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What Is Kubernetes Autoscaling?
Kubernetes autoscaling is the process of automatically adjusting resources in a Kubernetes cluster to match application demand. It helps optimize resource usage by scaling pods, nodes, or resource allocations up or down based on real-time metrics or predefined conditions. By automating this process, Kubernetes ensures that applications maintain high availability and performance while minimizing costs and manual intervention.
Autoscaling can occur at different levels, including pods, nodes, and container resources, each serving specific purposes. Kubernetes employs mechanisms like the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler to meet these needs. Advanced techniques, such as event-driven scaling and custom metric-based autoscaling, offer greater flexibility to handle varying workloads and unpredictable traffic patterns.
This is part of a series of articles about Kubernetes management.
Types of Kubernetes Autoscaling
1. Horizontal Pod Autoscaler (HPA)
HPA automatically adjusts the number of running pods in a Kubernetes deployment, ReplicaSet, or StatefulSet based on observed CPU utilization, memory usage, or custom application metrics.
How it works:
HPA continuously monitors pod metrics through the Kubernetes metrics server or external sources. When resource usage crosses predefined thresholds, it scales pods up or down accordingly. The scaling decision is based on the average resource consumption across pods.
Pros and cons:
✅ Ensures applications can handle fluctuating workloads
✅ Helps optimize resource usage dynamically
✅ Works well with stateless applications
❌ Scaling speed is limited by polling intervals
❌ Not ideal for stateful applications with complex dependencies
2. Autoscaling Pods Based on Custom Metrics
This method extends HPA by using custom-defined metrics, such as request latency, database connections, or business-specific KPIs, to determine scaling needs.
How it works:
Custom metrics are collected via Prometheus, Datadog, or other monitoring systems and fed into Kubernetes. HPA then scales workloads based on these metrics rather than just CPU or memory usage.
Pros and cons:
✅ Allows scaling based on application-specific performance indicators
✅ More accurate than CPU/memory-based scaling in some cases
✅ Works well for complex applications with unique scaling needs
❌ Requires additional monitoring infrastructure
❌ More complex to configure than standard HPA
❌ Requires explicit support from the applications monitored
3. Vertical Pod Autoscaler (VPA)
VPA automatically adjusts the resource requests and limits (CPU and memory) for individual pods instead of changing the number of pods.
How it works:
VPA analyzes historical resource usage and recommends or enforces updates to pod resource allocations. If a pod requires more resources, it is restarted with updated limits, ensuring optimal performance without over-provisioning.
Pros and cons:
✅ Reduces manual tuning of resource requests
✅ Prevents pods from running out of memory or CPU
✅ Works well for workloads with stable pod counts
❌ Causes pod restarts, leading to potential downtime
❌ Not ideal for scaling out applications
4. Cluster Autoscaler
Cluster Autoscaler adjusts the number of worker nodes in a Kubernetes cluster based on resource demand.
How it works:
It monitors pending pods that cannot be scheduled due to insufficient resources. If pods are waiting, cluster autoscaler provisions new nodes. It also removes underutilized nodes to save costs.
Pros and cons:
✅ Ensures workloads always have enough resources
✅ Reduces cloud costs by removing unused nodes
✅ Works well alongside HPA and VPA
❌ Scaling new nodes takes time, causing delays
❌ Not useful for on-premises clusters with fixed hardware
❌Incorrect configuration can increase costs of the cluster
5. Event-Driven Autoscaling with KEDA
Kubernetes Event-Driven Autoscaler (KEDA) scales workloads based on event-driven metrics such as queue length, Kafka topics, or external API requests.
How it works:
KEDA monitors external event sources and scales pods dynamically when predefined conditions are met. Unlike HPA, which mainly relies on CPU and memory, KEDA can trigger scaling based on real-time events.
Pros and cons:
✅ Enables fine-grained, real-time scaling based on external events
✅ Supports a wide range of event sources (message queues, databases, etc.)
✅ Reduces unnecessary resource consumption during idle periods
❌ Requires additional configuration for event sources
❌ Not suitable for workloads without event-based triggers
VP of Open Source, Octopus Deploy
Dan is a seasoned leader in the tech industry with a strong focus on open source initiatives. Currently serving as VP of Open Source at Octopus Deploy, contributing as an Argo maintainer, co-creator of Open GitOps, and leveraging extensive experience as a co-founder of Codefresh, now part of Octopus Deploy.
TIPS FROM THE EXPERT
In my experience, here are tips that can help you better implement Kubernetes autoscaling:
- Calibrate HPA sensitivity based on application behavior: Tuning HPA’s polling intervals and threshold margins can significantly improve autoscaling efficiency. Applications with frequent, short-lived spikes may need shorter polling intervals, while those with gradual traffic increases benefit from conservative scaling to avoid oscillations. Use experimentation to balance speed and stability.
- Use predictive scaling with machine learning: Integrate predictive autoscaling to anticipate demand before it occurs. Custom ML-based models can forecast usage trends based on historical data, ensuring that scaling occurs proactively, reducing latency during demand surges.
- Employ buffer nodes for fast scale-up: Cluster autoscaling may introduce delays as new nodes provision. To mitigate this, maintain a pool of pre-warmed buffer nodes that can be activated immediately when demand increases. Once stable, the cluster autoscaler can add long-term nodes.
