This repository contains the experimental scripts used to analyze kubernetes container workloads from azure kubernetes service and suggest appropriate request/limits for cpu and memory.
The project is focused on an optmization for container sizes. This is achieved by rightsizing the containers by determining parameters such as cpu requests, memory requests, cpu limits and memory limits.
The first notebook Data_Collection.ipynb has information on how to retrieve large result data sets from azure log analytics workspace and the KQL queries necessary to obtain this perf data.
The second notebook Resource_Recommendation_Algorithm.ipynb contains an implementation of the recommendation algorithm which will calculate request, limit values for cpu and memory for containers using their perf data. These recommendations will improve resource utilisation/performance/stability by addressing underprovsioning and overprovisioning of resources.