Cluster Autoscaler vs. Karpenter: A Deep Dive into Kubernetes Auto-Scaling

Introduction

One major trend in Kubernetes infrastructure over the past few months has been the shift from Cluster Autoscaler (CAS) to Karpenter. Organizations are making this move to enhance efficiency, optimize costs, and improve scheduling capabilities in their clusters. While both tools serve the purpose of scaling worker nodes dynamically, they do so with fundamentally different architectures and approaches. I have tried to break down the use cases and provide an in-depth comparison of Cluster Autoscaler and Karpenter, their benefits, challenges, and why Karpenter is emerging as the preferred choice for modern Kubernetes workloads.

Understanding Cluster Autoscaler (CAS)

Cluster Autoscaler (CAS) is a well-established tool that scales Kubernetes clusters by adjusting the size of underlying cloud provider auto-scaling groups (ASGs). It monitors pending pods and resizes the node pool accordingly, adding or removing nodes when necessary.

How CAS Works

1. Pod Scheduling Requests: CAS continuously watches for pods that fail to schedule due to resource constraints.

2. Node Expansion: If unschedulable pods are detected, CAS modifies the ASG to launch new instances.

3. Node Removal: If nodes become underutilized and eligible for scale-down, CAS terminates them.

4. Integration with Cloud Providers: CAS interacts with cloud provider APIs (such as AWS EC2 Auto Scaling Groups) to manage scaling events.

Challenges with CAS

• Multi-Zone ASGs and Persistent Volumes: CAS struggles with multi-zone auto-scaling groups when dealing with Persistent Volumes (PVs) that have zonal requirements. Since CAS relies on ASGs, it does not inherently factor in PV placement constraints, leading to scheduling inefficiencies. A PV is typically bound to a specific availability zone (AZ). If CAS scales the ASG by launching nodes in a different AZ, the new nodes may be unable to access the PV, leaving pods in an unschedulable state.

• Static Instance Types: CAS is constrained by the instance types defined in the ASG, which means organizations must predefine the instances they want to scale with.

• Latency in Scale-Up: CAS follows a polling-based approach, making it slower in reacting to real-time scaling needs.

• Underutilized Nodes: CAS operates on an entire node group, which may lead to over-provisioning, as scaling decisions are based on the predefined node group settings.

Karpenter: The Next-Generation Kubernetes Autoscaler

Karpenter is a node provisioning system that offers a more flexible and efficient way of scaling Kubernetes workloads. Unlike CAS, Karpenter does not rely on ASGs but instead interacts directly with the cloud provider’s compute resources, enabling real-time, dynamic scaling with greater efficiency.

How Karpenter Works

1. Monitoring Pending Pods: Karpenter continuously watches for unschedulable pods.

2. Node Provisioning: Instead of scaling pre-defined ASGs, Karpenter provisions the optimal compute capacity required to accommodate pending workloads.

3. Node Deprovisioning: Karpenter actively consolidates workloads and removes underutilized nodes to reduce costs.

4. Real-Time Decisions: Karpenter makes near-instantaneous decisions by integrating directly with the cloud provider’s API (e.g., AWS EC2 Fleet API for instance provisioning).

Advantages of Karpenter

• Improved PV-Aware Scheduling: Karpenter integrates directly with the Kubernetes scheduler to consider zonal constraints when provisioning nodes. This ensures that pods requiring a Persistent Volume (PV) bound to a specific zone are always scheduled appropriately.

• Flexibility in Instance Selection: Karpenter dynamically selects the best instance type based on real-time workload needs, rather than being restricted to a fixed set of ASG instance types.

• Faster Scale-Up: Karpenter provisions nodes in real-time, reducing the latency associated with CAS's polling mechanism.

• Cost Efficiency: By consolidating workloads and dynamically selecting the most cost-effective instances, Karpenter reduces infrastructure costs.

• No ASG Dependence: Unlike CAS, Karpenter does not require predefined auto-scaling groups, allowing for greater flexibility in scaling strategies.

Real-World Challenges and Solutions

Persistent Volumes (PVs) and Zonal Constraints

One of the major pain points organizations face with CAS is scheduling pods that require zonal Persistent Volumes (PVs). When a PV is created, it gets bound to a specific availability zone, which can cause scheduling failures if the autoscaler provisions a node in a different zone. CAS struggles with this, especially in multi-zone ASGs, because it does not natively consider PV placement during node provisioning.

How Karpenter Solves This:

• Karpenter detects pod storage requirements and provisions nodes in the correct zone, ensuring proper PV attachment.

• It eliminates cross-zone scheduling failures by factoring in zonal affinity during provisioning.

Scaling Speed and Efficiency

CAS follows a reactive approach, polling the cluster for unschedulable pods and then adjusting the ASG size accordingly. This delay can impact workloads that require fast, on-demand scaling.

How Karpenter Solves This:

• Karpenter uses event-driven scaling, making near-instantaneous decisions based on pod scheduling needs.

• It interacts directly with EC2 Fleet APIs to provision nodes, bypassing the ASG constraints.

Cost Optimization

Organizations using CAS often face inefficiencies due to over-provisioning or underutilized instances. CAS scales entire node groups based on predefined configurations, which may not always align with real-time demand.

How Karpenter Solves This:

• Karpenter consolidates workloads dynamically, reducing the number of running nodes without impacting performance.

• It selects cost-effective instance types in real-time, rather than sticking to predefined ones.

Migration from CAS to Karpenter

Given these advantages, many organizations are transitioning from CAS to Karpenter. The migration involves the following steps:

1. Disabling CAS: Ensure that CAS is no longer managing the node pool to avoid conflicts.

2. Deploying Karpenter: Install Karpenter in the cluster and configure the provisioning policies.

3. Defining Node Templates: Specify requirements such as instance types, zones, and storage constraints.

4. Testing Scaling Behavior: Validate that Karpenter provisions nodes correctly for various workloads.

5. Optimizing Cost and Performance: Monitor and tweak Karpenter configurations for optimal efficiency.

Karpenter represents the next evolution in Kubernetes autoscaling, addressing many of the limitations of Cluster Autoscaler. Personally I refer to Karpenter as CA on steroids, because of its real-time decision-making, dynamic instance selection, and improved handling of Persistent Volume constraints, it provides a more flexible and cost-effective solution. While CAS remains a viable option for simpler workloads, organizations looking to enhance efficiency and scalability are increasingly adopting Karpenter for their Kubernetes environments, and I think you should too.