Kubernetes Persistent Volumes: Scaling Strategies

Kubernetes has made managing containerised applications easier, but scaling stateful workloads like databases or file systems remains challenging. These workloads require persistent storage that maintains data integrity during pod restarts or failures. Traditional scaling often ignores storage needs, leading to issues like service interruptions or overprovisioning, which wastes resources and increases costs.

Recent advancements, including Persistent Volume Autoscalers (PVAs) and dynamic provisioning, address these challenges by automating storage scaling. These tools help reduce downtime, improve efficiency, and simplify administration. Key strategies include:

• Dynamic provisioning: Automatically creates storage when needed, reducing manual effort and costs.
• Volume expansion: Allows resizing of storage without disruption.
• PVC autoscaling: Automatically adjusts Persistent Volume Claims (PVCs) based on usage thresholds.
• Autoscalers integration: Combines tools like Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) to align storage and compute scaling.

Efficient scaling also involves monitoring usage, right-sizing volumes, and using snapshots for backups. Tools like Prometheus and Grafana can track storage metrics, while resource quotas ensure balanced resource use. By combining these strategies, organisations can optimise costs and improve storage reliability for stateful Kubernetes workloads.

How to Scale and Create a StatefulSet - Civo Academy

Main Scaling Strategies for Persistent Volumes

As Kubernetes continues to evolve, managing persistent volumes efficiently has become a key focus. Recent advancements in scaling strategies have made it easier to reduce downtime and optimise costs. By using automated provisioning, smart expansion methods, and coordinated resource management, organisations can handle storage demands more effectively.

Dynamic Provisioning and Volume Expansion

Dynamic provisioning has changed the way storage is managed by removing the need for manual pre-allocation. Instead of requiring administrators to create storage resources ahead of time, Kubernetes automatically provisions persistent volumes whenever applications request them through PersistentVolumeClaim (PVC) objects.

Dynamic volume provisioning allows storage volumes to be created on-demand... It automatically provisions storage when users create PersistentVolumeClaim objects. [3]

This process relies on StorageClass objects, which are configured with the necessary parameters to enable on-demand provisioning. This approach not only simplifies storage management but also helps control costs as storage needs fluctuate [1].

Volume expansion takes this a step further by allowing users to resize existing volumes without the hassle of recreating them. To use this feature, the allowVolumeExpansion field must be set to true in the StorageClass configuration. Users can then edit their PVC object to request more storage space. Introduced in Kubernetes v1.11, this feature has reached a level of maturity suitable for production environments [4]. By automating what was once a manual process, volume expansion saves time and reduces errors.

In environments where multiple applications share the same underlying storage, dynamic provisioning ensures that each Persistent Volume has its own access point, maintaining isolation between applications [5]. These automated features lay the groundwork for further enhancements, such as PVC autoscaling.

Automating PersistentVolumeClaims (PVCs)

Scaling PVCs automatically is a game-changer for Kubernetes environments, addressing one of the most common operational pain points. The PVC Autoscaler project offers a practical solution for organisations looking to eliminate manual storage adjustments.

PVC Autoscaler is an open-source project aimed at providing autoscaling functionality to Persistent Volume Claims (PVCs) in Kubernetes environments. [6]

This tool works with StorageClasses that have the allowVolumeExpansion: true setting. By using annotations, users can configure scaling thresholds - such as expanding storage by 20% when usage reaches 80% capacity, up to a defined limit of 20Gi [8]. Combining this automation with resource quotas ensures that no single application consumes excessive storage, maintaining a balanced and scalable system [8].

When integrated into Kubernetes' broader autoscaling ecosystem, these PVC automation capabilities significantly enhance storage reliability and efficiency.

How Autoscalers Help Manage Persistent Volumes

The combination of dynamic provisioning and PVC automation pairs seamlessly with Kubernetes autoscalers, creating a robust scaling system for managing persistent volumes. Tools like the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler each contribute to maintaining resource alignment with application demands.

HPA is particularly useful for stateless applications, as it scales horizontally by adjusting the number of pod replicas. When used alongside persistent volumes, HPA ensures that storage resources are available as application instances grow. However, for HPA to function effectively, all pods must have resource requests configured to enable accurate scaling decisions [7].

