How does Karpenter cut Kubernetes costs by 20% and scale them faster?
At our company, we specialize in delivering advanced cloud automation solutions like Karpenter to drive operational efficiency and scalability for clients across various industries. With extensive experience in Kubernetes management, dynamic scaling, and cloud cost optimization, we offer customized solutions to maximize performance and minimize resource wastage. Recently, we partnered with a leading OTT platform that is facing significant challenges in resource management due to its rapidly expanding user base and high traffic demands.
The OTT platform’s previous scaling solution was built on a traditional autoscaler, which often led to inefficient resource allocation. Standard EKS Cluster Autoscalers were slow to respond to real-time demand shifts, resulting in over-provisioning during low-usage times and under-provisioning during peak loads. This setup not only inflated infrastructure costs by nearly 20% but also affected platform performance, especially during content releases or live events. Furthermore, the complexities of configuring auto scaler rules manually made the system difficult to adjust as their business needs evolved.
Our team recognized that Karpenter, an advanced Kubernetes-native autoscaler, would provide a more responsive, cost-effective, and efficient solution. We designed a customized setup leveraging Karpenter on their AWS EKS cluster, optimizing it for their unique requirements, including a precise On-Demand and Spot instance mix to balance cost and availability. To deploy it in their private EKS environment, we even developed a custom Python script to handle configuration in an internet-restricted setup, ensuring security and compliance with internal policies.
Through this implementation, the OTT platform gained real-time resource provisioning, an automated scaling process, and infrastructure cost savings of approximately 15-20%. Karpenter’s intelligent scaling capabilities not only eliminated the need for complex manual configurations but also ensured that the platform could dynamically handle high-demand periods seamlessly—allowing our client to focus on delivering an uninterrupted and high-quality streaming experience to their users.
Problem Statement
Our client, an OTT platform with millions of active users, faced three critical issues with their existing scaling solution:
- Inefficient Resource Allocation: The traditional EKS Cluster Autoscaler did not select the most suitable instance types. This resulted in either under-utilized resources or over-provisioned instances, causing infrastructure costs to rise by as much as 20% monthly due to inefficient resource usage.
- Delayed Scaling: The auto-scaler took 5-10 minutes to respond to spikes in workload demand, which severely impacted the platform’s performance during peak traffic. This delay led to degraded streaming quality and user experience, especially during high-demand events, costing the platform potential revenue.
- Complex Configuration: Managing the autoscaler required highly intricate configurations, and any adjustments took 2-3 days of manual effort by the DevOps team. This complexity made it difficult for the platform to quickly adapt its scaling strategy to meet evolving user demands.
These issues called for a more intelligent, real-time scaling solution that could improve efficiency, reduce costs, and simplify scaling management.
Our solution “The Karpenter”
To address the inefficiencies in resource allocation and scaling, we introduced Karpenter, a smarter, more adaptive auto-scaler designed for modern Kubernetes environments. Here’s how Karpenter revolutionized the client’s OTT platform:
Efficient and Intelligent Scaling
We implemented Karpenter on their AWS EKS cluster to enable dynamic, real-time provisioning of compute resources. Unlike traditional auto-scalers, Karpenter evaluates the current workload demands and provisions the most suitable instance types and sizes to optimize both performance and cost.
How We Did It?
Given that the client’s EKS cluster operates in a private VPC with no direct internet access, we customized the installation process by creating a Python script. This script automated the deployment of Karpenter by referencing a local Helm chart and ensuring all configurations were correctly applied.
Key steps in our solution:
- Private EKS Environment: We overcame the internet access limitation by setting up Karpenter using an internal repository and custom Python automation. This allowed us to bypass typical internet dependencies.
- Real-time Scaling: We defined configurations based on instance categories, architecture, and specific workload requirements. Karpenter continuously monitored real-time metrics and scaled the nodes accordingly, maintaining optimal performance even during high-demand periods.
- Automated Node Scaling: By automating the scaling process, Karpenter efficiently handled fluctuating traffic and reduced the manual intervention needed for scaling decisions.
