What is cloud bursting?

Cloud bursting is a configuration in cloud computing where an application runs in a private cloud or on-premises data center and "bursts" into a public cloud when the demand for computing capacity spikes. This essentially acts as an overflow valve, so that when the private infrastructure reaches its limit, the traffic automatically directs to public cloud services to ensure no interruption in service. You can think of it like a retail store that opens extra checkout lanes only when the lines get too long. The cloud bursting setup is a specific type of hybrid cloud deployment.

In a standard cloud scaling model, a company might try to handle everything in one environment. However, owning enough physical servers to handle your busiest day of the year means those servers sit empty and unused for the other 364 days. Cloud bursting can help solve for this, as it can allow an organization to pay for basic capacity in their own data center and only pay for extra public cloud resources when they actually need them. This approach can help companies handle sudden traffic surges without buying expensive hardware that they don't need all the time.

How cloud bursting works

To understand the mechanics of a cloud burst, imagine your private cloud as a water tank. Under normal conditions, the water (data traffic) stays within the tank's capacity. However, when a sudden storm hits (a traffic spike), the tank risks overflowing.

In a cloud bursting setup, IT teams configure a "trigger" or threshold—typically when resource usage hits about 70-80%. Once this threshold is crossed, the system automatically opens a valve to a secondary tank—the public cloud. The application continues to run seamlessly, with the overflow traffic being routed to the public cloud resources. Once the storm passes and traffic levels drop back down, the system closes the valve and decommissions the public cloud resources, returning operations solely to the private cloud.

Types of cloud bursting

There are different ways to set up these bursts depending on how much control or automation a team needs.

Manual bursting: This happens when an administrator manually adds public cloud resources. This can be useful for predictable events, like a planned software launch, where a human can decide exactly when to start and stop the extra capacity.
Automated bursting: This method uses software policies to trigger the burst. When the system detects that resource use has hit a specific limit, it can automatically spin up extra resources in the public cloud without human intervention.
Distributed load balancing: This approach spreads traffic across both the private and public clouds simultaneously. It routes user requests to the location that is closest to them or has the most available capacity at that moment.

When to use cloud bursting

Cloud bursting isn’t always the right fit for every application, especially those that rely on complex, sensitive data that cannot leave a private network. It’s typically best suited for workloads with fluctuating, seasonal, or unpredictable demand patterns where speed and uptime are critical, such as in the following situations:

Seasonal or expected demand spikes

Retailers often face massive traffic surges during popular shopping events like Black Friday or Cyber Monday. Cloud bursting allows these businesses to handle millions of shoppers for a few days using the public cloud, then scale back down to their private infrastructure when the rush is over.

Resource-intensive, limited duration jobs

Data scientists and engineers often run high performance computing (HPC) tasks such as complex simulations, AI model training, or other heavy computation like 3D rendering. These jobs might need thousands of servers for just a few hours. Bursting lets teams rent this massive power temporarily instead of waiting in a long supercomputing queue, or building a supercomputer that will be underutilized.

Development and testing (Dev/test)

Software developers frequently need to spin up temporary environments to test new code or updates. Rather than taking up space on the main private servers, they can burst these test environments to the public cloud. This helps keep the production environment safe and stable.

Supporting disaster recovery (DR)

If a local data center goes offline due to a power outage or natural disaster, cloud bursting can act as a failover mechanism supporting disaster recovery. The system can redirect traffic to the public cloud to keep the application running until the primary site is fixed.

How can organizations implement cloud bursting?

Implementing cloud bursting requires more than just two computing environments; it requires a strategy to handle the complexity of moving data and applications between them. To do this effectively, organizations need features that ensure seamless connectivity and consistent management.

Configuring a Burst Trigger with GKE

One of the most effective ways to implement a cloud bursting trigger is by using Google Kubernetes Engine (GKE) and the Horizontal Pod Autoscaler (HPA) with external metrics. In this scenario, your on-premise application sends a signal (a metric) to Google Cloud Monitoring. When that signal crosses a threshold, GKE automatically spins up new pods in the cloud to handle the load.

Here is how you can set up a trigger based on a Pub/Sub queue depth (a common indicator that your on-premise workers are overwhelmed):

1. Enable the custom metrics API: First, you must allow your GKE cluster to read metrics from Cloud Monitoring. You do this by deploying the Custom Metrics Stackdriver Adapter to your cluster. This adapter acts as a bridge, translating Google Cloud metrics into something Kubernetes can understand.