For stateful workloads, VPA and the Cluster Autoscaler offer tailored solutions. VPA adjusts the CPU and memory requests and limits for individual pods, making it ideal for stateful applications like databases that rely on persistent volumes [9]. Meanwhile, the Cluster Autoscaler dynamically adjusts the cluster size based on pod demands, provisioning additional nodes when workloads require more capacity.

Research highlights inefficiencies in many Kubernetes deployments, with only 20–30% of CPU and 30–40% of memory typically being utilised [9]. Proper configuration of autoscalers helps address these inefficiencies, ensuring resources are allocated based on actual needs.

To maximise the effectiveness of autoscalers, best practices include combining HPA with the Cluster Autoscaler to align pod scaling with node behaviour. However, avoid using HPA and VPA simultaneously for the same pod sets unless HPA relies on custom or external metrics [7]. Additionally, the Cluster Autoscaler requires at least one CPU for its resource requests to ensure it remains operational during scaling.

For organisations seeking to fine-tune these strategies, Hokstad Consulting offers expertise in DevOps transformation and cloud cost management, helping to optimise autoscaler configurations for reduced costs, faster deployment cycles, and improved system reliability.

Cost Control and Storage Efficiency

When it comes to scaling Kubernetes environments, managing costs effectively is just as crucial as ensuring operational efficiency. Persistent volume management plays a key role here, as it directly impacts both performance and financial sustainability. Studies reveal that many Kubernetes clusters suffer from significant overprovisioning, which opens up opportunities to cut waste through better monitoring, automation, and smarter storage practices [11].

Right-Sizing Volumes and Preventing Overprovisioning

Overprovisioning is one of the biggest culprits driving up costs in Kubernetes environments. Jesse Houldsworth from nOps explains:

Overprovisioning in Kubernetes (alternatively known as excess capacity, resource waste, underutilisation, etc.) occurs when your deployments' requested resources (CPU or Memory) are significantly higher than what you actually use. [11]

For stateful applications, overprovisioning rates can vary widely - 15% in R&D environments, 25% in cost-sensitive production setups, and up to 35% in performance-focused configurations [11].

The solution? Historical usage data is your best guide. For example, p95 metrics work well for stateless applications, p98 for batch jobs, and maximum values for mission-critical workloads [11]. Tools like Prometheus and Grafana make it easier to monitor and visualise resource usage, while namespace-level ResourceQuotas help ensure no single application hogs cluster resources [11].

Using Snapshots for Backup and Disaster Recovery

Once volumes are right-sized, organisations can save even more by adopting efficient backup strategies. Volume snapshots provide a cost-effective way to safeguard data, but they need to be managed wisely to avoid racking up unnecessary expenses.

Strategies like storage tiering and incremental backups can significantly reduce the long-term costs of snapshots. Automating retention policies - using tools like Velero's ttl parameter - ensures outdated backups are automatically deleted [12]. Additionally, adjusting backup frequency to match business needs (e.g., switching to daily or weekly backups for non-critical workloads) can yield substantial savings [12]. This approach ensures strong data protection without the high costs of frequent manual backups.

Performance Tuning for Scalable Storage

Optimising storage performance goes hand-in-hand with cost control. Techniques like deduplication and compression can shrink the overall storage footprint while maintaining performance - especially in environments where data patterns are repeated across multiple volumes [2].

Choosing the right storage class for specific workloads is another key factor. This ensures a balance between performance and cost, helping to avoid both underperformance and overspending [10].

Here’s a quick summary of best practices for optimising Kubernetes costs and improving storage efficiency:

Regular audits and automated cleanups of orphaned persistent volumes can also help minimise costs while improving overall efficiency [10].

For businesses looking to fine-tune their storage costs and performance, Hokstad Consulting offers expert services in cloud cost management and DevOps transformation. Their tailored approach focuses on reducing cloud expenses through smarter resource management, automated monitoring, and optimised persistent volume strategies.

Comparing Different Scaling Methods

Selecting the best scaling method for Kubernetes persistent volumes hinges on understanding the trade-offs between available options. Each method has its strengths, depending on your organisation's needs, operational readiness, and available resources.

