IoT gateway functionalities helped us focus more on building business application specific components than the core software stack needed for an edge gateway.  This helped us in rapid development of proof of concept for the use case we envisioned.

Key components of the solution include:

  • Edge Gateway hardware, Industrial grade camera & a sensor to detect piston rods placed in final quality inspection station
  • Software components:
    • Gateway powered by EdgeX software stack with enhancements to support the use case
    • Deep learning model trained with previously analyzed & classified defects
    • Intel’s OpenVino toolkit for maximizing inference performance on the edge gateway
    • EdgeX Device Service Layer enhancements to enable missing southbound connectivity
    • Automated decision making in the edge by leveraging EdgeX provided local analytics capability
    • EdgeX AppSDK customizations to connect to business applications hosted on cloud
    • Manufacturing Performance Dashboard to define, create and monitor Key Performance Indicators

Once the rod reaches inspection station, the gateway triggers the camera to take surface pictures.  The image captured is fed into the inference engine running on gateway, which looks for the presence of different types of surface defects.  The inference output is fed into the business application hosted on cloud to provide actionable insights with rich visual aids.

The analysis of historical data for similar pattern of defects, correlating data from OT systems with inference output for a given time period and providing feedback to OT systems for potential corrective actions are possible future enhancements.

Benefits:

  • Reduced inspection cost: Automated quality inspections, reduced need for manual inspection
  • Centralized management: Real time visibility to plant operations from remote locations
  • Consistent performance: Less dependency on human judgement, which varies from one person to another
  • Reduced inspection time: Consistent & reliable results in a much shorter time
  • Learning on the go: Continuous retraining of deep learning models make automated inspections accurate & closer to reality
  • Available 24 x 7

If you have questions or would like more information about this use case or LF Edge member Wipro, please email naga.shanmugam@wipro.com.

What is Baetyl?

By Baetyl, Blog

Baetyl is an open edge computing framework of LF Edge that extends cloud computing, data and service seamlessly to edge devices. It can provide temporary offline, low-latency computing services, and include device connect, message routing, remote synchronization, function computing, video access pre-processing, AI inference, device resources report etc.

About architecture design, Baetyl takes modularization and containerization design mode. Based on the modular design pattern, Baetyl splits the product to multiple modules, and make sure each one of them is a separate, independent module. In general, Baetyl can fully meet the conscientious needs of users to deploy on demand. Besides, Baetyl also takes containerization design mode to build images. Due to the cross-platform characteristics of docker to ensure the running environment of each operating system is consistent. In addition, Baetyl also isolates and limits the resources of containers, and allocates the CPU, memory and other resources of each running instance accurately to improve the efficiency of resource utilization.

Advantages

  • Shielding Computing Framework: Baetyl provides two official computing modules(Local Function Module and Python Runtime Module), also supports customize module(which can be written in any programming language or any machine learning framework).
  • Simplify Application Production: Baetyl combines with Cloud Management Suite of BIE and many other productions of Baidu Cloud(such as CFCInfiniteEasyEdgeTSDBIoT Visualization) to provide data calculation, storage, visible display, model training and many more abilities.
  • Service Deployment on Demand: Baetyl adopts containerization and modularization design, and each module runs independently and isolated. Developers can choose modules to deploy based on their own needs.
  • Support multiple platforms: Baetyl supports multiple hardware and software platforms, such as X86 and ARM CPU, Linux and Darwin operating systems.

Components

As an edge computing platform, Baetyl not only provides features such as underlying service management, but also provides some basic functional modules, as follows:

  • Baetyl Master is responsible for the management of service instances, such as start, stop, supervise, etc., consisting of Engine, API, Command Line. And supports two modes of running service: native process mode and docker container mode
  • The official module baetyl-agent is responsible for communication with the BIE cloud management suite, which can be used for application delivery, device information reporting, etc. Mandatory certificate authentication to ensure transmission security;
  • The official module baetyl-hub provides message subscription and publishing functions based on the MQTT protocol, and supports four access methods: TCP, SSL, WS, and WSS;
  • The official module baetyl-remote-mqtt is used to bridge two MQTT Servers for message synchronization and supports configuration of multiple message route rules. ;
  • The official module baetyl-function-manager provides computing power based on MQTT message mechanism, flexible, high availability, good scalability, and fast response;
  • The official module baetyl-function-python27 provides the Python2.7 function runtime, which can be dynamically started by baetyl-function-manager;
  • The official module baetyl-function-python36 provides the Python3.6 function runtime, which can be dynamically started by baetyl-function-manager;
  • The official module baetyl-function-node85 provides the Node 8.5 function runtime, which can be dynamically started by baetyl-function-manager;
  • SDK (Golang) can be used to develop custom modules.

Architecture

../_images/design_overview.pngArchitecture

Contributing

If you are passionate about contributing to open source community, Baetyl will provide you with both code contributions and document contributions. More details, please see: How to contribute code or document to Baetyl.

Contact us

As the first open edge computing framework in China, Baetyl aims to create a lightweight, secure, reliable and scalable edge computing community that will create a good ecological environment. In order to create a better development of Baetyl, if you have better advice about Baetyl, please contact us: