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Stand up a MATLAB Distributed Computing Server cluster using Azure Deployment
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MATLAB Distributed Computing Server on Microsoft Azure


Before starting, you will need the following:


You are responsible for the cost of the Azure services used when you create cloud resources using this guide. Resource settings, such as instance type, will affect the cost of deployment. For cost estimates, see the pricing pages for each Azure service you will be using. Prices are subject to change.


The following guide will help you automate the process of launching MATLAB Distributed Computing Server and MATLAB Job Scheduler on Azure using your Azure account. The cloud resources are created using Azure Resource Manager (ARM) templates. For information about the architecture of this solution, see Learn About Cluster Architecture.

Choose a Deployment Option

The MATLAB Distributed Computing Server cloud reference architecture for Azure supports two license configurations: online licensing and a network license manager. For more information, see Configure MATLAB Distributed Computing Server Licensing on the Cloud. Choose a cluster deployment option based on your license configuration:

Learn About Cluster Architecture

Parallel Computing Toolbox and MATLAB Distributed Computing Server software let you solve computationally and data-intensive programs using MATLAB and Simulink on computer clusters, clouds, and grids. Parallel processing constructs such as parallel-for loops and code blocks, distributed arrays, parallel numerical algorithms, and message-passing functions let you implement task-parallel and data-parallel algorithms at a high level in MATLAB. To learn more see the documentation: Parallel Computing Toolbox and MATLAB Distributed Computing Server.

The MATLAB Job Scheduler is a built-in scheduler that ships with MATLAB Distributed Computing Server. The scheduler coordinates the execution of jobs, and distributes the tasks for evaluation to the server’s individual MATLAB sessions called workers.

Microsoft Azure is a set of cloud services which allow you to build, deploy, and manage applications hosted in Microsoft’s global network of data centres. This document will help you launch a compute cluster using compute, storage, and network services hosted by Azure. For more information about the range of services offered by Microsoft Azure, see Azure Services. Services launched in Azure can be created, managed, and deleted using the Azure Portal UI. For more information about the Azure Portal, see Azure Portal.

The MATLAB Job Scheduler and the resources required by it are created using Azure Resource Manager templates. The architecture of the cluster resources created by the template is illustrated in Figure 2. For more information about each resource, see the Azure template reference.

Cluster Architecture

Figure 2: Cluster Architecture

The following resources are created.

Networking resources

  • Virtual Network (Microsoft.Network/virtualNetworks) The Virtual Network includes the following components:
    • Subnet (Microsoft.Network/virtualNetworks/subnets)
    • Network Security Group (Microsoft.Network/networkSecurityGroups) : Ingress rules from client IP address:
      • Allow 3389: Required for Remote Desktop Protocol to the cluster nodes.
      • Allow 27350 – 27257 + (4 * number of workers): Open 7 ports, plus 4 additional ports for each worker service on the Virtual Machine (VM). Required for communication from clients to the job scheduler and worker processes.
      • Allow all internal traffic: Open access to network traffic between all cluster nodes internally.
  • Each instance deployed to the Virtual Network will create the following:
    • Network interface (Microsoft.Network/networkInterfaces)
    • Public IP Address (Microsoft.Network/publicIPAddresses)


  • Headnode instance (Microsoft.Compute/virtualMachines): A Compute instance for the cluster headnode. The MATLAB install is part of the VM image and the job database is stored either locally on the root volume, or optionally, a separate data disk can be used. Communication between clients and the headnode is secured using SSL.
    • Database Volume (optional) (Microsoft.Compute/disks): A separate data disk to store the MJS job database. This is optional, and if not chosen the root volume will be used for the job database.
    • Custom Script Extension (Microsoft.Compute/virtualMachines/extensions): An extension which configures this instance at deployment time as the headnode of the cluster.
  • Worker Scaling Set (Microsoft.Compute/virtualMachineScaleSets): A scale set for worker instances to be launched into. The scaling features are not currently used. The scale set is configured to attach an extension to each instance which configures the instance at deployment time as a worker node of the cluster. Communication between clients and workers is secured using SSL.


  • Storage Account (Microsoft.Storage/storageAccounts): A standard geographically redundant storage (GRS) Storage Account which hosts the File Share used to distribute files amongst cluster instances.
  • File Share created inside Storage Account. Created by the head node and mounted by all instances to K: drive.
    • Used to distribute the Shared Secret created by head node to all worker VMs. The Shared Secret is required for worker instances to register and establish a secure connection with the job scheduler.
    • Used to distribute the Cluster Profile to clients. The Cluster Profile is required to authenticate that a user has permission to connect to the cluster.
    • Files uploaded to this File Share will be available to all workers using the K: drive.

Enhancement Request

Provide suggestions for additional features or capabilities using the following link:

Technical Support


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