At the heart of the decision lies a balance between control and automation. Manual scaling provides oversight but demands constant attention, while automated scaling responds swiftly yet introduces additional layers of complexity. Similarly, static provisioning offers predictability but risks resource waste, whereas dynamic provisioning ensures efficient use of storage by creating it on demand. Below is a breakdown of these approaches and their trade-offs.

Scaling decisions play a crucial role when managing persistent volumes at scale. Poor choices can lead to performance bottlenecks and unnecessary expenses. On the other hand, dynamic scaling - whether of resources or node pools - can help optimise costs while boosting performance [16].

Comparison Table: Manual vs Automated Scaling, Static vs Dynamic Provisioning

To strike the right balance between efficiency and cost, it’s essential to understand how these approaches compare across key factors:

Manual Scaling

Manual scaling involves hands-on management through tools like kubectl or by applying configuration patches [13]. It suits organisations with predictable workloads and limited operational complexity. However, as clusters grow in size and complexity, manual scaling becomes less practical and harder to sustain.

Automated Scaling

Automated scaling uses tools such as the Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) to adjust resources dynamically based on live metrics [13]. HPA is particularly effective for stateless applications, while VPA is better suited for workloads with stable pod numbers [15]. It's worth noting that using HPA and VPA together is discouraged unless HPA is configured to rely on custom or external metrics [7]. For effective implementation, consider adjusting HPA polling intervals and threshold margins to align with your application's behaviour [15]. Predictive scaling methods, which leverage machine learning, and buffer nodes can further enhance performance during sudden scaling events [15].

Static Provisioning

Static provisioning requires administrators to allocate storage volumes manually before they are needed by pods [14]. While this method ensures consistency, it often results in underutilised resources if the allocated storage doesn't align perfectly with actual needs. Static provisioning works best in environments with predictable and consistent storage requirements.

Dynamic Provisioning

Dynamic provisioning automates storage creation as needed, eliminating the need for pre-allocation [3]. This approach is highly efficient but requires careful configuration of the StorageClass and active monitoring to avoid unexpected costs. It's ideal for scenarios where storage demands fluctuate and flexibility is key.

For organisations adopting automated scaling, fine-tuning configurations and exploring predictive scaling can make a significant difference. These strategies not only improve efficiency but also help mitigate the risks of over-provisioning or performance dips during peak demand.

Summary and Implementation Recommendations

Key Points from Research and Strategies

Persistent Volume scaling can be a tricky balancing act between cost and performance. However, organisations that excel in this area tend to focus on three main principles: automation, right-sizing, and continuous monitoring.

Automation works best when combined with optimised storage classes, resource quotas, and effective monitoring. Successful strategies often include using the Horizontal Pod Autoscaler (HPA) for load-based scaling, adopting a microservices architecture to scale specific components under pressure, and implementing comprehensive resource management to avoid unnecessary system-wide expansion.

Practical Recommendations for Businesses

To address these challenges and implement effective solutions, consider the following measures:

• Optimise and automate storage provisioning: Configure your storage classes with appropriate replication factors and backup schedules. Enabling dynamic provisioning allows storage to scale automatically based on actual demand, reducing the need for manual intervention and guesswork.

• Set up robust monitoring systems: Tools like Prometheus and Grafana can help track storage usage, performance metrics, and cost trends. Regularly audit for unused or orphaned volumes to avoid unnecessary charges and improve efficiency.

• Establish resource quotas and limits: Define clear limits on PersistentVolumeClaims to prevent overspending and ensure fair distribution of storage resources across your cluster. This helps maintain budget control while meeting operational needs.

• Secure your storage infrastructure: Use encryption for data both at rest and in transit, implement strict access controls, and utilise volume snapshots for backups. These measures not only protect your data but also ensure compliance with regulatory requirements.

For businesses looking to streamline these processes, Hokstad Consulting offers valuable expertise in cloud cost engineering and DevOps transformation. Their services focus on optimising cloud infrastructure costs and aligning with the principles of right-sizing and monitoring. Whether it's through strategic cloud migration or custom automation solutions, they can help organisations adopt scalable, cost-efficient Persistent Volume strategies.

The real secret to success lies in treating Persistent Volume scaling as an ongoing process. Regularly review your storage usage, fine-tune performance, and analyse costs to ensure your strategy adapts to your business's evolving needs. This continuous approach is what makes Kubernetes a powerful tool for managing stateful applications effectively.

FAQs