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rity","categoryPolicies":{"__typename":"CategoryPolicies","canReadNode":{"__typename":"PolicyResult","failureReason":null}}},"Conversation:conversation:2075882":{"__typename":"Conversation","id":"conversation:2075882","topic":{"__typename":"BlogTopicMessage","uid":2075882},"lastPostingActivityTime":"2023-11-12T16:11:59.854-08:00","solved":false},"User:user:332548":{"__typename":"User","uid":332548,"login":"joecoco","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-8.svg?time=0"},"id":"user:332548"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDc1ODgyLTI0NzU0N2lDREZCNUI5MjU1OEEwMTNF?revision=10\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDc1ODgyLTI0NzU0N2lDREZCNUI5MjU1OEEwMTNF?revision=10","title":"MSC17_dataCenter_016.jpg","associationType":"TEASER","width":2000,"height":1333,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDc1ODgyLTI0NzUyNWk0OUIxMTk4REZBNjlCQUZE?revision=10\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDc1ODgyLTI0NzUyNWk0OUIxMTk4REZBNjlCQUZE?revision=10","title":"EFLOWDiagram1.png","associationType":"BODY","width":1491,"height":878,"altText":null},"BlogTopicMessage:message:2075882":{"__typename":"BlogTopicMessage","subject":"Enabling Linux based Azure IoT Edge Modules on Windows IoT","conversation":{"__ref":"Conversation:conversation:2075882"},"id":"message:2075882","revisionNum":10,"uid":2075882,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:332548"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Learn more on how EFLOW provides the ability to deploy Linux IoT Edge modules onto a Windows IoT device ","introduction":"","metrics":{"__typename":"MessageMetrics","views":86495},"postTime":"2021-01-21T08:00:00.070-08:00","lastPublishTime":"2023-11-12T16:11:59.854-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" [Updated on June 24th 2021] Azure IoT Edge for Linux on Windows (a.k.a. EFLOW) is now generally available. You can read about it on this new blog post. \n   \n Today we are announcing the availability of Azure IoT Edge for Linux on Windows, also known as EFLOW, in public preview. EFLOW enables customers for the first time to run production Linux-based cloud-native workloads on Windows IoT. Customers retain their existing Windows IoT assets plus benefit from the power of Windows IoT for applications that require an interactive UX and high-performance hardware interaction. There is no longer a need to choose between Windows or Linux; customers can now leverage the best of both platforms. \n   \n Windows IoT is deployed in millions of intelligent edge solutions around the world in numerous industries including manufacturing, retail, medical equipment and public safety. Customers choose Windows to power their edge devices because it is an out of the box that provides a rich platform to create locked-down, interactive user experiences with natural input, provides world class security, enterprise grade device management, and 10 years of servicing allowing you to build a solution that is designed to last. In addition to all these features, customers also want to benefit from existing Linux workloads and leverage the advances in cloud-native development. \n   \n EFLOW provides the ability to deploy Linux IoT Edge modules onto a Windows IoT device. This opens a world of capabilities for commercial IoT as well as AI/ML with the availability of pre-built modules from the Azure Marketplace such as Live Video Analytics, SQL Edge, and OPC Publisher as a few examples. As a developer, you may also choose to implement your own custom modules using the Linux distribution of your choice to address specific business requirements. Running Linux modules on Windows IoT becomes a seamless of your solution. \n   \n In addition, Windows applications can easily interact with Linux modules running on the same physical device. A Windows process that provides UI or accesses cameras, sensors, or other hardware can seamlessly communicate with business logic or ML inferencing provided by a Linux module. \n   \n \n   \n So how does it work? \n   \n How do you run Linux IoT Edge Modules on Windows IoT devices? Windows has had a Linux kernel for years which is used in products like Windows Subsystem for Linux (WSL). Microsoft leveraged that kernel and produced a user mode to develop an internal Linux distribution, CBL-Mariner, which is open sourced and already used in the Azure Kubernetes Service on Azure Stack HCI. \n   \n The magic of EFLOW is that we are