Merge pull request #103166 from laurenhughes/vm-sizes-reorg

VM sizes reorg

Author: GitHubber17

.openpublishing.redirection.json

@@ -48536,6 +48536,96 @@
       "redirect_url": "/azure/cognitive-services/speech-service/quickstarts/setup-platform?pivots=programming-language-python",
       "redirect_document_id": false
     },
+    {
+      "source_path": "articles/virtual-machines/windows/sizes-general.md",
+      "redirect_url": "/azure/virtual-machines/sizes-general",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/linux/sizes-general.md",
+      "redirect_url": "/azure/virtual-machines/sizes-general",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/windows/sizes-compute.md",
+      "redirect_url": "/azure/virtual-machines/sizes-compute",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/linux/sizes-compute.md",
+      "redirect_url": "/azure/virtual-machines/sizes-compute",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/windows/sizes-memory.md",
+      "redirect_url": "/azure/virtual-machines/sizes-memory",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/linux/sizes-memory.md",
+      "redirect_url": "/azure/virtual-machines/sizes-memory",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/windows/sizes-storage.md",
+      "redirect_url": "/azure/virtual-machines/sizes-storage",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/linux/sizes-storage.md",
+      "redirect_url": "/azure/virtual-machines/sizes-storage",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/windows/sizes-gpu.md",
+      "redirect_url": "/azure/virtual-machines/sizes-gpu",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/linux/sizes-gpu.md",
+      "redirect_url": "/azure/virtual-machines/sizes-gpu",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/windows/sizes-hpc.md",
+      "redirect_url": "/azure/virtual-machines/sizes-hpc",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/linux/sizes-hpc.md",
+      "redirect_url": "/azure/virtual-machines/sizes-hpc",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/windows/sizes-previous-gen.md",
+      "redirect_url": "/azure/virtual-machines/sizes-previous-gen",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/linux/sizes-previous-gen.md",
+      "redirect_url": "/azure/virtual-machines/sizes-previous-gen",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/windows/acu.md",
+      "redirect_url": "/azure/virtual-machines/acu",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/linux/acu.md",
+      "redirect_url": "/azure/virtual-machines/acu",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/windows/b-series-burstable.md",
+      "redirect_url": "/azure/virtual-machines/sizes-b-series-burstable",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/virtual-machines/linux/b-series-burstable.md",
+      "redirect_url": "/azure/virtual-machines/sizes-b-series-burstable",
+      "redirect_document_id": false
+    },
     {
        "source_path": "articles/cognitive-services/Bing-News-Search/vs-bing-news-search-connected-service.md",
        "redirect_url": "/azure/cognitive-services/bing-news-search/search-the-web",

articles/app-service/overview-hosting-plans.md

@@ -32,7 +32,7 @@ Each tier also provides a specific subset of App Service features. These feature
 <a name="new-pricing-tier-premiumv2"></a>
 
 > [!NOTE]
-> The new **PremiumV2** pricing tier provides [Dv2-series VMs](../virtual-machines/windows/sizes-general.md#dv2-series) with faster processors, SSD storage, and double memory-to-core ratio compared to **Standard** tier. **PremiumV2** also supports higher scale via increased instance count while still providing all the advanced capabilities found in the Standard plan. All features available in the existing **Premium** tier are included in **PremiumV2**.
+> The new **PremiumV2** pricing tier provides [Dv2-series VMs](../virtual-machines/dv2-dsv2-series.md) with faster processors, SSD storage, and double memory-to-core ratio compared to **Standard** tier. **PremiumV2** also supports higher scale via increased instance count while still providing all the advanced capabilities found in the Standard plan. All features available in the existing **Premium** tier are included in **PremiumV2**.
 >
 > Similar to other dedicated tiers, three VM sizes are available for this tier:
 >

articles/azure-functions/durable/durable-functions-perf-and-scale.md

@@ -246,7 +246,7 @@ When planning to use Durable Functions for a production application, it is impor
 > [!TIP]
 > Unlike fan-out, fan-in operations are limited to a single VM. If your application uses the fan-out, fan-in pattern and you are concerned about fan-in performance, consider sub-dividing the activity function fan-out across multiple [sub-orchestrations](durable-functions-sub-orchestrations.md).
 