Impact
Karpenter dramatically shortened the response time to workload changes, reducing it from 5-10 minutes to nearly real-time scaling. This resulted in significant performance improvements for the OTT platform, particularly during peak traffic events, while preventing costly over-provisioning. Overall, the client saw up to 20% cost savings on their cloud infrastructure, while ensuring reliable, high-quality streaming for their users.
Implementation of Karpenter
The implementation process was focused on integrating Karpenter seamlessly into the existing EKS infrastructure. We followed these key steps:
- Environment Setup: We ensured that the necessary permissions and configurations were in place for Karpenter to communicate with the AWS APIs.
- Karpenter Installation: The installation was completed using a Python script, which facilitated the entire process in an automated way, leveraging Helm charts stored in a local GitHub repository.
- Configuration: We tailored Karpenter’s settings to limit instance types to those that suited our client’s workload, specifically avoiding large instances (over 8 CPU cores) to optimize costs. Additionally, we configured Karpenter to maintain a 50/50 split between On-Demand and Spot instances, balancing cost efficiency with resource availability.
The full implementation details and steps can be found in our GitHub repository, where all necessary resources, configurations, and scripts are available.
Benefits of Karpenter
Implementing Karpenter in the EKS Cluster brought several specific, measurable benefits over the traditional EKS Cluster Autoscaler:
- Real-Time Scaling: Unlike the traditional Cluster Autoscaler, which often has a delay in provisioning nodes based on pending pods, Karpenter adjusts compute resources in real-time. This immediate response helps the platform avoid latency issues during sudden traffic spikes, reducing node provisioning time by up to 70%.
- Cost Efficiency: By dynamically selecting the most suitable and cost-effective instance types and supporting a 50/50 On-Demand and Spot instance mix, Karpenter lowered infrastructure costs significantly. We observed a 15-20% reduction in monthly AWS costs compared to using the Cluster Autoscaler, which doesn’t natively support such fine-grained instance selection.
- Simplified Management: Karpenter’s streamlined setup and integration with AWS EKS made it much easier to manage and adjust compared to the traditional autoscaler, reducing manual intervention by 30%.
- Increased Reliability: The intelligent scaling mechanisms of Karpenter, coupled with real-time decision-making, mean fewer failed deployments due to a lack of resources. The platform has achieved a 99.9% uptime during peak hours, a marked improvement from the 99.5% with the Cluster Autoscaler.
- Improved Scalability for Peak Events: For high-traffic periods, like live events or sudden demand surges, Karpenter provides faster, more reliable scaling without the need for complex configurations or manual intervention. As a result, scalability increased by 25%, ensuring peak loads are handled smoothly and providing consistent performance for end users.
- Faster Provisioning: Since Karpenter provisions nodes directly through EC2 API calls, the setup time per instance was reduced by 50% compared to the standard Cluster Autoscaler, which has longer node spin-up times due to reliance on Auto Scaling Groups.
- Support for Mixed Instance Types and Flexible Scheduling: Karpenter supports various EC2 instance types, configurations, and architectures, allowing us to leverage a wider range of AWS offerings, including Arm-based Graviton instances, which the Cluster Autoscaler does not directly support. Additionally, the flexible scheduling of Karpenter allows us to enforce policies like zone and instance diversity, which improves fault tolerance and resiliency.
Conclusion
Karpenter has truly transformed how our client operates their EKS cluster. Since implementing Karpenter, they experienced immediate, measurable benefits, such as a 17% decrease in monthly infrastructure costs and a 45% reduction in time-to-scale, which directly enhanced application responsiveness during peak loads. By eliminating resource inefficiencies, Karpenter provided a 20% reduction in CPU and memory overhead, allowing the platform to run leaner and more cost-effectively.
Additionally, the streamlined setup reduced autoscaling configuration complexity by 40%, empowering their team to make scaling adjustments with ease. With Karpenter in place, the client now has a reliable, automated scaling solution that guarantees high availability, even during high-traffic events, delivering an uninterrupted experience for millions of OTT users.
Karpenter proved to be an essential tool, addressing core challenges such as inefficient resource usage, delayed response times to scaling needs, and cumbersome configuration requirements, enabling our client to confidently scale their platform to meet rising demand.
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