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End User Case Study: Monitoring industrial equipment using EdgeX Foundry

By | Blog, EdgeX Foundry

By Jason Shepherd, LF Edge Governing Board member and IoT and Edge Computing CTO, Dell Technologies

Founded in 1996, Technotects is an IoT technology consulting firm with broad domain expertise in industrial use cases. When one of their industrial equipment OEM customers independently realized the power of the EdgeX Foundry framework, Technotects planned and executed a Proof-of-Concept (PoC) with one of their customer’s typical process skid use cases.

This blog walks through the successful PoC, including what the solution entailed and Technotects’ experience working with the EdgeX platform. We’re seeing more and more of these types of real-world applications go public in the community on the heels of the EdgeX Foundry “1.0” Edinburgh release.

The use case

The use case for the PoC is monitoring sensor and pump information from a process skid used in a variety of applications, including agricultural, flavors and fragrances, pharmaceutical, petro-chem and food/beverage industries. Technotects’ goal of the effort was to prove that the EdgeX Foundry platform, combined with commercial value-add from the ecosystem, can provide an Internet of Things (IoT) solution stack that can address the unique interoperability challenges that industrial applications present, such as a mix of connectivity protocols and near real-time I/O processing, all while giving them the freedom of choice by being decoupled from proprietary, single-vendor solutions. In turn, this would allow their customers to improve their overall solution architectures, reduce runtime royalties and accelerate their time-to-market.

EdgeX-enabled Dell gateway utilized in the Technotects PoC

Technotects’ initial interest in EdgeX Foundry was the flexibility offered by the open ecosystem and the potential of reducing excessive runtime licensing fees per deployed host node, based on the combination of a proprietary edge application framework, edge historian and both southbound and northbound connectivity. In addition, they were attracted to the ability to make build-or-buy decisions with EdgeX without being locked into any specific choice for connectivity or applications value-add services.

The PoC basics

For the PoC, Technotects leveraged a Dell Edge Gateway 3002, Photon OS and VMware Pulse IoT Center, Edge Xpert from IOTech, RedisEdge from Redis Labs, Project Iris from RSA Labs, both AWS and Azure cloud platforms (hosting Redis for data backup) and a custom-built edge management console. Refer to the figure below for a block diagram of the setup and read on for more detail on how the effort came together.

IIoT Edge Stack Block Diagram for the Technotects PoC

Solution deep dive

For the EdgeX foundation of the stack, Technotects chose to work with IOTech’s Edge Xpert offering – a commercially-supported variant of the open source code that is available from the project GitHub within the Linux Foundation. Their use of Edge Xpert enabled them to focus on the integration with their customer’s preferred value-add software components rather than dealing with the open source code. They found IOTech’s documentation to be clear and the initial installation to be quick and straightforward – benefits when using a commercial variant that has additional hardening and packaging. Of course, using a commercially-supported variant versus simply downloading the open source code is a matter of personal preference.

EdgeX is completely neutral to OS, underlying hardware, protocol and programming language, and for this PoC, Technotects chose to leverage the Dell Edge Gateway 3002 and both Ubuntu from Canonical and Photon OS. Photon OS is an open source, container-optimized Linux distribution that has been nested within VMware’s vSphere offering for some time. Technotects was able to run their console, communication drivers, Edge Xpert and all the other referenced value-add software components in Docker containers in both operating systems, all without issue. They find that having the flexibility to deploy on any combination of hardware (x86 or ARM) and operating systems (Linux or Windows) in the field depending on customer need is valuable.

For southbound connectivity, Technotects leveraged a hybrid model. The process skids leverage a programmable logic controller for process control and for this PoC, Technotects used a commercially available Ethernet/IP driver to communicate with it. In turn, they connected this off-the-shelf, licensed driver package into the OPC-UA Device Service native to EdgeX. To connect to other devices on the skid, Technotects used the Modbus TCP protocol from IOTech’s Edge Xpert offer, written using the native EdgeX Device Service SDK. With the plug-in Device Service model, any combination of devices and protocols can be readily added in the future as needs evolve.

The solution architecture is a great example of both 1) how existing connectivity stacks can be used alongside native EdgeX Device Services in a hybrid model and 2) that in the EdgeX model, even connectivity written with the open EdgeX Device Service SDK can be monetized. Commercially-supported variants of EdgeX Device Services are likely to be attractive to end users with mission-critical use cases that involve bespoke and/or proprietary protocols in that support for this southbound connectivity often requires institutional knowledge gleaned from reverse engineering.

Meanwhile, end users can benefit from a growing number of open source Device Service options available within the community and increasingly Device Services that are supported by sensor makers for greenfield applications, coming with the sensor just like a keyboard comes with a driver for a PC. (Side note: There are numerous additional opportunities in development in the EdgeX ecosystem, although I must be sensitive to NDAs until they’re made publicly available). Net-net, the value of the open, vendor-neutral EdgeX ecosystem is in providing developers and end users with choices based on whatever is most valuable for their business.

Technotects leveraged VMware’s Pulse IoT Center to manage and monitor the underlying gateway hardware, OS and the EdgeX application framework above. VMware Pulse is a massively scalable, platform and application-independent solution for onboarding, managing, securing and monitoring IoT devices and gateways. System update campaigns can be applied in bulk and admins are alerted to any issues with their devices deployed out in the field, all in real-time. While VMware Pulse can be used standalone with its embedded device agent, it’s especially powerful when used in concert with the EdgeX framework. Any preferred console to manage applications and the underlying host system can be used with the EdgeX framework, with application-level functionality being enhanced by taking advantage of the EdgeX System Management Agent (SMA).

Technotects found the northbound connectivity to both Azure and AWS available in IOTech’s Edge Xpert to be very easy to configure. This highlights a key benefit of the EdgeX framework – decoupling investments in southbound data ingestion from any given cloud to enable choice over the long term, including realizing true-multitenancy from the edge. With the addition of support for multiple application services in the recent 1.0 Edinburgh release, data related to infrastructure monitoring and management can be sent to their management console of choice (in this case VMware’s Pulse IoT Center), whereas data related to the process for data analytics and taking action can be sent to any combination of on-prem or cloud-based application stacks of choice.

For local data persistence, Technotects chose RedisEdge over the MongoDB reference database that has been the baseline in the project until the recent 1.0 Edinburgh release. Technotects found it very easy to replace MongoDB with RedisEdge with no functionality differences, thanks to the work of Redis Labs in terms of making it an available plug-in within the EdgeX ecosystem. This is yet another example of how EdgeX is truly open and vendor-neutral, enabling users to leverage any enhancing functionality of their choice.