-The following table shows the expected *maximum* throughput numbers for the previously described scenarios. "Instance" refers to a single instance of an orchestrator function running on a single small ([A1](../../virtual-machines/windows/sizes-previous-gen.md#a-series)) VM in Azure App Service. In all cases, it is assumed that [extended sessions](#orchestrator-function-replay) are enabled. Actual results may vary depending on the CPU or I/O work performed by the function code.
+The following table shows the expected *maximum* throughput numbers for the previously described scenarios. "Instance" refers to a single instance of an orchestrator function running on a single small ([A1](../../virtual-machines/sizes-previous-gen.md)) VM in Azure App Service. In all cases, it is assumed that [extended sessions](#orchestrator-function-replay) are enabled. Actual results may vary depending on the CPU or I/O work performed by the function code.
 
 | Scenario | Maximum throughput |
 |-|-|

articles/batch/batch-pool-compute-intensive-sizes.md

@@ -40,7 +40,7 @@ The RDMA or GPU capabilities of compute-intensive sizes in Batch are supported o
 
 | Size | Capability | Operating systems | Required software | Pool settings |
 | -------- | -------- | ----- |  -------- | ----- |
-| [H16r, H16mr, A8, A9](../virtual-machines/linux/sizes-hpc.md#rdma-capable-instances)<br/>[NC24r, NC24rs_v2, NC24rs_v3, ND24rs<sup>*</sup>](../virtual-machines/linux/n-series-driver-setup.md#rdma-network-connectivity) | RDMA | Ubuntu 16.04 LTS, or<br/>CentOS-based HPC<br/>(Azure Marketplace) | Intel MPI 5<br/><br/>Linux RDMA drivers | Enable inter-node communication, disable concurrent task execution |
+| [H16r, H16mr, A8, A9](../virtual-machines/linux/sizes-hpc.md)<br/>[NC24r, NC24rs_v2, NC24rs_v3, ND24rs<sup>*</sup>](../virtual-machines/linux/n-series-driver-setup.md#rdma-network-connectivity) | RDMA | Ubuntu 16.04 LTS, or<br/>CentOS-based HPC<br/>(Azure Marketplace) | Intel MPI 5<br/><br/>Linux RDMA drivers | Enable inter-node communication, disable concurrent task execution |
 | [NC, NCv2, NCv3, NDv2 series](../virtual-machines/linux/n-series-driver-setup.md) | NVIDIA Tesla GPU (varies by series) | Ubuntu 16.04 LTS, or<br/>CentOS 7.3 or 7.4<br/>(Azure Marketplace) | NVIDIA CUDA or CUDA Toolkit drivers | N/A | 
 | [NV, NVv2 series](../virtual-machines/linux/n-series-driver-setup.md) | NVIDIA Tesla M60 GPU | Ubuntu 16.04 LTS, or<br/>CentOS 7.3<br/>(Azure Marketplace) | NVIDIA GRID drivers | N/A |
 
@@ -50,7 +50,7 @@ The RDMA or GPU capabilities of compute-intensive sizes in Batch are supported o
 
 | Size | Capability | Operating systems | Required software | Pool settings |
 | -------- | ------ | -------- | -------- | ----- |
-| [H16r, H16mr, A8, A9](../virtual-machines/windows/sizes-hpc.md#rdma-capable-instances)<br/>[NC24r, NC24rs_v2, NC24rs_v3, ND24rs<sup>*</sup>](../virtual-machines/windows/n-series-driver-setup.md#rdma-network-connectivity) | RDMA | Windows Server 2016, 2012 R2, or<br/>2012 (Azure Marketplace) | Microsoft MPI 2012 R2 or later, or<br/> Intel MPI 5<br/><br/>Windows RDMA drivers | Enable inter-node communication, disable concurrent task execution |
+| [H16r, H16mr, A8, A9](../virtual-machines/sizes-hpc.md)<br/>[NC24r, NC24rs_v2, NC24rs_v3, ND24rs<sup>*</sup>](../virtual-machines/windows/n-series-driver-setup.md#rdma-network-connectivity) | RDMA | Windows Server 2016, 2012 R2, or<br/>2012 (Azure Marketplace) | Microsoft MPI 2012 R2 or later, or<br/> Intel MPI 5<br/><br/>Windows RDMA drivers | Enable inter-node communication, disable concurrent task execution |
 | [NC, NCv2, NCv3, ND, NDv2 series](../virtual-machines/windows/n-series-driver-setup.md) | NVIDIA Tesla GPU (varies by series) | Windows Server 2016 or <br/>2012 R2 (Azure Marketplace) | NVIDIA CUDA or CUDA Toolkit drivers| N/A | 
 | [NV, NVv2 series](../virtual-machines/windows/n-series-driver-setup.md) | NVIDIA Tesla M60 GPU | Windows Server 2016 or<br/>2012 R2 (Azure Marketplace) | NVIDIA GRID drivers | N/A |
 
