TLDR; Anyone who manages significantly sized Kubernetes environments has tried to optimize them.
Or, at least, had the intention to.
Optimizing the cost of Kubernetes clusters can be challenging and can result in trade-offs between system performance and costs. Some common things to consider when optimizing the cost of running Kubernetes include:
- Resource Allocation: Reducing the amount of resources allocated to each pod can reduce costs, but reducing too much can impact the overall performance of the application by causing increased latency and decreased reliability.
- Scaling: Adjusting scaling configurations can reduce cost, but can lead to suboptimal results, such as not scaling up enough resources when demand increases, which can impact performance and lead to downtime.
- Networking: Networking costs can be a significant portion of the total cost of running a Kubernetes cluster. However, optimizing networking costs may result in decreased network performance and increased latency.
- Storage: Cost optimization may result in suboptimal storage decisions, such as using lower-performance or capacity storage options. This can lead to reduced performance and potential downtime.
- Maintenance: The cost of maintaining a cost-optimized Kubernetes cluster can be high, as there may be a need for continuous governance, monitoring, and analysis to ensure that the cluster continues to operate efficiently.
It is important to find the right balance between optimizing costs and ensuring that the performance and reliability of the applications running on the cluster are not compromised. Data-driven decisions are key to avoiding these issues and preventing future fire drills that can impact the productivity of you and your team.
With a significant portion of applications running on the cloud, maintaining the cost while ensuring resilience of Kubernetes clusters has become a critical concern. While optimizing the cost of running applications on a Kubernetes cluster is important, it can also be challenging and result in trade-offs between performance and cost. In this blog post, we'll discuss some of the common issues that may arise when optimizing the cost of running Kubernetes, and how to find a balance between performance and cost.
1. Resource Allocation: A Fine Balance
One of the key aspects of cost optimization in Kubernetes is resource allocation. If resources are over-provisioned, the cost of running the application will increase, potentially outweighing any benefits in performance. By reducing the amount of resources allocated to each pod, organizations can lower their overall costs. However, reducing the resources too much can lead to decreased performance and increased latency. To avoid this issue, organizations should carefully consider the resource requirements of their applications and ensure that each pod is allocated enough resources to meet the performance needs of the application. Under-provisioning can cause Out of Memory (OOM), CPU throttling, evictions, or latency which will cause additional fire drills your team will need to handle.
2. Autoscaling: Cost vs. Performance
Another important factor in cost optimization is leveraging autoscaling capabilities, like Horizontal Pod Autoscaler (HPA), KEDA, and/or Cluster Autoscaler/Karpenter. To remain cost effective, DevOps and Platform Engineering teams must balance the cost of scaled resources against the need for performance. If resources are scaled up too late, or scaled down too early, when demand spikes happen the performance, availability, and reliability of the application can be impacted. On the other hand, if resources are overprovisioned, autoscaling may result in the cost of running the application to increase, potentially outweighing any benefits in performance. To find the right balance, organizations should monitor their applications closely and make scaling decisions based on the specific requirements of their applications, and keep in mind that system loads continually change over time depending on your usage behaviors.
3. Networking: The Cost of Connectivity
Networking costs can be a significant portion of the total cost of running a Kubernetes cluster. To optimize networking costs, organizations may consider using different pod tolerations to keep most traffic within a region/zone/node. However, these cost-saving measures can also result in SLA or SLO breaches caused by decreased network performance and increased latency. To avoid this issue, organizations should carefully consider the network requirements of their applications as a group and choose networking topology that optimally balances cost and performance.
4. Storage: The Cost of Data
Storage costs can also be a significant portion of the overall cost of running a Kubernetes cluster. To optimize storage costs, organizations may consider using lower-cost, lower-performance, or lower capacity storage options. However, this can result in reduced performance and increased downtime, as the storage system may not be able to keep up with the demands of the application. To balance cost and performance, organizations should carefully consider the storage requirements of their applications and choose storage options that help them avoid these issues.
5. Maintenance: The Cost of Keeping Things Running
The price of maintaining a cost-optimized Kubernetes cluster may be higher, as there may be a need for continuous governance, monitoring, and analysis to ensure that the cluster continues to operate efficiently. To minimize the cost of maintenance, organizations should consider using proper tools, automation, and processes to monitor and manage their cluster.
Maintaining high performance and availability for applications running on a Kubernetes cluster, while controlling costs is a critical concern for organizations. By carefully considering the resource, scaling, networking, storage, and maintenance requirements of their applications, organizations can find the right balance between cost optimization and performance. Regular monitoring and evaluation of the cluster are essential in making the adjustments needed to ensure your cluster continues to operate efficiently, and that applications continue to perform as expected.
PerfectScale makes it simple to continuously optimize your Kubernetes clusters. We provide complete visibility across your multi-cloud, highly-distributed Kubernetes environment and allow you to quickly drill down into individual services, workloads, and containers that need your attention. Our AI-guided intelligence analyzes the dynamic usage patterns of your Kubernetes environment to understand the requirements needed to meet the demand of your application. This allows us to provide precise recommendations on how to optimally configure the size and scale of your environment, allowing you to easily and effortlessly improve system reliability, performance, cost-effectiveness, and environmental sustainability. Start your free, 30-day trial of PerfectScale today.