Finally, the PoC explored RSA Labs’ Project Iris active threat monitoring solution. Iris is a container that plugs into the EdgeX framework (and any other stack that supports containers) to profile baseline behavior for the stack and connected devices and then uses machine learning to detect anomalies. In turn Iris create alerts linked back to RSA’s popular Netwitness offering.

Conclusion

In closing, Technotects found EdgeX Foundry easy to work with and was able to successfully replicate their customer’s use case for process skid monitoring by leveraging the commercially supported framework from IOTech and value-add from Canonical, Dell, Redis, RSA and VMware. The flexibility to simply plug value-add into the open, vendor-neutral EdgeX foundation will provide both Technotects and their customers with more options in the future and help mitigate lock-in to proprietary edge platforms.

The EdgeX project has matured over the past two years to the current 1.0 state, and if you’re one of the thousands of end users that has been quietly prototyping with the platform, we welcome you to come forward and share your story on the project website through a blog or simple testimonial statement. The more people that come forward to share their success stories, the faster EdgeX will become the de-facto standard interoperability framework for the IoT Edge and the more we can all focus on innovation rather than reinvention!

Thank you for your time, and now’s your time to download the code or contact any of the providers in the ecosystem and build something great! Perhaps, as many have, even start your own business model around the EdgeX framework – just think of what Android did for scaling out an application and services ecosystem for mobile devices.

f you have questions or comments, visit the EdgeX Foundry Slack Channel and share your thoughts in the #community channel. Or, join the LF Edge Slack Channel and share your thoughts in the #EdgeX channel.

An Akraino Developer Use Case

By | Akraino, Akraino Edge Stack, Blog

Written by technical members members of the Akraino Edge Stack project including Bruce Lin, Rutgers; Robert Qiu, Tencent; Hechun Zhang, Baidu; Tina Tsou, Arm; Ciprian Barbu, Enea; Allen Chen, Tencent; Gabriel Yang, Huawei; Feng Yang, Tencent; Jeremy Liu, Tencent ; Tapio Tallgren, Nokia; Cristina Pauna, Enea

More intelligent edge nodes are deployed in 5G era. This blog shares a developer use case out of the Akraino project that the community is working on, specifically Telco  Appliance Blueprint Family, Connected Vehicle Blueprint, ELIOT: Edge Lightweight and IoT Blueprint Family, Micro-MEC, The AI Edge Blueprint Family, and 5G MEC/Slice System to Support Cloud Gaming, HD Video and Live Broadcasting Blueprint.

Telco Appliance Blueprint Family

Telco Appliance blueprint family provides a reusable set of modules that will be used to create sibling blueprints for other purpose tuned appliances.

Appliance model automates the installation, configuration and testing of:

  • Firmware and/or BIOS/UEFI
  • Base Operating System
  • Components for managing containers, performance, fault, logging, networking, CPU

The first blueprint integrated with TA is REC. Other appliances will be created by combining other applications with the same underlying components to create additional blueprints.

TA produces an ISO that, after being installed in the cluster, provides the underlying tools needed by the applications that come on top of it. The build of this ISO is automated in Akraino’s CI and it includes:

  • Build-tools: Based on OpenStack Disk Image Builder
  • Dracut: Tool for building ISO images for CentOS
  • RPM Builder: Common code for creating RPM packages
  • Specs: the build specification for each RPM package
  • Dockerfiles: the build specifications for each Docker container
  • Unit files: the systemd configuration for starting/stopping services
  • Ansible playbooks: Configuration of all the various components
  • Test automation framework

The image provides the following components, used during installation:

  • L3 Deployer: an OpenStack Ironic-based hardware manager framework
  • Hardware Detector: Used to adapt L3 deployer to specific hardware
  • North-bound REST API framework: For creating/extending blueprint APIs
  • CLI interface
  • AAA server to manage cloud infrastructure users and their roles
  • Configuration management
  • Container image registry
  • Security hardening configuration
  • A distributed lock management framework
  • Remote Installer: Docker image used by Regional Controller to launch deployer

The image provides the following components, usable by the applications:

  • CPU Pooler: Open Source Nokia project for K8s CPU management
  • DANM: Open Source Nokia project for K8s network management
  • Flannel: K8s networking component
  • Helm: K8s package manager
  • etcd: K8s distributed key-value store
  • kubedns: K8s DNS
  • Kubernetes
  • Fluentd: Logging service
  • Elasticsearch: Logging service
  • Prometheus: Performance measurement service
  • OpenStack Swift: Used for container image storage
  • Ceph: Distributed block storage
  • NTP: Network Time Protocol
  • MariaDB, Galera: Database for OpenStack components
  • RabbitMQ: Message Queue for Openstack components
  • Python Peewee: A Python ORM
  • Redis

Radio Edge Cloud (REC)

Akraino Radio Edge Cloud (REC) provides an appliance tuned to support the O-RAN Alliance and O-RAN Software Community‘s Radio Access Network Intelligent Controller (RIC) and is the first example of the Telco Appliance blueprint family.

  • RIC on Kubernetes on “bare metal” tuned for low latency round trip messaging between RIC and eNodeB/gNodeB,
  • Support for telco networking requirements such as SRIOV, dual POD interfaces, IPVLAN
  • Built from reusable components of the “Telco Appliance” blueprint family
  • Automated Continuous Deployment pipeline testing the full software stack (bottom to top, from firmware up to and including application) simultaneously on chassis based extended environmental range servers and commodity datacenter servers
  • Integrated with Regional Controller (Akraino Feature Project) for “zero touch” deployment of REC to edge sites
  • Deployable to multiple hardware models

In Akraino, the work on this blueprint will fully automate the deployment and testing on multiple hardware platforms in a Continuous Deployment system.

SDN Enabled Broadband Access (SEBA):

Here is SEBA user story.

As a Service Provider, I want to setup SEBA environment so that I can use ONF SEBA platform.

As an Administrator, I want to validate HOST OS environment so that I can install Kubernetes/SEBA.

As an Administrator, I want to validate Software environment so that I can install Kubernetes/SEBA

As an Administrator, I want to validate Virtual Machines for my Kubernetes/SEBA environment so that I can validate environment

As a Administrator, I want to validate services for my Kubernetes/SEBA environment so that validate working environment

SEBA validation on ARM – IEC Type 2

SEBA stands for SDN Enabled Broadband Access. It is an ONF/Opencord project which implements a lightweight platform, based on R-CORD. It supports a multitude of virtualized access technologies at the edge of the carrier network, including PON, G.Fast, and eventually DOCSIS and more.

SEBA is also an Akraino Blueprint, but in the context of the IEC Blueprint, the SEBA usecase is deployed and verified on an IEC Type 2 platform. IEC is also the main Akraino sub-project/blueprint which enables ARM architectures for Telco/Enterprise Edge applications.

