After connecting your cluster Magalix KubeAdvisor starts analyzing your cluster's data. It generates a report and recommendations to improve the cluster at different dimensions, such as performance, reliability, security, cost, etc.
You will get recommendations overview at your home dashboard.
Each recommendation covers a certain improvement that you can apply to your cluster or container. But all recommendations have the same overall structure.
Each recommendation consists of 7 main sections
It tells you what you need to change to fix a reported issue. In many cases, the required change is specific and quantifiable. The above sample snapshot, for example, proposes a change nodes types, sizes, and/or numbers.
This section tells a quantifiable impact whenever possible. For example, this cost-saving recommendation tells how many yearly savings you will achieve
This section explains what Magalix agent was able to observe to generate this recommendation. Think of as an explainer to the collected evidence. In the above example, this section explains what Magalix agent was able to observe about the current capacity and compares it to the current cluster capacity. It provides a detailed analysis of the current and suggested utilization, capacity, VM types, and different billing plans.
This section shows the relevant metric or meta-data that Magalix agent collected from your cluster. The above snapshot shows the aggregated usage, allocated, and currently available of CPU and memory. It provides a visual comparison to help you decide if you are under or over-utilizing capacity that you allocated for your cluster.
This section explains and links to different resources to resolve the reported issue. Magalix in some cases provides an out of the box automation to resolve the issue. If not automation possible, you will get generic instructions to fix it.
This section provides links from the community and verified blogs to learn about the target category of issues reported in any given recommendation.
You see in this section how many times the issue was detected and a recommendation provided by Magalix KubeAdvisor.