running an optimized Linux virtual machine and Azure IoT Edge, on a Windows host. This allows you to run Linux modules from the Azure marketplace or from other sources, that simultaneously communicate with existing Windows code and the Azure cloud. And the entire solution is built, validated, and maintained by Microsoft. \n   \n \n   \n The EFLOW solution includes: \n   \n Windows Admin Center ​​​​​​A ​Windows Admin Center (WAC) extension facilitates installation, configuration, and diagnostics from a remote workstation or on the local device. IT can administer the entire deployment with their existing infrastructure like any other Windows service using WAC, reducing the overall infrastructure management complexity. \n   \n Interoperability Bi-directional communication between Windows processes and Linux containers provides the best of both worlds where Windows process can provide UI or hardware proxy for business logic and ML inferencing provided by the Linux containers. ​​​​​​ \n   \n Curated Linux VM with Azure IoT Edge A Linux virtual machine, based on CBL-Mariner, is purpose-built for Azure IoT Edge, validated using an automated test pipeline, and supported as a Tier 1 environment for Azure IoT Edge workloads. \n   \n Microsoft Update Integration with Microsoft Update to keep the software up to date, including the Windows runtime components, the Mariner VM, and Azure IoT Edge. \n   \n So why does this matter? \n   \n Based on customer input, we developed EFLOW to provide an integrated end-to-end solution for workloads that normally require separate hardware and software. \n   \n Imagine a retail store with cameras recording the activity. Many network video recorders like this are powered by Windows IoT. The videos are only used when something changes or something of interest has occurred. If the store wanted to use the camera feed for local analytics or other intelligence, they may need to add a Linux box to leverage Linux AI workloads. With EFLOW, the operator can easily deploy Linux-based Azure Live Video Analytics on Windows, leveraging their existing Windows expertise and investments. They can then tie this with the new or existing ML models and have gone from having a basic video recording system to an intelligent solution that can trigger actions based on the content of the video feed. \n   \n EFLOW is available on all Hyper-V capable Windows 10 installations. This makes 100s of millions of existing devices EFLOW capable, which can easily be managed and connected through Azure. \n   \n Want to know more or where to get started? \n   \n Start by watching the IoT Show: IoT Edge for Linux on Windows 10 IoT Enterprise on Channel9. \n Detailed documentation to get started is available at https://aka.ms/AzIoTEdgeforLinuxOnWindows \n   \n Stay in touch \n   \n If you want to stay up to date and get notified of future updates to Azure IoT Edge for Linux on Windows, you can register using this link. Note that the information you will share will only be used by Microsoft for the purpose of keeping you informed about this product. \n   \n   ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"5853","kudosSumWeight":6,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDc1ODgyLTI0NzU0N2lDREZCNUI5MjU1OEEwMTNF?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0yMDc1ODgyLTI0NzUyNWk0OUIxMTk4REZBNjlCQUZE?revision=10\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:1407066":{"__typename":"Conversation","id":"conversation:1407066","topic":{"__typename":"BlogTopicMessage","uid":1407066},"lastPostingActivityTime":"2020-11-17T12:05:31.763-08:00","solved":false},"User:user:395211":{"__typename":"User","uid":395211,"login":"twarwick","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-7.svg?time=0"},"id":"user:395211"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xNDA3MDY2LTE5Mzc3MGkxNjdENzZFREM4RjVGOUZD?revision=61\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xNDA3MDY2LTE5Mzc3MGkxNjdENzZFREM4RjVGOUZD?revision=61","title":"Windows for IoT Banner.png","associationType":"TEASER","width":502,"height":186,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xNDA3MDY2LTE5MzY3MWk2QUU1QTQ2RDVCQTYxMEMy?revision=61\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xNDA3MDY2LTE5MzY3MWk2QUU1QTQ2RDVCQTYxMEMy?revision=61","title":"twarwick_0-1590088849284.png","associationType":"BODY","width":391,"height":132,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xNDA3MDY2LTE5Mzc1NGk2RDRBNjY3M0FFMDU0OEQ0?revision=61\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xNDA3MDY2LTE5Mzc1NGk2RDRBNjY3M0FFMDU0OEQ0?revision=61","title":"twarwick_1-1590098184560.png","associationType":"BODY","width":614,"height":237,"altText":null},"BlogTopicMessage:message:1407066":{"__typename":"BlogTopicMessage","subject":"Linux