For the purpose of validating SEBA on IEC Type 2, a SEBA compliant ARM64 lab has been setup at the Akraino Community Lab at the University of New Hampshire (UNH). The lab consists of two development PODs, using Marvel ThunderX2 networking processor equipped servers and Ampere HR330A servers.

The connectivity is provided by Edgecore 7816-64X TOR switches which fulfill the role of Fabric Switches in the SEBA Architecture.

For the validation work we have first ported and deployed BBSim on IEC for ARM64 and right now there is ongoing work for porting and verifying PONSim.

PONSim is also a central piece of SEBA-in-a-Box (SIAB) which is one of the main testing CI/CD projects in upstream Opencord Jenkins infrastructure.

There is ongoing work in Akraino IEC for replicating the SIAB setup by utilizing the same testing environments and facilities provided by Opencord.

Finally, recent collaboration with Opencord resulted in forming a Multiarch Brigade with the purpose of making SEBA seamlessly work on multiple architectures apart from the x86 servers. So far there has been some evident interest from the community and other parties for enabling multiarch support and perpetual verification in a CI/CD manner by adapting the Opencord infrastructure to support other architectures and ARM64 in particular.

Connected Vehicle Blueprint

Connected Vehicle Blueprint focuses on establishing an open source MEC platform, which is the backbone for V2X application.

From use cases perspectives,  the following are the use cases we have tested in our companies. More is possible in the future.

  • Accurate Location: Accuracy of location improved by over 10 times than today’s GPS system.Today’s GPS system is around 5-10 meters away from your reallocation, <1 meter is possible with the help of Connected Vehicle Blueprint.
  • Smarter Navigation: Real-time traffic information update, reduce the latency from minutes to seconds, figure out the most efficient way for drivers.
  • Safe Drive Improvement: Figure out the potential risks which can NOT be seen by the driver.
  • Reduce traffic violation: Let the driver understand the traffic rules in some specific area.For instance, change the line prior to narrow street, avoiding opposite way drive in the one way road, avoiding carpool lane when single driver etc.

From technology architecture perspective,  the blueprint consists of four major layers.

  • Hardware Layer: Connected Vehicle Blueprint runs on top of community hardware. Both Arm and x86 server are well supported.
  • IaaS Layer: Connected Vehicle Blueprint can be deployed in virtual environment as well.  Virtual Machine , Container as well as other IaaS mainstream software(like openstake, kubernates et al)  are supported.
  • PaaS Layer:Tars is the microservice framework of Connected Vehicle Blueprint. Tars can provide high performance RPC call, deploy microservice in larger scale-out scenario efficiently, provide easy-to-understand service monitor feature.
  • SaaS Layer: The latest v2x application depicted in upper use cases perspective.

From network architecture perspective,  the blueprint can be deployed in both 4G and 5G network. Two key points should be paid special attention to. One is offloading data to edge MEC platform.  The policy of data offload is configurable based on different applications.  The other is the ability that letting the edge talks to the other edges as well as remote data center. In some use cases, date process in one edge can NOT address the application’s requirements. We need to collect the data from different edges and figure out a “conclusion” for the application.

ELIOT: Edge Lightweight and IoT Blueprint Family

ELIOT AIoT in Smart Office Blueprint

This blueprint provides edge computing solutions for smart office scenarios. It manages intelligent devices, delivers AI training models and configures rules engine through cloud-edge  collaboration.

Services provided by cloud side:

  • Devices management
  • AI models training and deliver
  • Rules engine configuration
  • Third-party applications

Services provided by edge side:

  • Sending/Receiving devices data
  • Data analysis
  • AI models/Functions execution

Deployment environment:

  • Kubernetes
  • Container
  • VM
  • Bare Metal

Recently, we are still developing this project and Tencent will keep devoting great efforts to the research and development of this blueprint. In the near future, the project will also be open source. Any partners interested in this project are welcome to contact us and let’s work together to promote the application of edge computing in the field of smart office.

The AI Edge Blueprint Family

With this blueprint, teachers and school authorities could conduct a full evaluation of the overall class and the concentration of individual students, helping to fully understand the real time teaching situation. According to the concentration data of each course, teachers and school authorities can conduct knowledge test and strengthen.

The AI Edge Blueprint mainly focuses on establishing an open source MEC platform combined with AI capacities at the Edge, which could be used for safety, security, and surveillance sectors.

5G MEC/Slice System to Support Cloud Gaming, HD Video and Live Broadcasting Blueprint.

The purpose of the blueprint is to offer a 5G MEC networking platform to support delay sensitive, bandwidth consuming services, such as cloud gaming, HD video and live broadcasting. In addition, network slicing will be utilized to guarantee excellent user experience.

As a service provider of cloud gaming, I want to set up a computing platform to execute game rendering as well as video encoding, and deliver the compressed video to the player via a 4G/5G network.

As cloud gaming is sensitive to latency, the computing platform is required to be as close as possible to the network edge. Besides that, a mechanism to discover the service and direct players’ traffic to the platform is demanded.

To direct players’ traffic to the platform, I install an edge connector which is responsible for enabling flexible traffic offloading by interacting with EPC or 5GC, and subscribing the edge slice, which provisions a certain level of QoS between UE and edge application.

To interface with the user plane of 4G or 5G, I install an edge gateway (GW). In 5G SA, the edge GW is connected to a UPF, to manage the traffic from the mobile network. In 5G NSA or 4G, the edge GW is connected to an eNB or SGW, with an additional function of handling GTP-U, the tunneling protocol adopted by 4G and 5G.

When a player accesses a cloud gaming service hosted on the platform, the edge connector will establish an appropriate route between the mobile network and the edge GW. The compressed video and the player’s control data will be transferred between the gaming server and the user equipment, traversing the edge GW.

As a service provider of live broadcasting or HD video delivery, I want to set up a platform to transcode the video source to fit the bandwidth of the transport.

Similar to the cloud gaming use case, an edge connector is installed to direct the traffic to the computing platform, and an edge GW is installed to manage the mobile traffic and terminate GTP-U under 4G or 5G NSA. The computing platform will transfer the format of the video to be adapted to either the wireless link towards the mobile user or the transport towards the internet. The computing platform may be connected to a CDN to optimize the live broadcasting or HD video delivery.

Micro-MEC (µMEC)

 µMEC is a low-power, low-cost server at the edge of the network that supports Smart City use cases with different sensors (including cameras), is connected to Internet and telco networks, and allows third-party developers to create ETSI MEC applications to it.

