Merge pull request #58118 from MicrosoftDocs/release-accelerators-edge-rm

Release accelerators edge rm

Author: huypub

articles/iot-accelerators/about-iot-accelerators.md

@@ -48,7 +48,7 @@ You can use the connected factory dashboard to monitor and manage your industria
 
 ### Predictive Maintenance
 
-Use this solution accelerator to predict when a remote device is expected to fail so you can carry out maintenance before the predicted failure happens. This solution accelerator uses machine learning algorithms to predict failures from device telemetry. Example devices might be airplane engines or elevators.
+Use this solution accelerator to predict when a remote device is expected to fail so you can carry out maintenance before the device fails. This solution accelerator uses machine learning algorithms to predict failures from device telemetry. Example devices might be airplane engines or elevators.
 
 You can use the predictive maintenance dashboard to view predictive maintenance analytics:
 
@@ -95,7 +95,7 @@ You can deploy the Remote Monitoring solution accelerator in the following confi
 * **Basic:** Reduced cost version for a demonstration or to test a deployment. All the microservices deploy to a single Azure virtual machine.
 * **Local:** Local machine deployment for testing and development. This approach deploys the microservices to a local Docker container and connects to IoT Hub, Azure Cosmos DB, and Azure storage services in the cloud.
 
-The cost of running a solution accelerator is an aggregate of the [cost of the underlying Azure services](https://azure.microsoft.com/pricing). You see details of the Azure services used when you choose your deployment options.
+The cost of running a solution accelerator is the combined [cost of running the underlying Azure services](https://azure.microsoft.com/pricing). You see details of the Azure services used when you choose your deployment options.
 
 ## Next steps
 

articles/iot-accelerators/iot-accelerators-remote-monitoring-add-edge-device.md

@@ -0,0 +1,52 @@
+---
+title: Remote Monitoring solution add Edge device - Azure | Microsoft Docs 
+description: This article describes how to add an IoT Edge device to a Remote Monitoring solution accelerator
+author: dominicbetts
+manager: timlt
+ms.author: dobett
+ms.service: iot-accelerators
+services: iot-accelerators
+ms.date: 10/09/2018
+ms.topic: conceptual
+
+# As an operator in the Remote Monitoring solution accelerator, I want add an IoT Edge deice to the solution so that I can receive telemetry from the device
+---
+
+# Add an IoT Edge device to your Remote Monitoring solution accelerator
+
+To add an [IoT Edge](../iot-edge/about-iot-edge.md) device to your solution accelerator, complete the following two steps:
+
+1. Add the Edge device on the **Devices** page in the Remote Monitoring solution accelerator web UI.
+1. Install the IoT Edge runtime on your Edge device.
+
+## Add the IoT Edge device
+
+To add an IoT Edge device to the Remote Monitoring solution accelerator, navigate to the **Devices** page in the web UI and click **+ New device**.
+
+In the **New device** panel, choose **IoT Edge device**. You can leave the default values for the other settings. Then click **Apply**:
+
+![Add IoT Edge device](media/iot-accelerators-remote-monitoring-add-edge-device/addedgedevice.png)
+
+### Alternative ways to add an IoT Edge device
+
+It's also possible to register an IoT Edge device directly with the IoT Hub instance in your solution accelerator. You need to know the name of the IoT hub in your solution accelerator before you follow any of these how-to guides:
+
+- [Register a new Azure IoT Edge device from the Azure portal](../iot-edge/how-to-register-device-portal.md)
+- [Register a new Azure IoT Edge device with Azure CLI](../iot-edge/how-to-register-device-cli.md)
+- [Register a new Azure IoT Edge device from Visual Studio Code](../iot-edge/how-to-register-device-vscode.md)
+
+When you register a device directly with the IoT hub in the Remote Monitoring solution accelerator, it's listed on the **Devices** page in the web UI.
+
+## Install the IoT Edge runtime
+
+Before you can deploy modules to your Edge device, you must install the IoT Edge runtime on the physical device. The following how-to guides show you how to install the runtime on common device platforms:
+
+- [Install the Azure IoT Edge runtime on Linux (x64)](../iot-edge/how-to-install-iot-edge-linux.md)
+- [Install Azure IoT Edge runtime on Linux (ARM32v7/armhf)](../iot-edge/how-to-install-iot-edge-linux-arm.md)
+- [Install Azure IoT Edge runtime on Windows to use with Windows containers](../iot-edge/how-to-install-iot-edge-windows-with-windows.md)
+- [Install the Azure IoT Edge runtime on Windows to use with Linux containers](../iot-edge/how-to-install-iot-edge-windows-with-linux.md)
+- [Install the IoT Edge runtime on Windows IoT Core](../iot-edge/how-to-install-iot-core.md)
+
+## Next steps
+
+Now that you have prepared your IoT Edge device, the next step is to deploy modules to it. See [Import an IoT Edge package into your Remote Monitoring solution accelerator](iot-accelerators-remote-monitoring-import-edge-package.md)

articles/iot-accelerators/iot-accelerators-remote-monitoring-architectural-choices.md

