Container technologies, spearheaded by Kubernetes, an open-source container orchestration system, are rapidly establishing themselves as the go-to solution for modern application development. Renowned organizations like Red Hat have reported that about two-thirds of IT leaders are already employing containers and Kubernetes. Both technologies are anticipated to witness an exponential rise in their adoption rates, as businesses strive to enhance the portability and efficiency of their applications.
However, Kubernetes can prove to be a costly affair if not managed efficaciously. In-house management of Kubernetes often incurs substantial expenses. Therefore, it becomes imperative for development teams to optimize their Kubernetes clusters and contemplate the utilization of a managed Kubernetes solution, like Amazon Elastic Kubernetes Service (EKS).
Regardless of the strategy adopted, it's crucial to understand the steps to achieve cost optimization with Kubernetes. Here are four critical tactics:
1. Enhancing Cluster Cost Visibility
The expenses associated with Kubernetes can be categorized into six dimensions:
- Workloads
- Infrastructure: encompassing compute, networking, and storage
- Platform: EKS and ECR
- DevOps: CI/CD and security
- Observability: logging, metrics, and tracing
- Operations: engineering
These dimensions offer the best opportunities for cost reduction, especially the workloads, compute, EKS, and engineering buckets. However, it's not sufficient to cut costs based solely on these categories. It's more effective to consider costs at the cluster, pod or container, service or deployment, or namespace level, as this approach aligns better with the practical deployment of Kubernetes clusters.
To enhance visibility into costs, consider the following best practices:
- Tagging AWS resources with native tools, like Cost Explorer or Cost Usage Reports
- Utilizing ecosystem tools such as Kubecost, StormForge, or CloudZero
- Remediating clusters based on tooling feedback
- Continual cost monitoring
- Implementing showback or chargeback for internal platform users
Keep in mind that the optimization of Kubernetes cluster costs is an ongoing process. Hence, maintaining precise visibility into expenditure is of utmost importance.
2. Profiling Applications
A common practice among organizations is to overprovision Kubernetes resources for their applications, mainly because IT teams often do not have a clear understanding of their applications' resource needs. This generally happens when teams deploy publicly available container images and rarely reassess their choices.
Regular application profiling enables you to adjust resource allocations as needed. Engineers can estimate their resource requirements and then right-size accordingly. Observability tools like Amazon CloudWatch simplify the understanding of resource utilization through various data visualizations. Additional best practices for application profiling include load-testing applications to establish resource requirements at scale, and implementing limits where necessary. It's advisable to prioritize memory limits.
3. Implementing Autoscaling for Elasticity
Autoscaling is critical when working with Kubernetes. Autoscaling can be thought of in two ways:
- Horizontal or vertical pod autoscaling (HPA or VPA)
- Cluster Autoscaler (CAS or Karpenter)
Pod autoscaling focuses on autoscaling applications. This method requires teams to continuously right-size resource allocations to pods (refer to application profiling). Horizontal pod scaling is more prevalent than vertical scaling, although VPA is favored for legacy applications. Engineers can set various targets for determining when HPA scaling should occur, like when CPU utilization reaches 80%, or when HTTP requests per minute exceed 15,000.
Cluster Autoscaler is an open-source project that facilitates cluster autoscaling. This method uses EC2 Auto-scaling groups to scale compute and adds new compute instances when there are no nodes available with the required capacity for the application. The compute instances added are based on the existing EC2 auto-scaling group.
Karpenter, another open-source software, can automatically add or remove compute resources. However, Karpenter dynamically selects compute resources that best meet the application's requirements. This flexibility in instance-type and improved resource utilization sets Karpenter apart from the Cluster Autoscaler.
4. Optimizing Cluster Compute
Optimizing cluster compute essentially means optimizing EC2 or AWS Fargate resources. Fortunately, developers have several options that can impact pricing, capacity, and performance.
One of the most significant decisions to make is whether to opt for self-managed node groups, managed node groups, or serverless infrastructure. With self-managed, organizations use their own autoscaling groups running on a custom AMI. In-house engineers are responsible for patching and maintaining the underlying operating system. A service like Amazon EKS manages the control plane within a company's VPC. The data plane can be managed via EC2 compute in the user's VPC or using AWS Fargate in an AWS VPC.
The ideal configuration depends on the target workload. With more than 550 instance types available, most business needs can be covered. Building an optimal list of instance types requires answering the following questions:
- What processors can my workload use?
- What are my workload's performance requirements?
- What is my workload's consumption pattern?
For users of EC2, Amazon offers multiple purchasing options: on-demand instances, savings plans, and Spot instances. Greater savings usually imply less flexibility. IT leaders need to decide what they want to prioritize.
Adopt Amazon EKS for Kubernetes Management
For those who prefer not to manage Kubernetes clusters in-house, consider Amazon EKS. This managed service simplifies the maintenance, scaling, and deployment of containerized applications at scale. Amazon EKS is compatible with containerized applications both in the cloud and on-premises. It also integrates seamlessly with other popular AWS services like EC2 and IAM, which are essential for building a robust container management capability.
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