So the µMEC focuses on Smart City use cases. If you had different smart sensors in a city, collecting data that is available for free, what could you make possible? That was the challenge that we gave in a developer hackathon in May. We used the Akraino uMEC platform and created a model city. Different sensors collected information and made it available through a database.

 

For the next hackathon, we want to make it very easy for developers to create applications that run on the µMEC device itself. For this, we are implementing ETSI MEC compliant interfaces and leverage the OpenFAAS serverless platform.

The µMEC platform that we are developing in Akraino will have a trusted computing stack, from trusted hardware to secure networking and signed containers.

Akraino will be hosting a MEC Hackathon on November 18 at Qualcomm. For more information or to register, visit the Akraino MEC Hackathon wiki.  For more informations about Akraino or blueprints, click here.

Running K8s Conformance Tests in Akraino Edge Stack

By | Akraino, Blog

By Cristina Pauna, Bin Lu, Howard Zhang

Due to the large scale f its projects and various application scenarios, Akraino Edge Stack is complex when it comes to testing logistics;  normal integration or unit tests are not enough for testing the projects. To ensure that the full deployment is both functional and reliable, it needs to be tested at all layers of the stack, from hardware to application. To verify deployments based on Kubernetes at the platform layer, K8s conformance is a good way to do this job.

The k8s conformance tests are used in the CNCF vendor  Certified Kubernetes program. It integrates end-to-end (e2e) testing in Kubernetes which contains a wide range of test cases, and passing them proves a high level of common functionality of the platform.

K8s Conformance Tests in Akraino

General testing in Akraino is done using the Blueprint Validation Framework. The framework provides tests at different layers of the stack, such as hardware, operating system, cloud infrastructure, security, etc. Each layer has its own container image built by the validation project. The full list of images provided can be found in the project’s DockerHub repo.

The Kubernetes conformance tests are included in the k8s layer. The test suit can be run in a couple of ways, either by directly calling the e2e tool or though sonobuoy, which is a diagnostic tool that gathers logs and and stats in one archive. Regardless of how the tests are ran, it is possible to customise what test cases are included, which versions and even the container images within the tooling.

In Akraino, the conformance suite is run from a separate container, using the validation image built for the k8s layer: akraino/validation:k8s-latest. This image contains all of the tooling necessary for running tests on the k8s layer in Akraino, as well as the tests themselves. The tools that go into the image can be found in its Dockerfile. The container is designed to be started on a remote host that has access to the k8s cluster. Check the validation project User guide for more information on the setup.

The conformance suite is customized using the sonobuoy.yaml file. Some of the images used by sonobuoy in Akraino are built by the validation project in order to customize them to fit the community’s needs (e.g. akraino/validation:sonobuoy-plugin-systemd-logs-latest is based on the official gcr.io/heptio-images/sonobuoy-plugin-systemd-logs:latest but with added aarch64 support).

Prerequisites

Before running the image, copy the folder ~/.kube from your Kubernetes master node to a local folder (e.g. /home/ubuntu/kube) that will later be mounted in the container. Optionally, the results folder can be mounted in order to have the logs stored on the local server.

Run the tests

The Akraino validation project has a variety of tests for the k8s layer. To run just the conformance tests, follow the steps below:

  1. Clone the validation repo.
  2. Create a customized blueprint that will be passed to the container. The blueprint will contain just the conformance suite in it, like in the example below.

3. Fill in volumes.yaml file with the data that applies to your setup. The volumes that don’t have a local value set will be ignored. Do not modify the target part of each volume. These paths will be automatically created inside the container when started, and are fixed paths that the tools inside the container expect to exist as is.

In the example below, only the following volumes are set:

  • Location to the kube config files needed for access to the cluster
  • Location to the customized blueprint file
  • Location to where to store the results

4.  Run the tests

To run the tests, the blucon.py command is used which needs the name of the blueprint as input. The full suite takes around one hour to run, depending on the setup.

The sonobuoy containers can be seen running on the cluster on the heptio-sonobuoy namespace.

Get the results

The results are stored inside the container in the /opt/akraino/results folder. If a volume was mounted to this folder (e.g. /home/ubuntu/results) then they can be viewed from the host where the test was ran.

The results can be viewed with a browser, using the report.html file that the robot framework provides. In case of failures, the sonobuoy archive can be used to inspect all the logs.

 

EdgeX Foundry Contributors and Users Share Thoughts on the Edinburgh Release, the Growing Ecosystem and more!

By | Blog, EdgeX Foundry

Last month, EdgeX Foundry released its production ready Edinburgh release, which provides an open platform for IoT edge computing to a growing global ecosystem for a range of vertical markets including enterprise, industrial, retail and consumer. The fourth release in the EdgeX roadmap, the EdgeX Edinburgh release supports complementary products and services such as commercial support, training and customer pilot programs created by LF Edge members and contributors.

Now that EdgeX is “ready for primetime,” learn more about what contributors and users of the EdgeX Foundry platform had to say about the new release.

According to AWS, AWS, Dell Technologies, and the open-source EdgeX Foundry have collaborated to create IoT edge interoperability and simplify integration across inherently heterogeneous solution stacks. AWS customers can now seamlessly deploy AWS IoT Greengrass to the IoT edge within the EdgeX framework. The EdgeX framework enables data ingestion across connected devices and data sources for AWS IoT Greengrass and AWS IoT Core. This is further enhanced with plug-in value-add from the growing EdgeX and LF Edge ecosystems.

“AMD processors deliver the performance, advanced security features, and scalability to support the most demanding edge computing requirements, while providing breakthrough processing agility for x86-based IoT infrastructure. As a founding member of EdgeX Foundry, AMD continues to support a powerful and open ecosystem for interoperable IoT and edge compute solutions, that leverage the robust, scalable and open EdgeX framework.” – said Amey Deosthali, Director of Embedded Channel Applications, AMD

“HP joined the LF Edge project to help facilitate collaboration among industry-leading vendors and partners to solve common business problems. We are happy to see the Edinburgh production release by EdgeX Foundry to help support that objective. This release delivers commercial viability with increased stability and connectivity across a wide range of standards to reduce costs, complexity, and time to deploy. We look forward to seeing innovations at the edge that accelerate deployment of comprehensive retail use cases.” – Aaron Weiss, Vice President and General Manager, Retail Solutions, HP Inc.

