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Azure Virtual Machine Scale Sets(VMSS)

Simplifying VM Deployment at scale.

Vaibhav Pandey

Every Azure Service solves one of the architecture(read “ilities” concerns) or business problem or sometimes both.

What is Azure VMSS and what you can achieve with it?

Azure VM Scale Sets is one of the key service used to achieve high scalability, availability and elastic computing on the Azure cloud infrastructure. The service lets its consumer add scale-in and scale-out rules to add/remove compute infrastructure(read VM’s) dynamically based on the metrics in the rule conditions.

Service is designed to handle load and ensure apps and services deployed on the underlying compute infrastructure remains available to its consumers. Azure will let users add or remove compute infrastructure in a fully automated manner.

VMSS service is not designed to add applications on the compute infrastructure. It is the responsibility of the organisations which owns and runs the services.

Are Scale Sets and Availability Set same thing?

Please note that Scale Sets is very different from Availability Set option available with Virtual Machine service. Availability set is more at solving the availability of the compute infrastructure. Availability Set will ensure that VM’s are available in case of outages due to maintenance, disasters, etc. Where as Scale Sets help you increase compute in an elastic manner in case of increase in load which may happen under certain conditions due to business or non business reasons e.g. Black Friday sales, etc.

How to install intended apps/services on the newly added machines as part of the VMSS?

Number of options are available in hands of service owners. Few important ones are listed below:

  1. Use Custom Script Extensions.
  2. Use Custom VM image to provision VMs as part of the scale set.
  3. Use Devops to install the apps once VM is added to the the group

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Vaibhav Pandey
Vaibhav Pandey

Written by Vaibhav Pandey

https://vaibhavpandey.co.uk, 9x Azure Certs Masters Degree in AI 2023, PG Diploma in AI 2022, Desertation in Cancer Prediction, Builds with AI

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