Cloud bursting is a hybrid cloud strategy that allows an application to dynamically scale from a private cloud or on-premises infrastructure to a public cloud when demand exceeds local resource capacity. This approach is primarily used to handle sudden spikes in traffic or computational workloads without maintaining excess infrastructure locally. When the demand subsides, the application scales back down to its original environment, optimizing costs and resource usage.
From a technical perspective, cloud bursting relies on automation to manage resource allocation. For example, an application running on a private Kubernetes cluster might use a cluster autoscaler to provision additional nodes in a public cloud like AWS or Azure when CPU usage exceeds a predefined threshold. The bursted resources are typically temporary and integrated via APIs or orchestration tools. Key components include monitoring systems to detect traffic spikes, policies to define scaling triggers (e.g., response time thresholds), and mechanisms to synchronize data between environments. Developers must design applications to be stateless or ensure data replication to the cloud during bursts to avoid latency or inconsistencies. Tools like Terraform or cloud-specific APIs (e.g., AWS Auto Scaling) are often used to automate provisioning.
Common use cases include e-commerce platforms during holiday sales, media streaming services during live events, or batch processing jobs that require occasional high compute power. However, challenges exist. Data locality can introduce latency if the application relies on on-premises databases, requiring cached data or cloud storage synchronization. Security and compliance must also be addressed, as sensitive data might temporarily reside in public cloud environments. Cost management is critical—unplanned bursts can lead to unexpected expenses if not monitored. For example, a video encoding service might burst to Azure VMs during peak hours but must ensure workloads terminate promptly. Developers should test failover and scaling logic rigorously to avoid outages during transitions.