- Consider pod disruption budgets to minimize downtime: When using VPA or cluster autoscaling, set pod disruption budgets to control how many pods can be evicted or restarted simultaneously. This minimizes downtime, ensuring availability during scaling events or resource reallocations.
- Optimize readiness probes for smooth scaling: Ensure readiness probes are fine-tuned to reflect real service availability rather than generic health checks. Misconfigured probes can cause premature traffic routing to pods still initializing, leading to failed requests during scale-ups.
Advanced Autoscaling Techniques
Autoscaling Stateful Applications
Autoscaling stateful applications presents challenges due to their need for data consistency and persistent storage. Techniques involve carefully managing instance replication to ensure data remains consistent and available. Kubernetes supports stateful workloads with tools like StatefulSets, which maintain stable network identifiers.
When scaling stateful applications, it’s essential to consider storage management and consistent data replication. Kubernetes provides building blocks like VolumeClaims and PersistentVolumes to support these requirements. Autoscaling must respect stateful application constraints, requiring thoughtful design and orchestration to avoid disruptions.
Combining HPA and VPA
Combining horizontal and vertical pod autoscaling offers a hybrid approach to resource management that can address both scale and resource efficiency. This combination can dynamically adjust pod counts and fine-tune resource requests, achieving optimal performance for various workloads.
The combination allows Kubernetes to handle diverse application resource demands efficiently. By enabling dynamic pod scaling (HPA) alongside automated resource allocation adjustments (VPA), organizations can achieve finely-tuned performance and cost-effective infrastructure management.
Autoscaling Based on Schedules
Autoscaling based on schedules allows Kubernetes to anticipate predictable demand patterns and adjust resources accordingly. This proactive approach is useful for applications with regular usage spikes, such as those timed with business hours or specified events. By automating these adjustments, organizations can optimize resource allocation and minimize costs.
Scheduled autoscaling involves setting predefined times to upscale or downscale resources. It eliminates the need for constant monitoring and manual adjustments. Leveraging Kubernetes’ native scheduling features helps achieve consistent performance while adapting infrastructure to known demand cycles.
Utilizing Custom Metrics APIs
Using custom metrics APIs enables Kubernetes to access specialized metrics beyond default CPU or memory usage statistics. By integrating these APIs, developers can implement sophisticated autoscaling strategies tailored to application needs, leveraging external monitoring tools to inform scaling decisions.
These APIs offer improved visibility into application performance and resource use, permitting precise adjustments. Custom metrics support a granular approach to autoscaling, aligning resource allocation with business objectives and operational priorities. This technique is particularly valuable for applications with unique demand profiles.
Related content: Read our guide to Kubernetes deployment strategy
5 Best Practices for Kubernetes Autoscaling
Here are some useful practices to consider when implementing autoscaling in Kubernetes.
1. Prioritize Monitoring and Visibility
Effective autoscaling begins with rigorous monitoring and visibility across all aspects of the Kubernetes environment. Ensuring that actionable insights are available aids in identifying performance bottlenecks and informs scaling decisions. Implementing comprehensive monitoring solutions forms the foundation of successful autoscaling strategies.
Proper visibility into application performance metrics enables administrators to set appropriate thresholds for scaling events. Detailed observability setups help diagnose issues quickly and provide data-driven insights, essential for proactive scaling and maintaining performance standards.
2. Test Autoscaling Configurations Iteratively
Testing autoscaling configurations iteratively allows for fine-tuning and validation of scaling policies. By performing controlled tests, organizations can assess how changes affect application performance and resource utilization, ensuring that autoscaling strategies are effective.
Iterative testing involves employing diverse scenarios to simulate different demand conditions. This approach helps identify potential shortcomings and optimize scaling parameters. Capturing these insights strengthens autoscaling frameworks, offering resilience and reliability in real-world operations.
3. Harmonize Cluster, Horizontal, and Vertical Autoscaling
Harmonizing cluster, horizontal, and vertical autoscaling ensures a balanced approach to resource management in Kubernetes environments. Integrating these autoscaling methods allows each layer to address scaling needs, improving overall system resilience.
Combining autoscaling strategies requires a holistic understanding of application workloads and system architecture. This alignment helps mitigate resource contention and ensures seamless scaling across all dimensions, maintaining application performance. Harmonization enables Kubernetes to utilize resources effectively, reducing downtime.
4. Optimize Resource Requests and Limits
Optimizing resource requests and limits is essential for efficient autoscaling in Kubernetes. Setting accurate resource parameters ensures that applications have what they need without over-provisioning, reducing waste and operational costs.
Precise resource allocation involves evaluating application requirements and historical performance data, enabling right-sizing of resource requests and limits. This optimization reduces resource contention and improves scalability, aligning Kubernetes infrastructure with business expectations.
5. Simplify Developer Experience with Automation
Automation simplifies the developer experience by reducing manual configuration and operational effort in deploying and managing autoscaling features. Implementing automated workflows simplifies development processes, enabling teams to focus on building high-quality applications.
By automating routine tasks associated with autoscaling, developers can expedite deployment pipelines and reduce human error. This approach improves overall productivity and efficiency, aligning development workflows with business objectives.
Related content: Read our guide to Kubernetes tools