How to Reduce Resource Waste with Kubernetes

Kubernetes has undoubtedly changed the way we deploy and scale applications. However, many organizations overlook a crucial aspect: optimizing resource utilization. While Kubernetes excels at scaling, monitoring and maximizing resource usage often falls on operations teams.

This oversight can lead to significant financial implications. A recent report showed startling figures about resource waste in Kubernetes environments, revealing that for customers running over 1000 nodes, potential savings could exceed $10 million annually. CPU resources, the most expensive component of cloud instances, are particularly prone to underutilization.

Even organizations with around 150 Kubernetes nodes could overspend by up to $980,000 per year due to inefficient CPU usage, demonstrating why it’s critical to know how to reduce resource waste with Kubernetes.

What is Kubernetes?

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. It provides a resilient framework for running distributed systems, allowing you to scale your applications and seamlessly roll out updates.

How are resources allocated in Kubernetes?

The kube-scheduler uses a two-step process of filtering and scoring to make its decisions:

Filtering: First, the scheduler filters out nodes that don’t meet the pod’s basic requirements. This includes checking if the node has enough CPU and memory resources, respects any node affinity rules, and meets any other constraints specified in the pod definition.

Scoring: After filtering, the remaining nodes are scored based on a set of priority functions. These functions consider factors like the number of resources allocated on the node, whether the node and pod are in the same failure domain, and any preferred node affinity rules. The node with the highest score is then selected for the pod.

This approach allows Kubernetes to make intelligent decisions about pod placement.

What is Kubernetes resource management?

Kubernetes resource management involves efficiently allocating and utilizing compute resources (CPU and memory) across the cluster. It includes specifying resource requests and limits for containers, setting up resource quotas for namespaces, and configuring the kube-scheduler to make intelligent decisions about pod placement.

The kube-scheduler plays a crucial role in resource management. It optimizes pod placement based on node capacity, current resource usage, and any specific constraints or affinities when deciding where to place pods.

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3 Key Challenges of Reducing Resource Waste with Kubernetes

Effectively managing resource consumption within the dynamic environment presents several key challenges.

1. Lack of Meaningful Visibility

Understanding resource utilization goes beyond having fancy graphs and beautiful dashboards. While these tools are important, gaining meaningful insights is something many organizations struggle with. Although some solutions like WAF tools integrated with Kubernetes provide built-in reporting features, gaining insight into resource waste is not as straightforward.

This gap between perceived importance and actual implementation maturity indicates that many organizations are flying blind when it comes to truly understanding their resource usage. Without deep, actionable insights, it’s challenging to identify and address resource waste effectively.

2. Multi-component Complexity

The multi-component nature of Kubernetes workloads means that nodes are not the only place where resource waste can occur. For example, you might be running more control plane nodes than necessary for your scale. A small cluster might only need one control plane node, but organizations sometimes over-provision, running two or three, leading to unnecessary resource consumption.

This complexity extends to other components like etcd clusters, ingress controllers, and monitoring systems. Each component needs to be right-sized for your specific needs, and failing to do so can result in significant resource waste across the cluster.

3. Poor Application Instrumentation

Without proper application instrumentation, resource allocation becomes a high-stakes guessing game. If you’re unsure how much resources your application uses, you might over-provision to ensure performance, leading to waste. Accurate application-level metrics are crucial for setting appropriate resource requests and limits. Without this data, teams often resort to rough estimates or generous over-allocation, both of which can lead to significant resource waste across the cluster.

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Why is it important to reduce Kubernetes resource waste?

Cost Optimization

Resource waste in Kubernetes environments directly translates to unnecessary cloud spending. Even organizations with moderate-sized clusters could be overspending by nearly $1 million annually due to underutilized resources. For larger enterprises, this figure can balloon to over $10 million. Companies can significantly cut their cloud bills and improve their bottom line by reducing resource waste.

Environmental Impact

Data centers account for about 1% of global electricity consumption, with this figure projected to rise. Efficient resource utilization in Kubernetes clusters isn’t just a financial concern—it’s an environmental one. Organizations can reduce their carbon footprint, contributing to broader sustainability goals.