modules with Azure IoT Edge on Windows 10 IoT Enterprise","conversation":{"__ref":"Conversation:conversation:1407066"},"id":"message:1407066","revisionNum":61,"uid":1407066,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:395211"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n   \n   \n Have you ever wanted to run your Linux modules on Windows edge devices? ","introduction":"","metrics":{"__typename":"MessageMetrics","views":11823},"postTime":"2020-05-26T08:08:28.340-07:00","lastPublishTime":"2020-11-10T21:21:40.754-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Microsoft is enabling the ability to run Linux modules with Azure IoT Edge for Linux on Windows 10 IoT Enterprise. \n   \n This includes the ability to deploy pre-built Linux modules available in the Azure Marketplace from Microsoft as well as 3 rd party publishers in addition to custom workloads built for a specific scenario. Running Linux modules on Windows just part of the solution. \n   \n In addition, Windows processes will be able to smoothly interact with Linux modules running on the same physical device. This will allow a Windows process that may provide UI as well as act as a proxy to data access or specific hardware to be backed by business logic or data inferencing provided by a Linux module. \n   \n \n   \n This blog will allow you to experience Linux modules on Windows using Azure IoT Edge for Linux as demonstrated in the IoT Show: Linux modules on Windows 10 IoT Enterprise, as a developer.  \n Update: Nov 10, 2020 Additionally, we now have multiple sample code projects available at https://aka.ms/winiotsamples which demonstrates messaging interop between a Windows application and a Linux Edge Module running in a Linux virtual machine. Keep an eye out for our Azure IoT Edge for Linux on Windows Public Preview coming soon https://aka.ms/AzIoTEdgeforLinuxOnWindows \n    \n   \n \n   \n Additional information will be provided before 2020 end-of-year about an \"official\" offering to support Linux modules on Windows using a Linux environment provided by Microsoft specifically for running Azure IoT Edge for Linux in a production environment.  \n Recipe: Linux modules on Windows \n The following recipe will walk you through the setup and deployment of Azure IoT Edge for Linux as well as deploy one or more Linux modules from the Azure Marketplace on Windows.  \n   \n For this exercise, you may use either a physical machine running Windows 10 or a virtual machine running Windows 10.   If you choose to use a Windows 10 virtual machine for this exercise you will need to enable nested virtualization before continuing.  \n Install the Windows Subsystem for Linux with Ubuntu 18.04 LTS \n Windows Subsystem for Linux is designed to support developer workflows, however in this case we are using it to demonstrate a “Proof of Concept” for IoT workloads in production environments. \n \n Turn on Windows Subsystem for Linux optional feature \n Turn on Virtual Machine Platform optional feature \n Install Ubuntu 18.04 LTS distribution from the Microsoft Store \n Setup your new Ubuntu 18.04 LTS distribution \n Note: Username and password will accept lower case letters [a-z] and numbers [0-9] only. \n \n Set Ubuntu 18.04 LTS distribution to use WLS 2 \n \n Now type the following command into PowerShell to confirm that you are ready to go to the next step. \n   \n   \n   \n   \n wsl --list --verbose \n   \n   \n   \n   \n   \n You should see the following: \n \n Configure WSL 2 to run SystemD to support Moby \n WSL 2, which uses a custom init to support integration with multiple Linux distributions, is nearly ready to install Azure IoT Edge for Linux, but first we need to provide a systemd process management environment. Systemd is preinstalled with the Ubuntu 18.04 LTS distribution that you installed previously but it is inactive. The following steps will create a new namespace running systemd then move into that namespace so that we can install Azure IoT Edge and the Moby container engine. \n   \n CAUTION:  Invoking systemd in WSL 2 is considered an unsupported *HACK* that we are using to demonstrate a this developer scenario. This is NOT to be interpreted as an officially supported scenario and may cause odd