Loading...

2. Define the HPA configuration: Create a HorizontalPodAutoscaler YAML file. Unlike a standard autoscaler that looks at CPU usage, this one will look at an external metric—specifically, the number of undelivered messages in a Pub/Sub subscription (num_undelivered_messages).

Loading...

3. Apply and monitor: Apply this configuration using kubectl apply -f hpa.yaml. Now, GKE is "watching" your queue. If your on-premise system slows down and the queue fills up past the target (50 messages), the HPA will automatically trigger the creation of new pods in the cloud to process the backlog. Once the queue is empty, GKE will scale the pods back down to zero.

Fine-tuning through monitoring

You can’t manage what you can’t see. To make cloud bursting work, IT teams need a clear view of their resources across both their private data center and the public cloud. Google Cloud offers tools that provide granular visibility into how applications use CPU and memory.

By understanding exactly how much "fuel" an application burns, teams can set accurate thresholds for when to burst. If the threshold is too low, you might spend money on the public cloud when you don't need to. If it’s too high, the app might crash before the new resources arrive. Unified monitoring helps organizations fine-tune these settings to balance performance and cost.

The role of automation

Manual balancing works for small, infrequent projects, but it may not scale well for enterprise applications. To be more efficient, organizations can implement software and tools to automatically orchestrate cloud computing resources. Automation tools, such as Terraform or Google Cloud's deployment manager, can help define infrastructure as code (IaC).

This means the system can provision, configure, and manage servers automatically based on real-time demand. When the traffic spike subsides, the automation tools also handle the "deprovisioning," or shutting down, of those resources. This ensures the company stops paying for the public cloud the moment it’s no longer needed.

Controls (monitoring and reporting)

Maintaining control during a burst is vital for security and budget management. Organizations need robust monitoring capability to track resources and ensure they are properly provisioned without service interruption.

Reporting tools help track how much the bursting costs over time. This data is essential for predicting future budgets. Furthermore, consistent security policies must apply to the bursting resources. Tools that implement monitoring and reporting can help reduce costs and increase efficiency over time by identifying trends and anomalies in usage.

Benefits of cloud bursting

Adopting a cloud bursting strategy can offer several advantages for organizations looking to balance performance and budget.

Cost savings

Companies only pay for the additional public cloud resources when they use them, which can help avoid the capital expense of buying hardware that sits idle during quiet periods.

Flexibility and scalability

It can give teams the freedom to test new projects or handle massive spikes in traffic without being limited by the physical space or power available in their own data center.

Business continuity and resilience

If the private data center has a problem or gets overwhelmed, the application stays online by shifting the load to the public cloud, which helps prevent crashes and downtime.

Resource optimization

IT teams can keep their private cloud running at a steady, efficient level for critical tasks while offloading variable, unpredictable traffic to the flexible public cloud.

Solve your business challenges with Google Cloud

New customers get $300 in free credits to spend on Google Cloud.

Google Cloud advantage for bursting and scalability

While the concept of cloud bursting is universal, the infrastructure that supports it varies significantly between providers. Google Cloud offers specific advantages that make hybrid bursting faster, more reliable, and easier to manage.

Consistent platform with GKE Enterprise: Many hybrid solutions require teams to manage two different environments—one for on-premises and one for the cloud—which can create compatibility issues during a burst. Google Cloud’s GKE Enterprise provides a consistent Kubernetes runtime across both environments. This means an application built for your private data center can burst instantly into Google Cloud without needing code changes or complex re-platforming.
Enhanced network performance: When an application bursts, data must travel between the private data center and the public cloud. Google runs one of the largest private fiber-optic networks in the world. By keeping traffic on this private backbone rather than the public internet, Google Cloud can reduce latency and improve security during critical high-traffic events.
Advanced global load balancing: Google Cloud Load Balancing doesn't just route traffic; it can respond to traffic spikes in seconds (not minutes) and distribute loads across regions globally. If a local burst isn't enough, the network can automatically route users to the next closest region with available capacity, offering a level of resilience that is difficult to achieve with standard networking tools.
Open source flexibility: Because Google Cloud is built on open standards like Kubernetes and TensorFlow, organizations avoid vendor lock-in. You can build a bursting strategy that works today and retains the flexibility to adapt your infrastructure in the future.