@@ -12,44 +12,72 @@ ms.topic: conceptual
 
 # Remote Monitoring architectural choices
 
-The Azure IoT Remote Monitoring solution accelerator is an open-source, MIT licensed, solution accelerator that introduces common IoT scenarios such as device connectivity, device management, and stream processing, so customers can speed up their development process.  The Remote Monitoring solution follows the recommended Azure IoT reference architecture published [here](https://aka.ms/iotrefarchitecture).  
+The Azure IoT Remote Monitoring solution accelerator is an open-source, MIT licensed, solution accelerator. To help you speed up your IoT development process, it shows common IoT scenarios such as:
 
-This article describes the architectural and technical choices made in each of the subsystems for the Remote Monitoring solution, and discusses alternatives considered.  It is important to note that the technical choices made in the Remote Monitoring solution are not the only way to implement a remote monitoring IoT solution.  The technical implementation is a baseline for building a successful application and should be modified to fit the skills, experience, and vertical application needs for a customer solution implementation.
+- Device connectivity
+- Device management
+- Stream processing
+
+The Remote Monitoring solution follows the recommended [Azure IoT reference architecture](https://aka.ms/iotrefarchitecture).
+
+This article describes the architectural and technical choices made, and the alternatives considered, in each of the Remote Monitoring subsystems. However, the technical choices Microsoft made in the Remote Monitoring solution aren't the only way to implement a remote monitoring IoT solution. You should regard the technical implementation as a baseline for building a successful application and you should modify it to:
+
+- Fit the available skills and experience in your organization.
+- Meet your vertical application needs.
 
 ## Architectural choices
 
-### Microservices, serverless, and cloud native
+The architecture that Microsoft recommends for an IoT application is cloud native, microservice, and serverless based. You should build the different subsystems of an IoT application as discrete services that you can deploy and scale independently. These attributes enable greater scale, more flexibility in updating individual subsystems, and provide the flexibility to choose an appropriate technology for each subsystem.
+
+You can implement microservices using more than one technology. For example, you could choose either of the following options to implement a microservice:
 
-The architecture we recommend for IoT applications are cloud native, microservice, and serverless based.  The different subsystems of an IoT application should be built as discrete services that are independently deployable, and able to scale independently.  These attributes enable greater scale, more flexibility in updating individual subsystems, and provide the flexibility to choose appropriate technology on a per subsystem basis.  Microservices can be implemented with multiple technologies. For example, using container technology such as Docker with serverless technology such as Azure Functions, or hosting microservices in PaaS services such as Azure App Services.
+- Use a container technology such as Docker with serverless technology such as Azure Functions.
+- Host your microservices in PaaS services such as Azure App Services.
 
-## Core subsystem technology choices
+## Technology choices
 
 This section details the technology choices made in the Remote Monitoring solution for each of the core subsystems.
 
-![Core Diagram](./media/iot-accelerators-remote-monitoring-architectural-choices/subsystem.png) 
+![Core Diagram](./media/iot-accelerators-remote-monitoring-architectural-choices/subsystem.png)
 
 ### Cloud Gateway
-The Azure IoT Hub is used as the Remote Monitoring solution cloud gateway.  The IoT Hub offers secure, bi-directional communication with devices. You can learn more about IoT Hub [here](https://azure.microsoft.com/services/iot-hub/). For IoT device connectivity, the .NET Core and Java IoT Hub SDKs are used.  The SDKs offer wrappers around the IoT Hub REST API and handle scenarios such as retries.
+
+Azure IoT Hub is used as the Remote Monitoring solution cloud gateway. [IoT Hub](https://azure.microsoft.com/services/iot-hub/) offers secure, bi-directional communication with devices.
+
+For IoT device connectivity, you can use:
+
+- The [IoT Hub device SDKs](../iot-hub/iot-hub-devguide-sdks.md#azure-iot-device-sdks) to implement a native client application for your device. The SDKs offer wrappers around the IoT Hub REST API and handle scenarios such as retries.
+- The integration with Azure IoT Edge in the solution accelerator to deploy and manage custom modules running in containers on your devices.
 
