As far back as 2012, the concept of cloud bursting began to take shape within multicloud strategies, offering organizations a dynamic way to manage workloads across multiple cloud environments. Initially seen as a solution for handling unpredictable spikes in demand, cloud bursting allows enterprises to seamlessly scale resources by offloading overflow traffic from private clouds to public clouds during peak periods. This flexibility not only enhances resource optimization but also introduces cost-efficiency, allowing companies to avoid over-investment in on-premises infrastructure. Since then, cloud bursting has evolved, becoming an integral part of modern multicloud strategies, empowering businesses to leverage the strengths of different cloud providers while maintaining operational continuity and performance.

The elevator pitch

A multicloud cloud bursting strategy delivers key advantages, including cost savings by only paying for additional compute power when needed, enhanced flexibility to scale quickly during peak demand, and improved business continuity by seamlessly shifting workloads to the public cloud during surges. It also enables you to leverage the best features of multiple cloud providers, avoiding vendor lock-in by distributing workloads across different clouds. This approach optimizes cloud usage based on specific needs while ensuring resilience during high-traffic periods.

What is “Bursting” in multicloud?

In a multicloud infrastructure, “bursting” involves using resources from one cloud to supplement the resources of another. If an organization using a private cloud reaches 100 percent of its resource capacity, the overflow traffic is directed to a public cloud so there’s no interruption of services. In addition to flexibility and self-service functionality, the key advantage to cloud bursting is economical savings.

In some cases, however, cloud bursting may become complicated for developers and not be the best fit. It’s generally not recommended to use cloud bursting for applications that involve critical business operations or handle sensitive data, as the movement of data between the private and public clouds can introduce security and compliance risks. How can you solve this problem intelligently?

To solve the challenges and risks associated with cloud bursting, particularly when dealing with critical business operations or sensitive data, several intelligent strategies can be implemented. These approaches focus on maintaining security, compliance, and reliability while leveraging the benefits of multicloud environments:

1. Hybrid Cloud Architecture with Data Sovereignty Controls

• A hybrid cloud architecture, where an organization maintains control over certain critical workloads in the private cloud and bursts to the public cloud only for non-sensitive or less critical tasks, is an intelligent way to address this issue.
• Implement data sovereignty policies to ensure sensitive data stays within regulatory boundaries. Organizations can use tools to ensure that data classified as sensitive is either never moved between clouds or only moved in a secure, encrypted manner.

2. Data Encryption and Secure Transfer Protocols

• Encrypt all data in transit and at rest, ensuring that sensitive data being transferred between private and public clouds is protected against unauthorized access. Utilize end-to-end encryption during cloud bursting to secure data while it is being moved between environments.
• Leverage VPNs or private dedicated connections like AWS Direct Connect or Azure ExpressRoute to establish secure, private connections between the private and public clouds.

3. Edge Computing for Sensitive Data

• Use edge computing to process and store sensitive data closer to the source, such as at the private cloud layer, while offloading non-sensitive processing tasks to the public cloud. This ensures that critical data never leaves the private cloud, mitigating security and compliance risks associated with cloud bursting.

4. Cloud Bursting Automation and Policy-Based Controls

• Implement automated policy-based controls that govern what data can be moved between clouds. This includes using tools like cloud management platforms (CMPs) to create rules around data classification and prioritize which workloads can be burst and under what conditions.
• Set policies that limit cloud bursting to only non-sensitive, less regulated workloads, preventing any unauthorized or risky data movement between the private and public clouds.

5. Compliance Monitoring and Auditing

• Implement continuous compliance monitoring tools to ensure that any cloud bursting actions meet regulatory standards, including GDPR, HIPAA, or other industry-specific guidelines. This can involve real-time monitoring of data flow between clouds and automatic alerts if data moves outside compliance requirements.
• Utilize audit logs and cloud-native compliance frameworks to track where sensitive data is being moved and ensure that all actions are logged and monitored.

6. Containerization and Microservices for Isolation

• Use containers and microservices to isolate workloads in both the private and public clouds. By containerizing applications, organizations can burst specific components of the application to the public cloud without moving the entire application. This allows critical parts of the system to remain in the private cloud while less sensitive components can burst to the public cloud.
• Containers also enable more granular control over security policies and configurations in different cloud environments.

7. Cloud Bursting Testing and Validation

• Before implementing cloud bursting in production, conduct testing and validation to simulate various scenarios, ensuring that sensitive data will never be exposed or transferred outside the private cloud unintentionally.
• Leverage sandbox environments to test cloud bursting strategies in a safe, controlled manner before using them in live operations.

Optimizing the function

Here are some smart examples of Kubernetes services, containerization and microservices, and edge computing that can optimize cloud bursting in a multicloud or hybrid cloud environment:

1. Kubernetes Services for Cloud Bursting Optimization

Kubernetes provides a powerful framework for managing containerized applications in cloud-native environments. It is an excellent tool for enabling cloud bursting due to its scalability and flexibility. Here are some examples:

• Multi-Cluster Federation:
  • Example: Using Kubernetes Cluster Federation, you can manage multiple Kubernetes clusters across different clouds (public and private). This allows workloads to burst into additional clusters in the cloud when resource demands spike. For example, during a traffic surge, a Kubernetes cluster running in your private cloud can offload to a public cloud cluster while maintaining consistency across both environments.
  • Optimization: Cloud bursting is seamless, as Kubernetes can automatically scale services horizontally across clusters. Federation ensures a consistent configuration, so applications remain functional across different environments.
• Horizontal Pod Autoscaling (HPA):
  • Example: Kubernetes’ Horizontal Pod Autoscaler automatically scales your application’s pods based on CPU utilization or custom metrics. In a cloud bursting scenario, when the private cloud runs out of resources, the HPA could trigger the bursting of additional pods into the public cloud.
  • Optimization: This ensures that applications can scale automatically without manual intervention, dynamically adapting to the changing demand.
• Kubernetes Ingress and Load Balancing:
  • Example: Using Kubernetes Ingress controllers with a global load balancer, you can route traffic intelligently between the private and public cloud clusters. This way, requests can be automatically sent to the least busy cluster, whether it’s in the private or public cloud, for optimal performance.
  • Optimization: The ability to intelligently distribute load between clouds ensures that no cluster is overwhelmed, optimizing resource use and maintaining availability during bursts.

