Automating Continuous Optimization with the Azure Optimization Engine


Hello, dear readers! Here is Hélder Pinto again, writing the last post of a series dedicated to the implementation of automated Continuous Optimization with Azure Advisor recommendations. For a contextualization of the solution here described, please read the introductory post for an overview of the solution, the second post for the details and deployment of the main solution components and also the third post to see how the Azure Optimization Engine generates recommendations and reports on it.



If you didn’t have time to read the full post series about the Azure Optimization Engine, let me quickly recap. The Azure Optimization Engine (AOE) is an extensible solution designed to generate custom optimization recommendations for your Azure environment. See it like a fully customizable Azure Advisor. It leverages Azure Resource Graph, Log Analytics, Automation, and, of course, Azure Advisor itself, to build a rich repository of custom optimization opportunities. The first recommendations use-case covered by AOE was augmenting Azure Advisor Cost recommendations, particularly Virtual Machine right-sizing, with VM metrics and properties all enabling for better informed right-size decisions. Other recommendations can be easily added/augmented with AOE, not only for cost optimization but also for security, high-availability, and other Well Architected Framework pillars.


In this last post, I will show you how we can use AOE to automate the remediation of optimization opportunities – the ultimate goal of the engine – and how to extend it with new custom recommendations.


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