Disaster Recovery Strategies for Stateful Virtual Machines on OpenShift
In this blog, we will learn about disaster recovery strategies for stateful virtual machines on OpenShift.
As enterprises increasingly migrate critical workloads to Kubernetes platforms, ensuring robust disaster recovery (DR) for stateful virtual machines (VMs) has become a vital aspect of business continuity planning. Unlike stateless containers, which can be easily redeployed without much concern for underlying storage, most enterprise VM workloads are inherently stateful. These workloads depend on persistent block storage that must remain intact and accessible even after a failure, restart, or migration. This blog highlights the fundamental concepts and challenges associated with implementing DR for VMs on Red Hat OpenShift.
Understanding the Basics
For the purpose of this discussion, disaster recovery refers to minimizing disruptions to business services during a site-wide outage, which requires restoring operations at an alternate site. However, DR plans are not limited to full site failures—they are also critical for addressing component failures, where specific subsystems fail and affect only a subset of business applications.
Two key metrics underpin any DR strategy:
Recovery Point Objective (RPO): The longest time interval of potential data loss that an organization can tolerate following a disruption.
Recovery Time Objective (RTO): This defines the maximum amount of time that a service can remain unavailable without causing significant harm to the organization.
It is also important to differentiate between the two common DR approaches:
Metro-DR: Leverages synchronous data replication across closely located data centers, allowing for zero data loss (RPO) due to minimal latency.
Regional-DR: Employs asynchronous replication between geographically distant data centers, where some data loss is tolerated due to higher latency.
Challenges with Stateful VMs
Stateful VMs pose unique challenges because they require durable and consistent storage that survives across failures and is quickly recoverable. Kubernetes’ built-in DR patterns primarily support stateless, ephemeral workloads, which are easier to restart elsewhere. VMs, however, demand more sophisticated handling to ensure both data integrity and availability.
An additional risk is the phenomenon known as a restart storm. This happens when many VMs attempt to restart at the same time, overwhelming the underlying infrastructure and causing significant delays in the recovery process.
Architectural Foundations
Effective DR starts with thoughtful decisions around cluster topology, storage architecture, and orchestration tools. These choices directly influence the feasibility of failover operations, achievable RPO and RTO targets, and the overall complexity of managing recovery processes.
In upcoming blogs, we will explore two principal DR architectures—unidirectional replication and symmetric replication—and analyze how each supports the unique requirements of VM workloads running on OpenShift Virtualization.