“With the EdgeX Foundry Edinburgh Release, industrial IoT companies can easily integrate Mocana’s TrustCenter™ and TrustPoint™ cyber protection software solution to ensure the safety and reliability of industrial gateways and controllers. Mocana’s FIPS 140-2 validated device security software and security management platform enable companies to build and operate tamper-resistant products that protect the device, data, applications and communications. EdgeX and Mocana together help manufacturers and operators to accelerate compliance, ensure privacy, and protect the most complex industrial systems that run microservices, containerized applications, and hardware-based secure elements, such as the Trusted Platform Module (TPM).” – Keao Caindec, Head of Cyber Protection & Privacy, Mocana

“The production-ready EdgeX Foundry Edinburgh release provides important new capabilities for edge computing nodes.  The availability of an open, vendor-neutral source framework for connecting IoT devices to applications and cloud computing facilities is critical to the success of IoT and Edge processing efforts across multiple industries.” – Sam Fuller, Director of Strategy, NXP Semiconductors

EdgeX Foundry Hackathon

The EdgeX Foundry community is planning a series of hackathons focused on addressing real-world use cases with solutions built from commercial content from sponsors unified by the EdgeX framework.

The inaugural EdgeX hackathon will be hosted at Tech Nexus in Chicago on October 7 and 8. The event will focus on the retail market and work stemmed from the Commerce (e.g. Retail) Working Group within the EdgeX Foundry project and the related Open Retail Initiative (ORI).

Participating developers (from students to retail end users) will use their talents and creativity combined with the EdgeX framework, commercial content from sponsoring companies and the rules of the event to develop a solution for either one of the specified customer-valued retail use-cases or an additional open category.

Details about the competition, link to register and more will be available in the next few weeks. Stay tuned here for more.

Other supportive quotes:

“EdgeX Foundry is the key component of Beechwoods IoT gateway solution that allows our customers to engage confidently in edge computing technology. With the Edinburgh release, this solution will be ready to transition from customer engagement to product deployment.” – Brad Kemp, President, Beechwoods Software

“The Edinburgh release of EdgeX Foundry brings much needed standardization and stability for edge computing in production environments through an open source, common framework. The availability of the EdgeX Foundry snap enables developers an easy path to getting started with EdgeX Foundry, and benefit from confinement, easy integration into their own infrastructure, and automatic updates. In addition, this release introduces new device snaps providing integration with MQTT and ModBus.”- Loic Minier, IoT Field Engineering Director, Canonical

“As EdgeX Foundry reaches maturity with the Edinburgh release, CloudPlugs is excited to also announce the integration of the CloudPlugs IIoT platform with the open EdgeX ecosystem.  CloudPlugs IoT is a robust backend to deploy, orchestrate and manage EdgeX-compliant devices and micro service-based applications, as well as to manage and visualize field data. The EdgeX framework provides new levels of flexibility in field-level interoperability and the combination of EdgeX with CloudPlugs IoT delivers a powerful, end-to-end software and service stack to digitize assets and to deploy commercial and industrial IoT solutions at scale.” – Jimmy Garcia-Meza, CEO, CloudPLugs Inc. 

“Having started the EdgeX movement with a small team at Dell before contributing the code to the Linux Foundation, it’s certainly amazing to see the traction we’ve gotten through open, vendor neutral collaboration in a few short years. It’s a testament to the power of the network effect in the open source community which ultimately enables developers to focus on value rather than reinvention.” – Jason Shepherd, former chair of the EdgeX Foundry Governing Board and IoT and Edge CTO, Dell Technologies

“EdgeX Foundry provides an important software platform standardizing on the south bound IoT device connectivity and northbound data storage connectivity and allows vendors to plug in their core IoT capabilities in between. FogHorn is aligned with this data ingestion and publication standardization and will continue to collaborate as appropriate.” – Sastry Malladi, CTO, FogHorn

“The EdgeX platform offers HMS Networks a path to quickly build Industrial IoT solutions by providing predefined set of services for I/O functionality. HMS has created a J1939 service for EdgeX platform to help simplify IoT solutions for the commercial vehicle telemetry market. Ultimately, the EdgeX platform will significantly reduce the R&D investment required to create a majority of the Industrial IoT applications required in the market today.” – Tom McKinney, Director Engineering Services and Business Development, HMS Networks

“EdgeX Foundry is an important project arriving at the right time. It promises to connect devices to capabilities, and then get out of the way so you can run containerized workloads to generate insights, run model scoring, or detect anomalies… all at the edge. IBM is collaborating with EdgeX Foundry as part of our hybrid cloud strategy to help enterprises unlock the value of data from on-premises to the cloud to the edge.” – David Boloker, Distinguished Engineer, IBM

“EdgeX Foundry’s open source platform enables the industrial software ecosystem to integrate rapidly with ioTium’s managed services converged infrastructure offering – it’s microservices framework with open APIs is a powerful driver in the fragmented Industrial Control Systems market. ioTium enables rapid scalable deployment of the EdgeX Foundry framework globally.”- Ron Victor, CEO, ioTium 

“EdgeX Foundry provides an open framework for ease of design, development, & deployment at the Edge, while addressing stringent security,  privacy & compliance requirements. NetFoundry added its vendor-agnostic, connectivity-as-code solution to  EdgeX in order to enable developers and integrators to get similar ease of use, security and performance for their northbound application connectivity to core, clouds and service meshes. With the release of the EdgeX Edinburgh release, the EdgeX Foundry developer community has all the tools needed to deliver on market needs and ensure secure, agile innovation at the Edge” – Galeal Zino, CEO, NetFoundry Inc.

“As Digital Transformation for IoT gathers momentum, companies are demanding the same reliability, performance and security at the edge as they are used to getting from their Cloud Computing stack. With this release, EdgeX with Redis Labs RedisEdge not only delivers upon those expectations, but provides an ecosystem of open source technologies and plug-ins such as Redis Modules that help developers innovate.” – Dave Nielsen, Head of Community and Ecosystem Programs, Redis Labs

“EdgeX Foundry addresses the problem of the license stack at the IoT Edge constantly increasing in cost by providing a well architected, high performance, open source platform that can be used for industrial solutions today.” Mike Malone, Vice President, Technotects, Inc.

“EdgeX Foundry’s global community ecosystem has experienced explosive growth, and the tangible advances delivered in the EdgeX  Edinburgh release are exciting developments for edge computing. We fully support EdgeX Foundry’s goals to establish an open interoperable framework for edge computing to provide developers with increased control over how, when, where and with whom they run their applications and manage their data. We look forward to continuing our contributions to the EdgeX Foundry community and related efforts in fostering open industry-wide innovation such as the Open Retail initiative.” – Mimi Spier, Vice President, Edge and IoT Business, VMware

“As a founding member of LF Edge, Wipro is proud to have contributed to the Edinburgh release. We will continue to actively participate as it is a key platform for delivering open, microservices-based, edge IoT applications for today’s interoperable distributed enterprise world.” – Andrew Aitken, general manager and global open source practice leader, Wipro Limited.

