The Azure Batch service is now available for general use. It is a fully managed service hosted in Azure that lets you configure scheduled jobs and supports performing compute resource management for other cloud based services. It is a turnkey solution for running large scale High Performance Computing (HPC) applications in parallel, leveraging the cloud scale. Note that it is a platform service that allows you to run resource intensive operations on a managed collection of virtual machines and can scale automatically depending on the need.
There are several use-cases for Azure Batch including scientific computations such as Monte Carlo simulations, financial modelling, media transcoding, and a more common scenario with automated testing of applications. The Azure Batch service works very well with scenarios that are intrinsically parallel in nature. Scenarios where a workload can be broken into multiple tasks that can run in parallel are the best possible use cases for Azure Batch service. Not only can the managed service run multiple workloads, it can also be configured for parallel calculations with a reduce step in the end.
To configure a new Batch service, login to your Azure portal and then find the Batch managed service from the Azure Marketplace by typing Batch in the search window.
Specify a name for the Batch service and configure the resource group and the storage account:
Once the service is deployed you will see the dashboard to configure the applications and jobs as illustrated in the following figure:
Now that you have successfully created the Batch Account, you can use the batch service most commonly in two ways:
- Use the Batch .NET API to programmatically schedule the job
- Use it part of a larger workflow like Azure Data Factory
In addition, there is an Azure Batch Explorer sample application available in GitHub that you can run to browse and manage the resources in your Batch Account.
You can use the Batch API to perform tasks such as creating a Job, provisioning a Schedule and adding Tasks to a Job. For example, you can create a console application that reads from a file and performs multiple parallel operations based on the content of the file. You can store the application in Azure Blob Storage and then configure the Job to run the application on a regular interval.
cloud services, Parallel processing, Azure Services, batch processing, performance gains