2. Containerization and Microservices for Cloud Bursting Optimization

Containerization and microservices are key to maximizing the flexibility and scalability of cloud-native applications, especially when it comes to cloud bursting. Here are some examples:

• Container Orchestration with Kubernetes:
  • Example: Containers can be managed by Kubernetes, which handles deployment, scaling, and networking of containers across both private and public clouds. When a private cloud hits its resource limit, Kubernetes can seamlessly deploy additional container instances in the public cloud.
  • Optimization: Containerization abstracts workloads, enabling cloud bursting without significant reconfiguration. Kubernetes ensures applications run consistently across environments, making the transition between private and public cloud effortless.
• Microservices Architecture with Containerized Services:
  • Example: With microservices, you break down a monolithic application into smaller, independently deployable services. For instance, a payment processing service can run in the private cloud while an analytics service bursts to the public cloud as needed.
  • Optimization: Microservices allow you to scale specific components of an application (such as analytics or background processing) on demand, reducing costs and complexity. By using containers, you can run these microservices consistently in both private and public clouds.
• Serverless Containers for Bursting:
  • Example: Combine Kubernetes with serverless frameworks (like Knative) to run containerized workloads in a serverless manner when bursting to the cloud. When resources in the private cloud are maxed out, Kubernetes can scale up serverless containers in the public cloud to handle demand spikes.
  • Optimization: Serverless containers help to optimize cloud bursting by ensuring you only pay for the compute resources you use, which reduces unnecessary costs while providing seamless scaling.

3. Edge Computing for Cloud Bursting Optimization

Edge computing can help to optimize cloud bursting by processing data closer to where it’s generated, reducing latency, and enabling more efficient use of cloud resources. Here are some smart edge compute strategies:

• Edge Compute for Local Data Processing and Cloud Bursting for Heavy Lifting:
  • Example: In a scenario where a manufacturing facility has IoT devices generating huge amounts of data, edge computing can be used to process and filter data locally. Only essential or aggregated data is sent to the cloud. If additional resources are needed to process data at scale, cloud bursting occurs, offloading processing to public cloud containers or Kubernetes clusters.
  • Optimization: By offloading heavy computation to the cloud only when necessary, edge computing minimizes data transfer, reduces latency, and ensures that critical, time-sensitive processing occurs locally, while cloud bursting is used for overflow tasks.
• Edge Device as an Extension of Cloud Resources:
  • Example: Devices at the edge, such as Raspberry Pi clusters or edge servers, can be used to run lightweight applications, microservices, or containerized workloads. When these edge devices reach capacity, they can burst into the public cloud for additional compute or storage.
  • Optimization: This approach reduces the need to send all data to the cloud, improving efficiency, and reducing cloud costs. It also ensures that cloud resources are used efficiently by only offloading workloads when required.
• Cloud-Edge Hybrid Deployment:
  • Example: Using platforms like AWS Snowcone or Azure Stack Edge, you can run compute workloads at the edge while still maintaining the ability to burst to the cloud when additional capacity is needed. For example, data analytics can be done on edge devices, while deep learning or complex computations are sent to the cloud when demand exceeds local resources.
  • Optimization: By combining edge computing with cloud bursting, organizations reduce the amount of data transferred to the cloud, decrease latency, and optimize cloud resources for peak performance during traffic spikes.

Pros and Cons of Cloud Bursting

Pros:

• Scalability: Cloud bursting encourages proactive capacity planning, ensuring applications can scale seamlessly during peak demand.
• Cost Efficiency: It avoids over-investing in on-premises infrastructure by only using extra cloud resources when needed.
• Disaster Recovery: Supports high availability and disaster recovery through automated failover and continuous data replication to the cloud.
• Peak Load Management: Enables handling of seasonal or unexpected demand spikes, maintaining application performance.
• Application Optimization: Monitors and optimizes performance through real-time scaling.
• Security and Compliance: Strengthens data security and compliance when properly managed.

Cons:

• Complexity in Parameters: Defining when to trigger cloud bursting can be challenging and must align with service-level agreements and user experience factors.
• Choosing the Right Partner: Selecting the best cloud platform is critical, as not all providers are suited for every organization’s needs.
• Security and Compliance Risks: Moving data between on-premises and the cloud can expose organizations to breaches, and certain data types may be restricted from the public cloud.
• Business Perspective: Cloud bursting should be carefully aligned with business goals to ensure it enhances customer experience and meets organizational needs.

Final thoughts:

To maximize the benefits of cloud bursting while minimizing risks, organizations must take a holistic approach to evaluate if it fits, and that the strategy includes strong DevSecOps security and compliance measures, such as encryption, policy-driven controls, and continuous monitoring. Leveraging technologies like Kubernetes, containerization, microservices, and edge computing can further optimize cloud bursting by enabling seamless scalability, reducing latency, and ensuring consistent performance across private and public clouds. By combining these strategies with the right expert team, businesses can effectively balance flexibility, cost-efficiency, and security, ensuring a resilient multicloud infrastructure that meets both operational needs and compliance requirements.