Cost Optimization With Kubernetes
Over the past two years at Magalix, we have focused on building our system, introducing new features, and scaling our infrastructure and microservices. During this time, we had a look at our Kubernetes clusters utilization and found it to be very low. We were paying for resources we didn’t use, so we started a cost-saving practice to increase cluster utilization, use the resources we already had and pay less to run our cluster.
In this article, I will discuss the top five techniques we used to better utilize our Kubernetes clusters on the cloud and eliminate wasted resources, thus saving money. In the end, we were able to cut our monthly bill by more than 50%!
- Applying Workload Right-Sizing
Kubernetes manages and schedules pods are based on container resource specs:
Resource Requests: Kubernetes scheduler is used to place containers on the right node which has enough capacity
Resource Limits: Containers are NOT allowed to use more than their resource limit
Resources requests and limits are container-scooped specs, while multi-container pods define separate resource specs for each container:
- name: nginx
- containerPort: 80
Kubernetes schedules pods based on resource requests and other restrictions without impairing availability. The scheduler uses CPU and memory resource requests to schedule the workloads in the right nodes, control which pod works 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 high/unneeded CPU or memory resource requests can end up running underutilized pods on each node, which leads to underutilized nodes.
In this section, we compared resource requests, limited against actual usage and changed the resource request to something closer to the actual utilization while adding a little safety margin.
- Choosing The Right Worker Nodes
Every Kubernetes cluster has its own special workload utilization. Some clusters use memory more than CPU (e.g: database and caching workloads), while others use CPU more than memory (e.g: user-interactive and batch-processing workloads)
Cloud providers such as GCP and AWS offer various node types that you can choose from.
Choosing the wrong node size for your cluster can end up costing you. For instance, choosing high CPU-to-memory ratio nodes for workloads that use memory extensively can starve for memory easily and trigger auto node scale-up, wasting more CPUs that we don’t need.
Calculating the right ratio of CPU-to-memory isn’t easy; you will need to monitor and know your workloads well.
For example, GCP offers general purpose, compute-optimized, memory-optimized with various CPU and memory count and ratios:
Just keep in mind that 1 vCPU is way more expensive than 1GB memory. I have enough memory in the clusters I manage so I try to make sure that when there is a pending pod, this pod is pending on CPUs (which is expensive) so the autoscaler triggers a scale-up for the new node.
To see the cost difference between CPU and memory, let us look at the GCP N2 machine price. GCP gives you the freedom to choose a custom machine type:
(# vCPU x 1vCPU price) + (# GB memory x 1GB memory price)
It’s clear here that the 1vCPU costs 7.44 times more than the cost of 1GB.
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