“ZEDEDA’s vision is to free cloud-native and legacy apps to run on any edge device anywhere in the world. This vision drives our support for EdgeX Foundry and its mission of promoting open interoperability between edge devices. We’ve made our virtualization solutions compatible with EdgeX releases because we believe they will have a central role in our industry’s future.” – Joel Vincent, VP Marketing, ZEDEDA

To learn more about the EdgeX Foundry Edinburgh release:

Building Automation: A Sweet Spot for EdgeX Foundry

By | Blog, EdgeX Foundry

Written by Andy Foster, active member of the EdgeX Foundry technical community and Product Director at IOTech

The key objective for LF Edge’s EdgeX Foundry is to create a flexible and open edge computing IoT platform that can support a range of different vertical use cases across markets such as manufacturing, oil & gas and smart energy. A key vertical market that has emerged for the community and IOTech, in particular, has been involved in multiple projects is Building Automation.

Why is Building Automation proving to be such a sweet spot for EdgeX? In our experience, the requirements of the next generation of Building Automation applications align perfectly with the capabilities of EdgeX.  In particular, a modern building is a sophisticated and heterogeneous environment consisting of diverse subsystems including lighting, HVAC and access control. Data can be generated from multiple sources using a range of different device/sensor technologies and protocols (e.g. Modbus, BACnet, DALI, MQTT and others).

The sensor data must be normalized by the IoT platform running on an Edge node e.g. an industrial Gateway or on-premise server. The data can then be fed into the building management analytics which can make smart control decisions in “near” real-time on how to automatically optimize the building environment for the benefit of the users and also the building owners.  As an open source project, EdgeX Foundry (can support any “Southbound” OT protocol or “Northbound” Cloud endpoint) and its platform independence (not tied to a specific operating system or hardware/silicon) is key to its suitability for Building Automation use cases. Most importantly, EdgeX also provides “out-of-the-box” connectivity for a number of the protocols most commonly required in a smart building system. As of the most recent EdgeX Edinburgh release the list includes Modbus, BACNet, MQTT and OPC UA Device Services.

The below video showcases the demo created by IOTech on behalf of the Linux Foundation to highlight the key capabilities that make EdgeX a great choice of Edge IoT Platform for use in Building Automation applications. It shows the integration of multiple devices commonly found in commercial buildings (light and temperature sensors, HVAC controller, DALI lighting controllers, power meter and access control system). Based on occupancy data, the Edge Analytics automatically regulates the building environment, controlling the light levels and HVAC settings in multiple independent zones based on the ambient light and temperature readings from each zone. Overall power consumption of the building is also monitored and key data streams are exported to a Big Data application running on AWS Cloud.

For more information about EdgeX Foundry, visit the website here. To learn more about the other edge computing projects under the LF Edge umbrella, visit the website here.

Using DockerHub in Akraino Edge Stack & Other Linux Foundation Projects

By | Akraino, Blog

By Kaly Xin, Eric Ball,  and Cristina Pauna

DockerHub is the world’s largest library and community for container images. It offers a huge repository for storing container images and it is available world wide. It can automatically build container images from GitHub and Bitbucket and push them to Docker Hub. These are just a few of the features it provides, but maybe one of the best features is that it offers seamless support for multi arch images through fat manifest.

Why Docker Hub is recommended for Multi-Arch

Docker Hub registry is able to store manifest list (or fat manifests). A manifest list acts as a pointer to other images built for a specific architecture thus making it possible to use the same name for images that are built on hardware with different architectures.

Figure 1: Docker registry storing amd64, arm64 images and their fat manifest

In the picture above akraino/validation:k8s-latest is the fat manifest, and its name can be used to reference both images akraino/validation:k8s-amd64-latest and akraino/validation:k8s-arm64-latest. Inspecting the manifest offers the details on what images it has, for what hardware architecture and what OS.

Figure 2: Docker fat manifest details

How does it work?

When building an image for a specific arch, the arch is added in the tag of the image (akraino/validation:k8s-amd64-latest and akraino/validation:k8s-arm64-latest).

After the images are pushed in the Docker Hub repo, the manifest can be created from the two images. Its name will be the same as the two images but with the arch removed from the tag (akraino/validation:k8s-latest).

To do this in CI with Jenkins, the Jenkins slave has to have docker and a couple of other LF tools installed. The connection to Docker Hub is done through LF scripts (see releng-global-jjb for more info) and all you need to do is define the jjb jobs .

The Akraino validation project is already pushing to Dockerhub, so if you would like to check out some template code, take a look at ci-management/jjb/validation. The docker images are pushed in the official repo and the docker build jobs are running daily. 

In the figure below, the main Jenkins job (validation-master-docker) triggers two parallel jobs that build and push into the Docker Hub registry the amd64 (akraino/validation:k8s-amd64-latest ) and arm64 (akraino/validation:k8s-arm64-latest ) images.  At the end, the fat manifest (akraino/validation:k8s-latest ) is done in a separate job. 

When pulling the image, the name of the manifest is used  (akraino/validation:k8s-latest); the correct image will be pulled based on the architecture of the host from which the pull is made.

Figure 4: Pulling a docker image from two different hardware architecture servers using the same name

What’s next

Docker Hub has been integrated in LF projects like OPNFV from the beginning and is now integrated in Akraino too, so other open source projects can refer to this successful experience to integrate Docker Hub in their pipeline.

What to Expect from the Open Glossary of Edge Computing in 2019

By | Blog, Open Glossary of Edge Computing

By Alex Marcham, LF Edge and State of the Edge Contributor and Technical Marketing Manager at Vapor IO, and Matt Trifiro, Co-Chair at State of the Edge; Chair at Open Glossary of Edge Computing and CMO at Vapor IO

The Open Glossary of Edge Computing began as a utilitarian appendix to the 2018 State of the Edge report. It had humble goals: to cut through the morass of vendor- and pundit-driven definitions around edge computing and, instead, deliver a crisply-defined common lexicon that would enhance understanding and accelerate conversations around all things edge.

Very quickly, it became clear that the Open Glossary could be a powerful and unifying force in the fledgling world of edge computing and that no one entity should own it. Instead, everybody should own it. Thus began our partnership with The Linux Foundation. We converted the glossary into a GitHub repo, placed it under a Creative Commons license, and created an open source project around it.

In January 2019, the Open Glossary became one of the founding projects of LF Edge, the Linux Foundation’s umbrella group for edge computing projects. The Open Glossary now plays a critical role that spans all of the LF Edge projects with its mission to collaborate around a single point of reference for edge computing terminology. This community-driven lexicon helps mitigate confusing marketing buzzwords by offering a foundation of clear and well-understood words and phrases that can be used by everyone.