@@ -64,7 +64,7 @@ The RDMA or GPU capabilities of compute-intensive sizes in Batch are supported o
 
 | Size | Capability | Operating systems | Required software | Pool settings |
 | -------- | ------- | -------- | -------- | ----- |
-| [H16r, H16mr, A8, A9](../virtual-machines/windows/sizes-hpc.md#rdma-capable-instances) | RDMA | Windows Server 2016, 2012 R2, 2012, or<br/>2008 R2 (Guest OS family) | Microsoft MPI 2012 R2 or later, or<br/>Intel MPI 5<br/><br/>Windows RDMA drivers | Enable inter-node communication,<br/> disable concurrent task execution |
+| [H16r, H16mr, A8, A9](../virtual-machines/sizes-hpc.md) | RDMA | Windows Server 2016, 2012 R2, 2012, or<br/>2008 R2 (Guest OS family) | Microsoft MPI 2012 R2 or later, or<br/>Intel MPI 5<br/><br/>Windows RDMA drivers | Enable inter-node communication,<br/> disable concurrent task execution |
 
 ## Pool configuration options
 
@@ -99,7 +99,7 @@ To run CUDA applications on a pool of Windows NC nodes, you need to install NVDI
 
 1. Download a setup package for the GPU drivers on Windows Server 2016 from the [NVIDIA website](https://www.nvidia.com/Download/index.aspx) - for example, [version 411.82](https://us.download.nvidia.com/Windows/Quadro_Certified/411.82/411.82-tesla-desktop-winserver2016-international.exe). Save the file locally using a short name like *GPUDriverSetup.exe*.
 2. Create a zip file of the package.
-3. Upload the package to your Batch account. For steps, see the [application packages](batch-application-packages.md) guidance. Specify an application id such as *GPUDriver*, and a version such as *411.82*.
+3. Upload the package to your Batch account. For steps, see the [application packages](batch-application-packages.md) guidance. Specify an application ID such as *GPUDriver*, and a version such as *411.82*.
 1. Using the Batch APIs or Azure portal, create a pool in the virtual machine configuration with the desired number of nodes and scale. The following table shows sample settings to install the NVIDIA GPU drivers silently using a start task:
 
 | Setting | Value |
@@ -135,8 +135,7 @@ To run CUDA applications on a pool of Linux NC nodes, you need to install necess
 To run Windows MPI applications on a pool of Azure H16r VM nodes, you need to configure the HpcVmDrivers extension and install [Microsoft MPI](https://docs.microsoft.com/message-passing-interface/microsoft-mpi). Here are sample steps to deploy a custom Windows Server 2016 image with the necessary drivers and software:
 
 1. Deploy an Azure H16r VM running Windows Server 2016. For example, create the VM in the US West region. 
-2. Add the HpcVmDrivers extension to the VM by [running an Azure PowerShell command](../virtual-machines/windows/sizes-hpc.md#rdma-capable-instances
-) from a client computer that connects to your Azure subscription, or using Azure Cloud Shell. 
+2. Add the HpcVmDrivers extension to the VM by [running an Azure PowerShell command](../virtual-machines/sizes-hpc.md) from a client computer that connects to your Azure subscription, or using Azure Cloud Shell. 
 1. Make a Remote Desktop connection to the VM.
 1. Download the [setup package](https://www.microsoft.com/download/details.aspx?id=57467) (MSMpiSetup.exe) for the latest version of Microsoft MPI, and install Microsoft MPI.
 1. Follow the steps to create a [Shared Image Gallery image](batch-sig-images.md) for Batch.

articles/batch/batch-pool-node-error-checking.md

@@ -99,7 +99,7 @@ Additional examples of causes for **unusable** nodes include:
 
 - A VM is moved because of an infrastructure failure or a low-level upgrade. Batch recovers the node.
 
-- A VM image has been deployed on hardware that doesn’t support it. For example, trying to run a CentOS HPC image on a [Standard_D1_v2](../virtual-machines/linux/sizes-general.md#dv2-series) VM.
+- A VM image has been deployed on hardware that doesn’t support it. For example, trying to run a CentOS HPC image on a [Standard_D1_v2](../virtual-machines/dv2-dsv2-series.md) VM.
 
