Optimizing AWS EKS with Karpenter: A Hybrid Instance Strategy

As Kubernetes adoption grows, so does the complexity of managing clusters efficiently. One common challenge is choosing the right EC2 instance types that balance performance, cost, and availability. Enter Karpenter, AWS’s open-source cluster autoscaler, designed to simplify and optimize Kubernetes infrastructure provisioning.

In this blog post, How Kubelancer, we implemented hybrid instance strategy using Karpenter on AWS EKS Cluster for our esteemed client, ton maximize performance while minimizing costs in production, if non-prod can think about spot fleet.

Generally Karpenter addresses these challenges:

1. Rapid provisioning: Instantly launching nodes when needed.

2. Cost optimization: Choosing the most cost-effective instance types.

3. Workload-aware scaling: Allocating resources based on real-time workload requirements.

4. Support for Spot Instances: Reducing costs further by leveraging EC2 Spot Instances.

But how can we unlock its full potential? Hybrid instance strategies hold the key.

The Challenge: Cost vs. Performance in Kubernetes

Kubernetes workloads vary in their resource requirements. Some applications are CPU-intensive, requiring high compute power, while others are memory-heavy, needing more RAM to function efficiently. If you stick to a single instance type, you may either:

1. Over-provision of resources, leading to wasted costs.
2. Under-provision resources, resulting in performance issues.

Let us Understanding Hybrid Instance Strategies

A hybrid instance strategy involves mixing different EC2 instance families based on workload needs. For example:

• Compute-optimized instances (c7i.large): Ideal for CPU-heavy applications.
• General-purpose instances (m7i.large): Perfect for balanced workloads requiring a blend of CPU and memory.

By blending these instances, you avoid over-provisioning and ensure that each workload gets precisely the resources it needs.

Our Workload Analysis: (example 3 microservices)

Imagine managing three critical microservices in an EKS cluster:

1. service-order

CPU Usage: High (1819m per pod)

Memory Usage: Low (~1.3 GiB per pod)

Best Fit: c7i.large for compute efficiency.

2. service-user

CPU Usage: Moderate (1830m per pod)

Memory Usage: Moderate (~2.4 GiB per pod)

Best Fit: c7i.large to optimize for CPU-bound processes.

3. service-search

CPU Usage: High (1805m per pod)

Memory Usage: Moderate (~3.7 GiB per pod)

Best Fit: m7i.large for a balanced approach.

Why did Kubelancer, chose the Hybrid Approach Works?

By mixing c7i.large and m7i.large instances, we will:

1. Reduce Costs: Compute-optimized instances are cheaper for CPU-heavy tasks.
2. Right-Size Resources: Memory-heavy applications get more memory-rich instances without wasting CPU capacity.
3. Ensure High Availability: Multiple instance types provide fault tolerance and flexibility during provisioning.

Performance & Cost Results

After we implemented this hybrid strategy:

• Resource Efficiency: Workloads are provisioned based on exact resource needs.
• Cost Savings: Reduced over-provisioning leads to lower monthly bills.
• Performance Gains: High CPU workloads get dedicated compute-optimized nodes.
• Flexibility: Karpenter’s dynamic scaling ensures adaptability to traffic spikes.

Karpenter, Every DevOps Engineer Infra Game Changer:

After we implemented the Hybrid Instance Strategy for AWS EKS powered by Karpenter, it is a game-changer for our microservices cloud-native applications. we achieved cost savings, better resource utilisation, and enhanced performance.

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