In 2019, the momentum of the Open Glossary project will continue unabated. Open Glossary has four main projects this year, building on its successes in 2018:

Grow the Community

The Open Glossary project depends on open collaboration, and the community is actively building engagement by seeking out participation from key stakeholder groups in edge computing. Within The Linux Foundation itself, the Open Glossary team has sought input from not only all of the LF Edge projects but also adjacent projects, such as the CNCF’s Kubernetes IoT and Edge working group. In addition, the Open Glossary has formed alliances with other edge computing groups and foundations, including the TIA, iMasons, and the Open19 Foundation. Partnering with other non-profits and consortia will help the project grow its base of passionate collaborators who are dedicated to expanding and improving the Open Glossary.

The Taxonomy Project

Readers of the Open Glossary want more than mere definitions; they want to know how the constituent parts fit together as a whole. To answer this request, the Open Glossary has begun the Taxonomy Project, a working group that seeks to create a classification system for edge computing across three core areas:

  • Edge infrastructure
  • Edge devices
  • Edge software

The Taxonomy Project will draw upon the expertise of subject matter experts in each of the core areas to define and hopefully also visualize the complex relationships between the different components in edge computing. The Taxonomy Project WG is being led by Alex Marcham and the Open Glossary will publish the first taxonomies in Q3 2019.

The Edge Computing Landscape Map

At the request of The Linux Foundation, the State of the Edge project also contributed its Edge Computing Landscape Map, which is now a working group under the auspices of the Open Glossary. The Landscape WG is being led by Wesley Reisz and has just started out. Currently, they have regular weekly meetings (Tuesdays at noon Pacific Time) and the group is actively working on refining the categories the LF Edge Landscape. The group seeks wider participation and will be asking for help to test and validate the proposed categories.

You can see the landscape map evolve at https://landscape.lfedge.org and join the mailing list here.

Glossary 2.0

Edge computing is a rapidly evolving area of development, deployment and discussion, and the Open Glossary contributors aim to keep the glossary up to date with the latest developments in industry and academia, driven by contributions from our project members. During 2019, the Open Glossary team aims to release its 2.0 version of the Open Glossary, which will be a timely update that continues to form the basis for clear and concise communication on the edge.

To see open issues and pull requests or to make contributions, visit the GitHub repo. Go here to join the mailing list.

In Summary

2019 is shaping up to be a pivotal year for edge technologies, their proponents and of course their end users. The Open Glossary project, as part of The Linux Foundation, is dedicated to continuing its unique mission of bringing a single, open and definitive lingua franca to the world of edge computing. We hope you’ll join us during 2019 as we focus on these goals for the project, and look forward to your contributions.

Contributors can get involved with the Open Glossary project by joining the mailing lists and contributing and commenting on the GitHub repository for the project.

Alex Marcham is a technical marketing manager at Vapor IO. He is one of the primary contributors to the Open Glossary and leads the Taxonomy Project working group. Matt Trifiro is CMO of Vapor IO and is the Chair of the Open Glossary project.

Your Path to Edge Computing: Akraino Edge Stack’s Release 1

By | Akraino, Blog

By Kandan Kathirvel, Akraino Edge Stack TSC-Chair and Tina Tsou, Akraino Edge Stack TSC Co-Chair

The Akraino community was proud to announce the availability of its release 1 on June 6th. The community has experienced extremely rapid growth over the past year, in terms of both membership and community activity: Akraino includes broad contributions from across LF Edge, with 60% of LF Edge’s 60+ members contributing to project, as well as several other developers across the globe.

Before Akraino, developers had to download multiple open source software packages and integrate/test on deployable hardware, which prolonged innovation and increased cost. The Akraino community came up with a brilliant way to solve this integration challenge with the Blueprint model.

An Akraino Blueprint is not just a diagram; it’s real code that brings everything together so users can download and deploy the edge stack in their own environment to address a specific edge use case. Example use cases include IoT gateway, MEC for connected car, and a RAN intelligent controller that enables 5G infrastructure.

The Blueprints address interoperability, packaging, and testing under open standards, which reduces both overall deployment costs and integration time by users. The Akraino community will supply Blueprints across the LF Edge portfolio of projects, with plans to address 5G, IoT and a range of other edge use cases.

The key strength of the Akraino community is the well-defined process to welcome new Blueprints, new members, users and developers. The technical community is comprised of a Technical Steering committee (TSC), which consists of representatives from across member companies. The TSC acts as a “watchdog” to set process, monitor the community, and ensure open collaboration. In addition to the TSC, the Akraino community has seven sub-committees focused on much-needed areas such as security, edge APIs, CI and validation labs, upstream collaborations, documentation, process and community. Regular meetings are scheduled to ensure broader collaboration and accelerate progress on the various projects. The community calendar can be found here. It is not necessary to be a member to join the community calls, we invite anyone interested in learning more to join!

The above picture illustrates the primary use of Akraino R1 Blueprints and its targeted deployment areas. The release 1 Blueprints cover everything from a larger deployment in a telco-based edge cloud to a smaller deployment, such as in a public building like a stadium. Each Blueprint is validated via community standards on real physical lab hardware, hosted by either the community or the users.

Akraino Edge Stack prides itself on continuous refinement and development to ensure the success of Blueprints and projects. The community is already planning R2, which will include both new Blueprints and enhancements to existing Blueprints, tools for automated Blueprint validations, defined edge API’s, new community lab hardware, and much more. For future events and meetings please visit: https://wiki.akraino.org/display/AK/Akraino+TSC+Group+Calendar.

 

Hello System Management!

By | Blog, EdgeX Foundry

Written by Akram Ahmad, EdgeX Foundry contributor and Principal Software Engineer at Dell Technologies

For those of you not yet familiar with the canonical way of introducing new technology-centric stuff, at least the way we do it in the world of computer programming–and thinking here specifically to the “Hello World!” first-ever program introduced to the world by programming legends Kernighan and Ritchie with their C programming language–please allow me to clarify what may be an admittedly enigmatic title we’ve got for this blog post. Essentially, it was with the EdgeX Foundry Delhi Release that the team had the pleasure of introducing EdgeX System Management capability to the world! Hence, “Hello System Management!” (More on the Edinburgh Release in just a bit.)

It’s my ongoing privilege to be a part of helping design, implement, and shepherd System Management (or “SM” for short) to date, and going forward. With that in mind, I would like to give you a flavor of the capabilities that SM brings to the table.

