How To Migrate Stateful Workloads On Kubernetes With Zero Downtime

Kubernetes was built to avoid manually moving virtual machines and instead use “ephemeral” workloads. These workloads are usually stateless, easy to redeploy, and don’t depend on the underlying infrastructure. The idea was to let workloads be destroyed and recreated on new nodes, eliminating the complexity of scheduled migration and interruptions.

However, this approach fell short for stateful applications that require persistent data and long-running processes. 

A common example of stateful applications includes databases like MySQL, PostgreSQL, or NoSQL databases such as MongoDB, which store large amounts of structured data, providing fast access and retrieval. They’re essential for many business-critical systems, such as financial transactions, customer records, and inventory management. Interruptions to these workloads can lead to data corruption, inconsistencies, or complete system failure, making continuous uptime a priority.

Other types of stateful applications include jobs that cannot be interrupted, such as AI/ML model training or long-running simulations. These processes often involve extensive calculations that rely on intermediate states and datasets.

Stateful workloads can’t simply be stopped and restarted without risking data loss or interruption. This is why Kubernetes’ initial promise to simplify infrastructure for all workloads failed to meet the needs of complex, data-driven applications. 

What kind of challenges do Kubernetes teams encounter when running stateful workloads?

Dealing with Stateful Workloads in Kubernetes: 3 Challenges

Interruptions resulting in downtime

In cloud environments, workloads can be interrupted for several reasons, such as node failures, necessary upgrades or patching, or even resource pressure that causes the kubelet to evict pods. These interruptions can often lead to workload failure, resulting in downtime and potential loss of critical data or service availability. 

Stateful workloads require constant data synchronization and backup mechanisms, meaning that any disruption can lead to significant downtime or data loss. 

Low resource utilization

Certain workloads, such as single replica services, jobs, or applications backed by Persistent Volume Claims (PVCs), cannot be effortlessly migrated or interrupted. This limitation leaves clusters with fragmentation, where nodes are underutilized but can’t be consolidated due to the nature of these workloads.

How can Kubernetes teams reconcile these seemingly opposing demands to drive innovation and efficiency in their cloud strategies?

Cost optimization vs. performance

When it comes to stateful applications, Kubernetes teams often find themselves caught in a tug-of-war between optimizing cloud costs and ensuring the best performance. 

This dual challenge stems from the inherent complexity of managing stateful workloads, which require persistent storage and consistent network configurations. As organizations scale their operations, the costs associated with maintaining these resources can quickly escalate, leading to budget overruns and operational inefficiencies.

Teams appreciate the capabilities of stateful applications but dread the potential pitfalls they introduce in terms of cost and complexity. 

The key lies in balancing these competing priorities to ensure both cost-effectiveness and optimal user experience. 

What Is Live Migration For Stateful Applications?

Stateful applications retain user data across sessions, which presents unique challenges when it comes to migration. Downtime can lead to data loss, reduced user satisfaction, and financial losses. 

This is why live migration is critical – it ensures that stateful applications remain resilient and responsive by allowing seamless transitions from one node to another without interrupting the application’s operation. 

The capability of live migration is essential for maintaining high availability and reliability in Kubernetes environments while minimizing disruptions during updates or scaling operations.

Zero Downtime Or Low Cloud Cost: Achieve Both With Cast AI Container Live Migration

At Cast AI, we have been aware of the issues teams encounter when running stateful workloads. This is why we solved it using automation. With Cast AI Container Live Migration, these once-immovable workloads are now automatically consolidated into fewer nodes. This ensures continuous uptime, reduces resource fragmentation, and results in additional cost savings.

Seamless continuity during node interruptions

With Cast AI’s Container Live Migration, these evictions become seamless. Instead of the workload failing or having to restart, the solution automatically moves it to the next available node without disruption. 

This ensures uninterrupted operation for stateful and critical workloads, reducing the risk of failure and maintaining continuous service delivery despite the underlying infrastructure changes.

Boosting resource utilization for cost savings

Cast AI’s Evictor and Rebalancing components are essential tools for optimizing cluster resource usage by bin-packing workloads into fewer, more efficient nodes. 

Container Live Migration enables uninterrupted movement of stateful workloads between nodes, allowing the Evictor and Rebalancer to function across various workloads. 

With Container Live Migration, Cast AI customers can achieve even greater bin packing efficiency, significantly reducing node fragmentation and lowering cloud infrastructure costs. This leads to better resource utilization and cost savings by maximizing the impact of both the Evictor and Rebalancing features.

Extra cost savings thanks to Spot instances

Container Live Migration with existing features covering the entire Spot instance lifecycle, from provisioning and rightsizing to decommissioning or moving workloads to on-demand instances if no instances are available. 

This enables teams to confidently run stateful workloads on cost-saving Spot instances, knowing interruptions will be handled without service impact. 

Wrap up

Stateful workloads used to present a challenge to Kubernetes teams. With Cast AI Container Live Migration, stateful workloads that were previously unmovable are automatically packed into fewer nodes, ensuring continuous uptime, eliminating resource fragmentation, and driving additional cost savings.

Book a demo to see how this feature could help you manage stateful K8s workloads in your cluster.