Vertical Scaling in Multi-Cluster CI/CD Pipelines

Vertical scaling in multi-cluster CI/CD pipelines focuses on increasing resources like CPU and memory for individual components rather than adding more instances. This method targets specific bottlenecks, improving performance and reducing costs. By using tools like the Vertical Pod Autoscaler (VPA), resource allocation can be automated based on usage patterns, making it easier to manage workloads across multiple clusters.

Key Takeaways:

• What it is: Adjusts resources (e.g., CPU, memory) for individual pods or nodes instead of adding more.
• Why it matters: Reduces cloud costs by up to 50% and improves deployment speeds (e.g., from 6 hours to 20 minutes).
• How it works: Tools like VPA dynamically manage resource usage, while horizontal scaling handles traffic surges.
• Best practices: Start with recommendation mode, analyse metrics (CPU/memory usage, deployment times), and tailor scaling for each environment (development, staging, production).
• Challenges: Balancing performance with costs and avoiding conflicts between vertical and horizontal scaling.

By combining vertical and horizontal scaling strategies, organisations can optimise CI/CD pipelines, ensuring smoother deployments and better resource efficiency.

Kubernetes Vertical Pod Autoscaler (VPA) Tutorial | Full Setup, Demo & VPA vs HPA Explained

Key Components of Vertical Scaling in Multi-Cluster Pipelines

Vertical scaling in multi-cluster pipelines relies on automated resource adjustments, accurate configurations, and coordinated scaling to maintain efficiency and avoid wasting resources.

Vertical Pod Autoscaler (VPA) Overview

The Vertical Pod Autoscaler (VPA) is at the heart of automated vertical scaling in Kubernetes. Instead of manually adjusting CPU and memory for each pod, VPA tracks resource usage over time [2] and automatically adjusts allocations. For instance, if a build server consistently maxes out its memory, VPA will recommend increasing the allocation.

Take a compilation stage as an example - it might need substantial CPU power for a short burst, then drop to minimal usage while waiting for the next task. VPA adapts to these fluctuations, reducing the need for constant manual adjustments.

VPA operates in three modes: recommendation mode (suggesting changes without applying them), auto mode (automatically applying recommendations), and initial mode (setting resources only when a pod is created). Starting with recommendation mode is a smart move, especially in production environments, as it lets teams review proposed changes before they go live - avoiding unexpected disruptions.

Each cluster's VPA setup should align with its specific workload. For example, a development cluster handling frequent, smaller builds will have different needs compared to a production cluster managing larger, less frequent deployments. Tailoring these configurations ensures optimal performance.

Once VPA is configured, the next step is defining precise resource requests and limits to maximise these dynamic adjustments.

Resource Requests and Limits in Multi-Cluster Environments

Setting accurate resource requests and limits is crucial for effective vertical scaling. Resource requests specify the minimum CPU and memory a pod needs to operate, while limits cap the maximum it can use. Getting these values right prevents over-provisioning, which can inflate costs, and under-provisioning, which can lead to delays and performance issues.

In multi-cluster setups, workloads can vary significantly. Analysing the actual usage patterns of each cluster is key to determining the right resource configurations.

By aligning resource requests and limits with real-world usage, you can cut costs and avoid performance bottlenecks [1]. Start by gathering baseline metrics from your pipeline components over a representative period. Keep an eye on CPU usage, memory consumption, and signs of resource throttling to identify typical patterns and peak usage spikes. This data will guide you in setting resource requests that reflect average needs and limits that handle peak demands.

Automation plays a big role in maintaining these configurations across multiple clusters. While VPA automates pod resource adjustments, using Infrastructure as Code (IaC) ensures these configurations are consistent and version-controlled.

You can also create distinct resource profiles for different pipeline stages. For example:

• Build stages might need higher CPU with moderate memory.
• Testing stages may require a balanced allocation.
• Deployment stages often need minimal resources but require guaranteed allocations for reliability.

With these configurations in place, combining vertical and horizontal scaling becomes the next step.

Integration with Horizontal Pod Autoscaling (HPA)

Bringing VPA and Horizontal Pod Autoscaler (HPA) together creates a well-rounded autoscaling strategy: VPA fine-tunes resources for individual pods, while HPA adjusts the number of pods in real time [2]. This combination handles everything from sudden traffic spikes to gradual workload changes.

In multi-cluster CI/CD pipelines, this integrated approach ensures infrastructure adapts quickly to fluctuating demands. For instance, if several developers trigger builds simultaneously, HPA can spin up additional pods to handle the load, while VPA ensures each pod gets the right amount of resources.