behavior with other distributions that you may be using with WSL 2. It is also important to understand that this *HACK* is not persistent. If you reboot Windows, WSL 2 will revert to its default configuration using init. \n   \n You will enable systemd using a PowerShell administrator session. \n   \n \n Click Start, type PowerShell \n Right-click Windows PowerShell, and then click Run as administrator. \n Start WSL to use the Linux Bash command line interface \n \n   \n   \n   \n   \n wsl \n   \n   \n   \n   \n \n Installing the Daemonize tool which will allow the creation of a systemd namespace \n \n   \n   \n   \n   \n sudo apt-get update -y\nsudo apt-get install -y daemonize \n   \n   \n   \n   \n \n Create the systemd namespace using daemonize \n \n   \n   \n   \n   \n sudo daemonize /usr/bin/unshare --fork --pid --mount-proc /lib/systemd/systemd --system-unit=basic.target \n   \n   \n   \n   \n \n Enter the newly created systemd namespace \n \n   \n   \n   \n   \n exec sudo nsenter -t $(pidof systemd) -a su - $LOGNAME \n   \n   \n   \n   \n   \n Note: To return to this WSL session running systemd after a reboot or a new login, simply repeat steps 5 and 6 above.   \n   \n Deploy Azure IoT Edge with Linux modules \n Now that the WSL 2 environment is configured to allow Azure IoT Edge to install, please follow the instructions in each of the following articles to install Azure IoT Edge for Linux and deploy a Linux module from Azure IoT Hub. \n   \n IMPORTANT: Make sure that you choose the Ubuntu 18.04 or Ubuntu Server 18.04 path when there are multiple operating system choices presented so that your Azure IoT Edge runtime matches the Linux distribution installed earlier.   \n   \n \n Register an Azure IoT Edge device \n Install Azure IoT Edge runtime for Debian-based Linux Systems   \n Deploy Azure IoT Edge Linux modules from the Azure portal \n \n   \n Next Steps \n Stay tuned for a follow up to this blog in June/July 2020 which will demonstrate how you can build a solution that employs bidirectional communication between a native Windows process and a Linux module as demonstrated in the IoT Show: Linux modules on Windows 10 IoT Enterprise. 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That's how you can mitigate the negative effects of a constrained and metered connection. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":3460},"postTime":"2020-10-22T08:00:00.048-07:00","lastPublishTime":"2020-10-22T08:00:00.048-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" There are cases where IoT devices are connected to the cloud over constrained and metered connections like LPWAN, narrow-band cellular, or satellite connections. \n Although such connections are sometimes the only viable option (think of a satellite link for an off-shore vessel), they may underperform in terms of: \n \n a lower bandwidth and bitrate, leading also to an increased latency \n higher operating-costs \n data plans with lower limits on the traffic volume \n \n If not an issue with simple MCU-based IoT devices sending few data-points per day, those constraints may be a major concern or even a showstopper in the case of CPU-based devices, running a full-fledged Operating System (OS) along with the services and workloads required by the specific use case. The overall traffic in the latter case is significantly bigger, and it includes a variety of data-flows with different characteristics and requirements in terms of frequency, traffic volume and latency. Just to mention a few: \n \n telemetry (which is a continuous stream, with a low to high traffic volume depending on the specific use case and the latency is usually not critical although important) \n high-priority events like alarms (random events, low volume but latency is critical) \n bulk uploads like file storage and DB syncs, OS and SW updates, container images pull (on a periodic basis or on-demand, usually very high volume, latency is not relevant at all) \n \n   \n \n   \n Transporting those data-flows over a constrained and metered connection poses several risks: the traffic volume can easily exceed the limit and increase the operating costs, and the low bandwidth makes it challenging to meeting the latency requirements of high-priority messages like alarms. \n Possible mitigations of those risks are: \n \n reducing the traffic volume, in order to comply with data plan limits, to reduce the operating costs, and to reduce the bandwidth footprint \n prioritizing data-flows, in order to comply with