VM Scaleset vs Azure Batch

Question

VM scale set can be used to create multiple VM\'s based on the business requirement and, Also, Azure batch is also used to execute job in multiple VM\'s.

What is the

Answer 1

Azure Batch is a Platform as a Service offering that has an entire plaform for scheduling, submitting tasks and obtaining their results. Jobs and tasks are submitted using Node pools. Node pools can be comprised of VMSS compute resourses.

Whereas a VMSS is an Infrastructure as a Service that provides compute resources for any intended purposes. While you can spin up your own VMSS for running tasks, you would have to also implement your own job, task and compute coordinator service around it in order to simulate the Azure Batch service offerings.

Answer 2

azure batch intent is to run jobs, vmss workloads. technically they do overlap a fair bit, but job is something rather short lived\bursty, whereas workload has to be working all the time

Answer 3

At a high-level, Azure Batch provides two fundamental pieces for scheduling Batch and HPC workloads in the cloud:

1. Managed infrastructure
2. Cloud-native job scheduling

Azure Batch presents infrastructure at a managed layer "above" VMSS and CloudServices. Azure Batch orchestrates the pieces underneath to provide a concept called Batch pools, which provide potentially higher scale (as multiple deployments can be orchestrated together transparently) and higher resiliency to failures as Batch automatically recovers virtual machines or cloud service instances which have degraded.

Additionally, and just as important, Azure Batch provides cloud-native job scheduling. This portion is fully managed, i.e., you don't have to run a scheduler yourself. In a nutshell, Azure Batch provides concepts for job queues and tasks which you can define within the programmatic (API/SDK) or tooling that is available. Azure Batch operates on these concepts to execute the work you define (e.g., a command-line with dependencies or a Docker container); tasks can even span multiple nodes (e.g., MPI jobs). Azure Batch has the ability to retry these tasks if they fail on different nodes within a pool. Azure Batch provides an autoscale system that allows you to dynamically resize your infrastructure (Batch pools) that respond to node metrics and the number of jobs/tasks executing in the system.

Please refer to the technical overview as a starting point.