However, integrating VPA and HPA requires careful planning to avoid conflicts. Begin by using VPA in recommendation mode when HPA is scaling pod replicas [2]. This cautious approach ensures that the decisions made by VPA and HPA complement each other rather than clash.

Managing VPA and HPA decisions across multiple clusters also requires robust monitoring and centralised observability [2]. A unified monitoring solution can track metrics like CPU usage, memory consumption, pod replica counts, and node capacity across all clusters. Fleet management platforms are becoming essential for scaling operations across dozens - or even hundreds - of Kubernetes clusters [3]. Additionally, self-service catalogues with pre-configured infrastructure and application stacks are helping standardise provisioning across organisations [3].

Finally, ensure that your cluster autoscalers, which handle node-level scaling, work seamlessly with both VPA and HPA [2]. Multi-cluster dashboards with secure reverse tunnels can provide a single interface for monitoring all resources without exposing cluster API servers to the internet [3]. These tools help you track how VPA and HPA interact, catch potential conflicts early, and confirm that your scaling strategy delivers the expected performance gains and cost savings.

Performance Optimisation in Multi-Cluster CI/CD Pipelines

Vertical scaling plays a key role in improving pipeline performance by dynamically allocating resources to components as needed. This approach helps eliminate bottlenecks while avoiding unnecessary resource waste.

Scaling Pipeline Components for Better Performance

Core components of CI/CD pipelines - such as build agents, test runners, and deployment controllers - can see significant performance improvements through vertical scaling. For instance, build agents often experience fluctuating demand, with high resource needs during compilation phases. By allocating additional CPU and memory during these peak times, delays in builds can be reduced, ensuring smoother downstream processes.

Similarly, test runners benefit from having sufficient memory to execute test suites in parallel. This reduces the overall testing time and avoids the inefficiencies of sequential execution. Deployment controllers, which manage rollouts across clusters, require robust resources to handle tasks like health checks, deployment state tracking, and rollout coordination. Without adequate provisioning, these controllers may face issues like timeouts, incomplete rollouts, or failed health checks - problems that are especially common in multi-cluster setups.

An example from the field highlights the impact of vertical scaling: a multi-cluster CI/CD pipeline managing over 2,500 microservices across 15+ clusters achieved a 60% reduction in deployment time by optimising resource allocation. The process involved identifying bottlenecks - such as CPU throttling or memory constraints - and addressing them with targeted resource increases. Tailoring these adjustments to specific environments (development, staging, or production) further ensured efficient operation across all stages.

Once resources are optimised, continuous monitoring becomes essential to maintain and validate these performance gains.

Monitoring and Analysing Pipeline Performance

After implementing vertical scaling, ongoing monitoring is crucial to confirm its effectiveness. By using aggregated dashboards, teams can compare current resource usage with historical data to identify peak demand periods and evaluate the impact on pipeline performance.

Tracking execution times for pipeline stages - such as build, test, and deployment - provides a clear measure of scaling success. For example, reductions in stage durations or fewer resource throttling events indicate that vertical scaling is working as intended. Queue depths also offer valuable insights. A backlog of build requests might suggest the need for horizontal scaling, while prolonged execution times without queueing point to the need for further vertical scaling.

Monitoring resource throttling events is equally important, as they signal that components are reaching their capacity limits. Alert systems should notify teams of these events so they can adjust resources before performance suffers.

Audit logs are indispensable in production environments, capturing details like deployment events, resource changes, and scaling decisions. These logs not only aid in compliance and troubleshooting but also help teams understand how scaling adjustments impact performance. Centralised observability tools, often using federated monitoring systems, make it easier to track metrics across all clusters, ensuring no performance issues go unnoticed.

Real-time dashboards, especially those integrated with tools like the Vertical Pod Autoscaler (VPA), provide actionable insights for proactive scaling decisions. Monitoring cost metrics alongside performance data is also essential to ensure that any resource increases remain cost-effective. Many organisations adopt environment-specific monitoring strategies, with production setups typically requiring more rigorous, real-time alerting compared to development or staging environments. This approach ensures that resources are used efficiently without overspending on infrastructure.

Cost Management and Scalability Considerations

Vertical scaling can boost performance, but it also comes with higher costs - a balance that needs careful attention. Managing how resource changes impact infrastructure spending is critical for maintaining efficient multi-cluster CI/CD operations.