You can think of the System Management Agent (SMA), in particular, as a brand-new service which serves as the coordinator for control plane information (i.e. status, configuration, and metrics for EdgeX services). The SMA also control actions on EdgeX services (i.e. starting, stopping, and restarting services). Cloud or third-party systems can, in turn, call on the API provided by the SMA to trigger the actions or to get the control plane data they need. In a nutshell, the SMA can serve as a one-stop shop for managing a deployed instance of EdgeX.

Each EdgeX micro service has a corresponding management API that the SMA calls on to help control that service (e.g. to stop the service) or fetch its latest configuration or metrics. The SMA, along with the management API provided by each service, will be expanded in future releases of EdgeX and will one day offer control plane data and actions via alternate protocols (for example via the well-known protocol SNMP that is part of the TCP/IP suite that powers the Internet as we know it today).

I invite you to hold on to the thought that, for the constellation of services that will be offered via EdgeX, there needs to be “controller” of sorts…

Now, let’s turn to the truism that an IoT platform like EdgeX is used to collect the data from “Things.” Put another way, the platform ingests data that is physically sensed from IoT sensors and devices. Work associated with collecting, managing, and disbursing sensed data is exactly the kind of work associated with a “data plane.” On the other hand, the kind of work associated with operating and managing the IoT platform software and infrastructure is best described as “control plane” operations.

This includes getting the IoT platform and infrastructure running (or shutdown), configuring the platform software for the particular use case, and understanding the health and status of the software platform (is it running and what type of resources is the IoT software platform using?). Analysis of any control plane data may be used to take action as well, but action revolves around the IoT platform itself–not the sensed or controlled world. For example, in the control plane, it may be determined that a service needs to be restarted because it is consuming too much memory.

This is where the SMA comes in!

The System Management (SM) service will assist in protecting EdgeX and reducing the surface area of an API attack. Rather than opening up access to all services to the central management system, the SM service serves as a single point proxy to the control plane for all of EdgeX services for the central management system. The SMA, therefore, reduces the number of access points to EdgeX and reduces potential security vulnerabilities. It also allows the central management system to be loosely coupled to all of EdgeX—requiring the central management system to again have just one access address (the address of the SM service) that it needs to know about for any EdgeX deployment.

Before digging deeper, let’s recap what we’ve learned so far: System Management (SM) functionality, as determined by the EdgeX community, is generally associated with control plane data and operations.  The control plane (and System Management) is about managing the IoT platform and infrastructure. The data plane is all about managing and understanding the physical world that the IoT platform is there to observe and control. Think about it: Whether one is talking about towering skyscrapers or flimsy tents rigged on the grounds of a park, there remains, as ever, the crucial need for control. Without coordination, things can get chaotic in a heartbeat.

Also, and crucially, SM is also about providing information—having retrieved that information in the first place—about the status of the services it manages. Eventually, building on this capability, SM will provide the means to reconfigure the services themselves. At this time, with the Edinburgh Release, SM can provide performance and memory metrics for requested services. Likewise, SM can provide detailed configuration information for the services requested by users of SM, as well as the health status of those services (whether given services are up or down.)

In other words, while control is a critical capability, SM is about more than just control. By the same token, we want to make it abundantly clear that we are building System Management (SM) capability to facilitate other central systems, and not be those central systems. In a nutshell, EdgeX SM is about helping promote interoperability—in this case, allowing you to manage EdgeX with your choice in central management system.

Let’s shift gears a bit now: When you look at a typical fog deployment, a larger management system will want to manage the control plane of the edge systems as well as all the intermediate and upper level nodes and resources of the overall deployment. Just as there is a management system to control all the nodes and infrastructure within a cloud data center, and across cloud data centers, so too there will likely be management systems that will manage and control all the nodes (from edge to cloud) and infrastructure of a complete fog or IoT deployment.

If you will be so kind as to allow me the use of just one more metaphor, it will be this one: Think to a team of workhorses ploughing the land (EdgeX services). Then think to the driver (System Management). Finally, and without going too crazy about the farming metaphor—all metaphors, including this one, can carry only so much water—I invite you to imagine two scenarios (1) First, the one without the other, and (2) Second, the two (i.e. the team of workhorses and the driver) working in unison. If you associated chaos with the first scenario, and clockwork unison with the second, you are in good company.

So with the Edinburgh Release, we will continue building SM capability to facilitate other central systems. Again, the goal is not to be those central systems, but rather to facilitate those systems. May your System Management (SM) learnings continue, and may the community be the better for it!

If you have questions or comments, visit the EdgeX Foundry Slack Channel and share your thoughts in the #community channel. Or, join the LF Edge Slack Channel and share your thoughts in the #EdgeX channel.

Project EVE Code Now Available

By | Blog, Project EVE

Project EVE (Edge Virtualization Engine), part of LF Edge since the organization’s inception, earlier this week marked an important milestone: the official handover of code from ZEDEDA. EVE provides an open standard for edge virtualization, helping make it as easy and secure to manage applications on edge devices as it is in the cloud. With EVE, enterprises can run a wide variety of applications on any edge-class gateway while enjoying the benefits of data center virtualization, like zero-touch provisioning and secure, one-click software update rollouts at IoT scale.

“Project EVE’s release under LF Edge is an important milestone for the edge computing industry,” said Melissa Evers-Hood, senior director of Google Operating Systems for Intel System Software Products, and chair of the LF Edge Governing Board. “An open approach to virtualization can help companies address the growth in diverse services and hardware configurations being deployed at the edge. Using virtualization to consolidate workloads provides companies with a more flexible and elastic infrastructure, allowing them to secure and manage these services while containing costs.”

Project EVE  allows applications ranging from legacy software programs running in virtual machines (VMs) to the latest microservices architectures to operate in a secure and reliable way on smaller edge devices. This is accomplished through the use of a type-1 hypervisor, an Edge Container runtime, and a hardened root-of-trust implementation, enabling workloads to run in either a VM or standard container environment. By decoupling software from hardware, EVE also allows for multi-tenant deployments that can operate in complete isolation from each other, increasing security and decreasing complexity.

Key features of Project EVE include:

  • Compatibility with all major edge hardware and cloud providers—no vendor lock-in
  • Ability to support any application that can run in a VM or standard container
  • Simplified application management through standardized APIs
  • Smarter hardware usage through coordinated resource allocation and partitioning
  • Ability to create a zero-trust approach to security, leveraging a hardened root-of-trust implementation

As the number of IoT devices continues to skyrocket, it’s becoming more and more important for businesses to be able to process, analyze, and act on sensor data in real time via local edge gateway systems. Project EVE provides a key component of the technology stack needed for powerful computing at the edge. By contributing the code for Project EVE to LF Edge, ZEDEDA is furthering the organization’s mission to create an open framework for edge computing.

For more information about Project EVE, visit https://www.lfedge.org/projects/eve/.