 - The VMs are in an [Azure virtual network](batch-virtual-network.md), and traffic has been blocked to key ports.
 

articles/container-instances/container-instances-gpu.md

@@ -37,9 +37,9 @@ To use GPUs in a container instance, specify a *GPU resource* with the following
 
   | SKU | VM family |
   | --- | --- |
-  | K80 | [NC](../virtual-machines/linux/sizes-gpu.md#nc-series) |
-  | P100 | [NCv2](../virtual-machines/linux/sizes-gpu.md#ncv2-series) |
-  | V100 | [NCv3](../virtual-machines/linux/sizes-gpu.md#ncv3-series) |
+  | K80 | [NC](../virtual-machines/nc-series.md) |
+  | P100 | [NCv2](../virtual-machines/ncv2-series.md) |
+  | V100 | [NCv3](../virtual-machines/ncv3-series.md) |
 
 [!INCLUDE [container-instances-gpu-limits](../../includes/container-instances-gpu-limits.md)]
 

articles/hdinsight/hdinsight-supported-node-configuration.md

@@ -38,9 +38,9 @@ The specifications of all minimum recommended VM types used in this document are
 
 For more details on the specifications of each VM type, see the following documents:
 
-* [General purpose virtual machine sizes: Dv2 series 1-5](../virtual-machines/linux/sizes-general.md#dv2-series)
-* [Memory optimized virtual machine sizes: Dv2 series 11-15](../virtual-machines/linux/sizes-memory.md#dv2-series-11-15)
-* [General purpose virtual machine sizes: Av2 series 1-8](../virtual-machines/linux/sizes-general.md#av2-series)
+* [General purpose virtual machine sizes: Dv2 series 1-5](../virtual-machines/dv2-dsv2-series.md)
+* [Memory optimized virtual machine sizes: Dv2 series 11-15](../virtual-machines/dv2-dsv2-series-memory.md)
+* [General purpose virtual machine sizes: Av2 series 1-8](../virtual-machines/av2-series.md)
 
 ### All supported regions except Brazil south and Japan west
 

articles/iot-edge/tutorial-machine-learning-edge-02-prepare-environment.md

@@ -20,7 +20,7 @@ This article helps you prepare your environment for development and deployment.
 
 This step is typically performed by a cloud developer. Some of the software may also be helpful for a data scientist.
 
-We created a PowerShell script that creates an Azure virtual machine with many of the prerequisites already configured. The VM that we create needs to be able to handle [nested virtualization](https://docs.microsoft.com/azure/virtual-machines/windows/nested-virtualization), which is why we chose a [Standard_D8s_v3](../virtual-machines/windows/sizes-general.md#dsv3-series-1) machine size.
+We created a PowerShell script that creates an Azure virtual machine with many of the prerequisites already configured. The VM that we create needs to be able to handle [nested virtualization](https://docs.microsoft.com/azure/virtual-machines/windows/nested-virtualization), which is why we chose a [Standard_D8s_v3](../virtual-machines/dv3-dsv3-series.md) machine size.
 
 The development VM will be set up with:
 
@@ -29,7 +29,7 @@ The development VM will be set up with:
 * [Docker Desktop for Windows](https://www.docker.com/products/docker-desktop)
 * [Git for Windows](https://gitforwindows.org/)
 * [Git Credential Manager for Windows](https://github.com/Microsoft/Git-Credential-Manager-for-Windows)
-* [.Net Core SDK](https://dotnet.microsoft.com/)
+* [.NET Core SDK](https://dotnet.microsoft.com/)
 * [Python 3](https://www.python.org/)
 * [Visual Studio Code](https://code.visualstudio.com/)
 * [Azure PowerShell](https://docs.microsoft.com/powershell/azure/overview?view=azps-1.1.0)
@@ -46,7 +46,7 @@ It takes about 30 minutes to create and configure the virtual machine.
 
 1. Clone or download the [Machine Learning and IoT Edge](https://github.com/Azure-Samples/IoTEdgeAndMlSample) sample repository to your local computer.
 
-1. Open Powershell as an administrator and navigate to the **\IoTEdgeAndMlSample\DevVM** directory located under the root directory where you downloaded the code. We will refer to the root directory for your source as `srcdir`.
+1. Open PowerShell as an administrator and navigate to the **\IoTEdgeAndMlSample\DevVM** directory located under the root directory where you downloaded the code. We will refer to the root directory for your source as `srcdir`.
 
     ```powershell
     cd c:\srcdir\IoTEdgeAndMlSample\DevVM