Cost Implications of Vertical Scaling

When the Vertical Pod Autoscaler (VPA) increases CPU and memory allocations for pipeline components, it directly raises node capacity requirements and, in turn, cloud costs. The key to managing these costs lies in setting accurate resource requests and limits. Requests represent the minimum guaranteed resources for pods, which influence capacity planning and infrastructure expenses. If the VPA adjusts these values upwards based on historical data, costs will rise. On the other hand, poorly configured requests and limits can lead to two costly scenarios: over-provisioning wastes money, while under-provisioning results in performance issues and failed deployments.

A practical approach is to set requests based on the 95th percentile of usage and limits at 150% of those requests. This method helps avoid waste. Regularly comparing actual resource usage to requested allocations can uncover opportunities to cut costs by 15–30%, all while maintaining performance.

Inconsistent configurations across clusters also inflate expenses. For instance, if a build agent is configured with 4 CPU cores in one cluster but 8 in another for the same workload, the extra capacity may go unused. Standardising configurations across environments not only reduces costs but also simplifies capacity planning.

Organisations often face the dual challenge of paying for unnecessary resources while missing out on optimisation opportunities. Cloud bills can rise without a noticeable improvement in performance, highlighting inefficiencies. Addressing these issues requires a systematic approach: right-sizing resources, automating processes, and allocating resources more intelligently.

Your cloud costs keep climbing, but performance isn't improving. You're paying for resources you don't need whilst missing optimisation opportunities. - Hokstad Consulting [1]

Monitoring throttling events is another way to manage costs effectively. These events can signal that components are nearing their capacity limits. By analysing historical data, teams can distinguish between consistent throttling and temporary spikes, allowing them to address genuine performance needs without unnecessary spending.

Balancing the trade-offs between performance and cost is essential, as we'll explore in the next section.

Balancing Performance and Cost

Vertical scaling works well for workloads like databases or batch jobs, where increasing CPU and memory is often more efficient than adding new instances. Horizontal scaling, on the other hand, suits stateless components. Combining VPA with Horizontal Pod Autoscaler (HPA) can strike a balance between cost and performance.

Real-world examples show how optimisation strategies can achieve both cost savings and performance improvements. An e-commerce site, for instance, reported a 50% boost in performance while cutting infrastructure costs by 30% through targeted cloud optimisation [1]. This success came from eliminating unnecessary resource spending, automating resource management, and monitoring usage to align allocations with actual needs.

Our proven optimisation strategies reduce your cloud spending by 30–50% whilst improving performance through right-sizing, automation, and smart resource allocation. - Hokstad Consulting [1]

Automated CI/CD pipelines, Infrastructure as Code (IaC), and robust monitoring tools not only streamline operations but also reduce errors. These tools contribute to faster deployments and better resource utilisation. For example, a tech startup reduced deployment times from 6 hours to just 20 minutes [1], demonstrating how process improvements can deliver concrete benefits beyond resource adjustments.

Tailored scaling strategies for different environments - development, staging, and production - can also help control costs. Development environments typically don’t need the same resources as production, yet many organisations apply identical configurations across all environments. Customising vertical scaling policies to fit each environment's needs prevents overspending while ensuring adequate performance for testing and development.

Frequent assessments of infrastructure and workloads can reveal areas where resources are either over-provisioned or underutilised. These reviews should account not just for current usage but also for growth patterns and seasonal spikes. For instance, a pipeline that handles 100 deployments daily under normal conditions but spikes to 500 during release cycles will need a different scaling approach than one with steady workloads.

Tracking metrics like cost per deployment, build, or test execution can provide valuable insights. If these metrics increase without corresponding performance improvements, it’s time to revisit scaling configurations. Comprehensive monitoring within CI/CD pipelines can help identify patterns in resource use, highlight components with excessive resource requests, and pinpoint periods when resources sit idle.

Implementation Roadmap for Vertical Scaling in Multi-Cluster CI/CD Pipelines

Rolling out vertical scaling in multi-cluster CI/CD pipelines calls for careful planning. A structured roadmap ensures you address technical needs, validate improvements, and keep systems stable throughout the process.

Assessing Current Infrastructure and Workloads

Start by evaluating your pipeline's performance and resource usage. Gather baseline metrics - such as CPU, memory, error rates, and deployment times - over a full sprint cycle and from at least 100 builds. This helps capture workload variations and provides a clear picture of your current setup.

