Cost Optimization With Magalix

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Is Our Spending Getting Worse?
I woke up one day to see this email from our CEO in my mailbox. I knew this would happen at some point as we had not been paying attention to our infrastructure spending. The time had finally come to deal with our problem.

Kubernetes Cost Optimization with Magalix
We need to ensure that our system is performant and meets our internally mandated SLAs. However, we also need to ensure that we are using our infrastructure efficiently and eliminate any unwarranted waste. The bottom line is just as important as the top line in any business. In a SaaS business like ours, the bottom line is mainly driven by our infrastructure spending

Background
Running Kubernetes on the cloud isn’t expensive- we can roll out a Kubernetes cluster with an average cost of $70 per month. What drives the cost up is running worker nodes to host and run our workloads. Many considerations that contribute to the cost, but most importantly is how much your cluster is utilizing the resources you’re actually running. We discussed various techniques to improve your cluster utilization in “Kubernetes Cost Optimization 101”

In this article, we will discuss how Magalix can help you better utilize your resources and pay less to run the same cluster.

Applying Workload Right-Sizing Using KubeOptimizer
Kubernetes manages and schedules pods based on container resource specs:

Resource Requests: Kubernetes scheduler places containers on the node that has enough capacity
Resource Limits: Containers are NOT allowed to use more than their resource limit
Resource requests and limits are container-scoped specs, while multi-container pods define separate resource specs for each container.

Kubernetes schedules pods based on the resource request and other restrictions without impairing availability. It uses CPU and memory resource requests to schedule workloads in the right nodes, while controlling which pod can work on which node and if multiple pods can schedule together on a single node

Every node type has its own allocatable CPU and memory capacities. Assigning unneeded CPU or memory resource requests can leave you with underutilized pods on each node, which leads to underutilized nodes

In this section, we will use the Container Resource Advisor (a Magalix built-in advisor) to right-size the containers in our cluster.

This is the Cluster Dashboard in Magalix console, In the “CPU Usage Distribution” and “Memory Usage Distribution”, I can view what workloads are utilizing my cluster the most. Right-sizing these workloads can save money by eliminating wasted capacity.

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