the latency requirements \n \n Let’s see how to implement such mitigation strategies. \n   \n Reducing the traffic volume \n Edge Computing is a valid option to reduce the overall traffic volume. It enables processing and analyzing the raw data close to its source, and transferring only the insights over the connection. Edge Computing adds several other benefits as well, like low-latency control at the edge, increased security/reliability through decentralization, privacy and compliance. There are various platforms available to implement edge computing securely and in a way that allows control of the workloads from the cloud. Azure IoT Edge is a good example of such platforms. \n   \n Prioritizing data-flows \n In addition to reducing the traffic volume, we need also a way to prioritize data-flows and control the latency. \n Some Edge Computing platforms may embed a mechanism to assign priorities to the data-flows they process. A good example is the “Priority Queues” mechanism implemented in the IoT Edge v1.0.10 and announced in this blog post. Each stream uses a dedicated queue, with individual settings in terms of time-to-live and priority over other streams. That would be used, for instance, to ensure that an alarm is delivered before other telemetry messages already in the queue. \n However, such a mechanism cannot be applied to bulk upload of raw data, or to any other data-flow which is out of the scope of edge computing (like OS/SW updates, container image pulls, …). The prioritization of such data-flows needs a different approach. \n   \n An easy but effective solution would be to limit the bandwidth that they are allowed to use. That would prevent an OS update or a file upload from saturating the whole available bandwidth, with the risk of interfering with other streams (telemetry, alarms) or even stopping them. \n   \n Bandwidth Shaping via Linux Traffic Control \n On a Linux system, the kernel's traffic control subsystem is an effective and quite versatile way of shaping any TCP/IP data-flow. \n Such Traffic Control subsystem embeds a packet scheduler and configurable \"queueing disciplines\" which allow for: \n \n limiting the bandwidth (with advanced filtering on networks, hosts, ports, directions, ...) \n simulating delays, packet loss, corruption, and duplicates (useful to investigate the impact of connection’s constraints on performance and reliability) \n \n The Traffic Control utility, aka 'tc', is a command-line tool that gives you full access to the kernel packet scheduler. It is pre-installed in many distros (ex. Ubuntu) or you can easily install it if missing. In case of Debian for instance: \n   \n   \n sudo apt-get install iproute2 \n   \n   \n You can start experimenting with the tc in a shell and create and apply rules that will limit the bandwidth of a service, or simulate packet loss/corruption and network delays. For instance, the following will limit the egress bandwidth of the network interface 'eth0' to 1Mbps: \n   \n   \n tc qdisc add dev eth0 root tbf rate 1mbit burst 32kbit \n   \n   \n You can find more examples here. \n Try also tc-config, which is a tc command wrapper (developed in Python) with simpler and more intuitive syntax and parameters. \n With a device running Linux, you could remotely SSH onto it and use command lines to configure tc, but that's not a scalable method if you are dealing with many devices in a production environment. If you are using Azure IoT Edge for edge compute, we have created an open-source module that will help you. With this module, you will be able to configure traffic control through the module twin like you would for any other IoT Edge modules. \n The traffic-control-edge-module (github here) is a sample IoT Edge module wrapping the tc-config command-line tool to perform bandwidth limitation and traffic shaping within an IoT Edge device via Module Twins. \n Here's an example showing how you would apply a 50Kbps bandwidth limitation to the data-flow associated to a bulk upload originated by the edge Blob Storage module named \"myBlobStorage\" running in the IoT Edge device: \n   \n \n The module implements also some simple logic to listen to containers lifecycle events, to detect whether a container has been (re)started and to (re)apply the related rules if any. \n   \n Check out the repository for the traffic-control-edge-module (github here) and do not hesitate to let us know what you think, file issues, and contribute 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