Performance and Scalability

Efficient resource utilization isn’t just about saving money—it’s about optimizing performance. When resources are allocated more efficiently, you can run more workloads on the same infrastructure, improving overall cluster utilization. It leads to better scalability, allowing your infrastructure to grow more cost-effectively as your needs increase.

6 Ways to Reduce Resource Waste With Kubernetes

The following are six actionable strategies to optimize your Kubernetes environment and minimize unnecessary costs.

1. Leverage Spot Instances

Major cloud providers offer variations of spot instances (called preemptible VMs on GCP), which can significantly reduce costs. These instances are available at a fraction of the price of regular instances but can be reclaimed by the provider with short notice.

To implement this effectively, use spot instances for fault-tolerant, stateless workloads. Implement proper handling of termination notices and consider using tools like Spot.io or AWS Spot Fleet to manage spot instances efficiently.

Additionally, consider setting up Pod Disruption Budgets (PDBs) for applications running on spot instances. A PDB limits the number of pods of a replicated application that are down simultaneously from voluntary disruptions.

Defining a PDB ensures that a certain number or percentage of pods remain available even when spot instances are reclaimed. For example, you might set a PDB that requires at least 70% of your application’s pods to be available at all times. This way, even if some spot instances are reclaimed, your application continues functioning with minimal disruption.

2. Profile Your Applications

Before adding that extra node to your cluster, understand where your application’s bottleneck is. Profiling can reveal whether you’re facing a resource constraint or an application-level issue. To gain insights into your application’s performance, utilize open-source tools like pprof for Go applications or cProfile for Python.

Complement this with distributed tracing tools like Jaeger or Zipkin to understand system-wide performance. By analyzing both application and infrastructure metrics, you’ll get a complete picture of your resource utilization and can make informed decisions about scaling or optimization.

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3. Implement HPA and VPA

Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) are powerful tools for dynamically adjusting resources based on demand.

Use HPA when your application can benefit from running multiple instances, and implement VPA when your resource needs fluctuate but can’t be scaled horizontally. Consider using both in tandem for optimal resource allocation. This dynamic approach ensures your applications have the resources they need without over-provisioning.

4. Monitor Wisely

While monitoring is indispensable, over-instrumentation can lead to noise that distracts from cloud cost optimization goals. Focus on key performance indicators (KPIs) directly related to resource usage and application performance.

Use tools like Prometheus and Grafana, but be selective about what you monitor. Regularly review and prune unnecessary metrics to keep your monitoring system lean and focused. This approach will help you maintain clarity on your resource utilization and optimization efforts.

5. Stop Before You Autoscale

Autoscaling and adding more nodes aren’t always the answer. Sometimes, the bottleneck might be in your application itself.

Before deciding to scale, conduct thorough application profiling and look for opportunities to optimize code or database queries. Consider if architectural changes (like implementing caching) could solve the issue more efficiently than scaling. This thoughtful approach can often lead to more sustainable and cost-effective solutions than simply throwing more resources at the problem.

6. Use Control Plane

Control Plane is a game-changing Internal Developer Platform (IDP). Utilize Control Plane’s unified view to identify resource waste across your entire infrastructure. You can leverage our open-source K8s cost savings calculator to quantify potential savings when running workloads on the Control Plane platform versus your cloud provider. Use these insights to make data-driven decisions about resource allocation and optimization, ensuring you’re maximizing the efficiency of your Kubernetes deployments.

Placing the Right Blocks With Control Plane

Like Tetris, efficient resource management in Kubernetes is all about smart placement and continuous optimization. This article has explored the challenges of Kubernetes resource waste and provided strategies to combat it, from leveraging spot instances to implementing autoscalers and wise monitoring practices.

But why stop there? Control Plane helps you master the game of cloud cost reduction, potentially slashing expenses by 60-80%. Like a well-placed piece in Tetris, Control Plane elegantly eliminates the need for costly services such as NAT Gateways, Internet Gateways, Load Balancers, and even extra Kubernetes control plane nodes. Get started with Control Plane and ensure you only ever pay for what you use.