The complexity of K8s management is no surprise for teams, particularly those managing mid or large-scale environments. Optimizing Kubernetes may present a significant challenge that requires a multi-dimensional strategy involving continuously right-sizing workloads to match actual demand while fine-tuning the underlying infrastructure. Such an approach allows teams to keep cloud expenses under control while maintaining performance and resilience.
“Node optimization is challenging, especially when teams lack visibility into workload behavior, scheduling patterns, or how autoscalers, such as Karpenter or Cluster Autoscaler, actually work,” said Eli Birger, CTO of PerfectScale by DoiT. “ Without accurate, workload-specific insights, teams often rely on generic monitoring tools and manual guesswork, which often leads to costly inefficiencies, compromised resilience, and fragile infrastructure.”
To address these challenges, PerfectScale is excited to announce the release of data-driven node recommendations for users. This level of depth helps manage Kubernetes infrastructure efficiently, freeing teams to focus on what matters.
Still with me? Great, let’s dive in!
What are node recommendations?
Node Recommendations is a powerful feature designed to simplify selecting the right node types for your Kubernetes environment, providing actionable, data-driven insights based on the continuous analysis of the historical workload behavior, resource usage patterns, and scheduling trends. This feature allows you to easily identify the best node for your workloads within a node group, ensuring that they run smoothly and efficiently.
This view provides a clear estimate of the potential cost savings achievable by applying the recommendations, with insights enabling more accurate forecasting, informed decision-making, and helping you seamlessly align optimization efforts with your budgeting and cost management goals.
Node recommendations in action
Let’s dive into the best practices we've collected to help you get the most out of node recommendations and drive effective optimization outcomes.
Get holistic cost optimization
PerfectScale automation combined with node recommendations creates a robust solution that delivers significant cost savings and increased cluster efficiency.
It all starts with workload right-sizing. In fact, node optimization will never yield the desired results if we are running unoptimized pods on them. Setting up PerfectScale automation enables continuous autonomous workloads right-sizing based on their actual resource consumption, which ultimately improves pod bin-packing and allows for reducing the size of the environment. All with no manual intervention!
In parallel, PerfectScale node recommendations allow you to make accurate and informed decisions when choosing nodes to support the needs of your services. This feature provides the most cost-effective instance types for your workloads and shows the projected cost impact, enabling you to evaluate the expected optimization results at a glance.
Ensure resilient infrastructure
PerfectScale Infrafit provides a comprehensive view of your Kubernetes infrastructure by continuously analyzing clusters and historical workload behavior. It identifies resource utilization patterns, highlighting undersized nodes that may compromise the system's performance and reliability, and provides you with actionable recommendations to address the issue.
By leveraging the recommendations, you can effectively eliminate associated risks and ensure your infrastructure remains resilient and aligned with your workloads' demands.
It is now time to make confident, impact-driven decisions that align infrastructure efficiency and reliability requirements with your budget goals, eliminating guesswork and prioritizing what really matters.
Verify node-autoscaling efficiency
PerfectScale continuously analyzes your node scaling, for example, Cluster Autoscaler, identifying inefficiencies and providing configuration recommendations to overcome its limitations and drive efficiency.
Like other node scaling solutions, the Cluster Autoscaler bases its decisions on the manually defined resource requests of pods rather than their actual usage. When a pod is pending, it checks for an available node, and if no appropriate node is found, it scales out. However, this approach is not able to detect underutilized node capacity, often resulting in significant resource waste. Additionally, the Cluster Autoscaler operates within the boundaries of pre-defined node group specifications, limiting its ability to optimize across different instance types or configurations, which may not be ideal for the workloads running in the cluster.
With PerfectScale’s recommendations, you can autonomously right-size your pods based on actual resource consumption and choose the most efficient node types to match the requirements of your workloads. This approach maximizes your node autoscaling outcome, ensuring the efficiency of your clusters.
Ready to see it in action? Head over to our Documentation for the complete guide, or book a session with our technical support team for a walkthrough of the platform.
Not using PerfectScale yet? Give it a try for free and start simplifying your Kubernetes optimization journey.