 ### Stream processing
-For stream processing the Remote Monitoring solution uses Azure Stream Analytics for complex rule processing.  For customers wanting simpler rules, we also have a custom microservice with support for processing of simple rules, although this set-up not part of the out of the box deployment. The reference architecture recommends use of Azure Functions for simple rule processing and Azure Stream Analytics (ASA) for complex rule processing.  
+
+For stream processing, the Remote Monitoring solution uses Azure Stream Analytics for complex rule processing. If you want to use simpler rules, there's a custom microservice with support for simple rule processing, although this set-up not part of the out-of-the-box deployment. The reference architecture recommends Azure Functions for simple rule processing and Azure Stream Analytics for complex rule processing.
 
 ### Storage
-For storage, the Remote Monitoring solution accelerator uses both Azure Time Series Insights and Azure Cosmos DB. Azure Time Series Insights stores the messages coming through IoT Hub from your connected devices. The solution accelerator uses Azure Cosmos DB for all other storage such as cold storage, rules definitions, alarms, and configuration settings. Azure Cosmos DB is the recommended general-purpose warm storage solution for IoT applications though solutions such as Azure Time Series Insights and Azure Data Lake are appropriate for many use cases. With Azure Time Series Insights you can gain deeper insights into your time-series sensor data by spotting trends and anomalies, which allows you to conduct root-cause analyses and avoid costly downtime. 
+
+For storage, the Remote Monitoring solution accelerator uses both Azure Time Series Insights and Azure Cosmos DB. Azure Time Series Insights stores the messages coming through IoT Hub from your connected devices. The solution accelerator uses Azure Cosmos DB for all other storage such as cold storage, rules definitions, alarms, and configuration settings.
+
+Azure Cosmos DB is the recommended general-purpose warm storage solution for IoT applications though solutions such as Azure Time Series Insights and Azure Data Lake are appropriate for many use cases. With Azure Time Series Insights, you can gain deeper insights into your time-series sensor data by spotting trends and anomalies. This feature lets you conduct root-cause analyses and avoid costly downtime.
 
 > [!NOTE]
 > Time Series Insights is not currently available in the Azure China cloud. New Remote Monitoring solution accelerator deployments in the Azure China cloud use Cosmos DB for all storage.
 
 ### Business integration
-Business integration in the Remote Monitoring solution is limited to generation of alarms, which are placed in warm storage. Further business integrations can be performed by integrating the solution with Azure Logic Apps.
+
+Business integration in the Remote Monitoring solution is limited to the generation of alarms, which are placed in warm storage. Connect the solution with Azure Logic Apps to implement deeper business integration scenarios.
 
 ### User Interface
-The web UI is built with JavaScript React.  React offers a commonly used industry web UI framework and is similar to other popular frameworks such as Angular.  
+
+The web UI is built with JavaScript React. React offers a commonly used industry web UI framework and is similar to other popular frameworks such as Angular.
 
 ### Runtime and orchestration
-The application runtime chosen for subsystem implementation in the Remote Monitoring solution is Docker containers with Kubernetes as the orchestrator for horizontal scale.  This architecture allows for individual scale definition per subsystem however incurs DevOps costs in keeping VMs and containers up-to-date from a security perspective.  Alternatives to Docker and Kubernetes include hosting microservices in PaaS services (for example, Azure App Service) or using Service Fabric, DCOS, Swarm, etc. as an orchestrator.
+
+The Remote Monitoring solution uses Docker containers to run the subsystems with Kubernetes as the orchestrator for horizontal scale. This architecture enables individual scale definitions for each subsystem. However, this architecture does incur DevOps costs to keep the virtual machines and containers up-to-date and secure.
+
+Alternatives to Docker include hosting microservices in PaaS services such as Azure App Service. Alternatives to Kubernetes include orchestrators such as Service Fabric, DC/OS, or Swarm.
 
 ## Next steps
+
 * Deploy your Remote Monitoring solution [here](https://www.azureiotsolutions.com/).
 * Explore GitHub code in [C#](https://github.com/Azure/azure-iot-pcs-remote-monitoring-dotnet/) and [Java](https://github.com/Azure/azure-iot-pcs-remote-monitoring-java/).  
 * Learn more about the IoT Reference Architecture [here](https://aka.ms/iotrefarchitecture).

articles/iot-accelerators/iot-accelerators-remote-monitoring-automate.md

@@ -6,7 +6,7 @@ manager: timlt
 ms.author: dobett
 ms.service: iot-accelerators
 services: iot-accelerators
-ms.date: 07/19/2018
+ms.date: 11/08/2018
 ms.topic: tutorial
 ms.custom: mvc