High cloud costs often signal inefficient resource use or missed optimisation chances. For instance, rising infrastructure expenses without matching performance gains might indicate over-provisioned or under-provisioned areas. Slow deployment cycles and frequent errors could point to outdated processes or manual tasks bogging down developers. These issues highlight the need for better automation and resource management.

Identify pipeline components that would benefit most from vertical scaling. Databases, batch jobs, and build agents with fluctuating resource demands are ideal candidates for Vertical Pod Autoscaler (VPA). These components often show significant spikes in CPU and memory usage, making dynamic resource adjustments essential. On the other hand, stateless services with predictable usage patterns might be better suited to horizontal scaling or fixed allocations.

Next, analyse resource requests and limits across all clusters. Look for inconsistencies - if identical workloads have different configurations across clusters, it can lead to unnecessary spending. Document cases where resources are throttled or consistently underused. This analysis can uncover bottlenecks and cost-saving opportunities.

Consider workload growth patterns and seasonal spikes. For example, pipelines handling 100 deployments daily may surge to 500 during release periods. Such scenarios require tailored scaling strategies compared to pipelines with steady workloads.

Phased Implementation Strategy

Once you've mapped out your resource needs, roll out vertical scaling in phases. Begin in non-production environments to test configurations and fine-tune your approach without risking customer impact.

Phase 1: Recommendation Mode in Development

Deploy VPA in off mode within development clusters. In this mode, the system suggests resource adjustments without applying them automatically. This lets you observe recommendations and identify patterns or anomalies over at least two weeks of data collection.

Set VPA parameters like minAllowed and maxAllowed to avoid extreme scaling decisions. For workloads with variable resource demands, such as batch jobs or databases, use wider ranges. For predictable workloads, like stateless services, narrower ranges are more effective.

Phase 2: Automated Scaling in Staging

After validating recommendations in development, move to staging environments using initial mode. Here, resource adjustments are applied only when pods are created, offering a middle ground between manual oversight and automation. Monitor deployment success rates, execution times, and resource usage during this phase.

Ensure staging environments mirror your production setup, including workload distribution and traffic patterns. Use real workloads rather than minimal test loads to get accurate results. Compare pre- and post-implementation metrics. If you notice slower deployments, higher error rates, or increased costs without performance gains, revisit your configurations before proceeding.

Phase 3: Production Rollout

Introduce vertical scaling in production clusters one at a time, starting with less critical systems. Use recreate mode to apply changes by restarting pods or auto mode for continuous optimisation, depending on your confidence level. Maintain consistent monitoring and alerting to quickly address any issues.

To keep configurations consistent across clusters, use declarative configuration management. For workloads spanning multiple clusters, ensure cross-cluster communication - tools like Istio can help prevent scaling decisions in one cluster from negatively affecting others.

Fine-tune settings like updatePolicy.updateInterval to control how often VPA recalculates recommendations. High-variability workloads may need shorter intervals (around 1 minute), while stable ones perform better with longer intervals (around 15 minutes). Similarly, adjust histogramBucketSize to balance accuracy and overhead - values between 0.1 and 0.5 work well for pipeline components like build agents.

Document rollback procedures for each phase and test them in non-production environments to ensure quick recovery if needed.

Validating Success Metrics

After completing the deployment, measure the impact of vertical scaling using targeted metrics. Focus on quantifiable outcomes that demonstrate performance gains and cost efficiency.

Deployment Performance Metrics

Track deployment speeds before and after scaling. For example, some companies have reduced deployment times from 6 hours to just 20 minutes [1]. Monitor success and error rates - optimised setups can cut downtime by up to 95% and reduce errors by as much as 90% [1].

Resource Efficiency Metrics

Compare actual resource usage against allocated requests. Effective scaling minimises the gap between allocated and used resources, reducing waste while maintaining performance. Monitor throttling incidents - fewer occurrences indicate that VPA is working as intended. Pay attention to resource usage patterns throughout the day and across workload types.

Cost Metrics

Review cost per deployment. A drop in this metric confirms financial efficiency.

Validation Timeline

Allow at least four weeks after each phase to gather stable data. Short-term metrics might be skewed by temporary workload fluctuations. Comparing data from similar time periods - like the same week in consecutive months - helps account for cyclical changes.

Document your findings and share them with your team. Success isn't just about hitting target metrics; it's also about understanding the changes and ensuring they’re sustainable in the long run.

Conclusion

Vertical scaling transforms resource-intensive multi-cluster CI/CD pipelines into streamlined, cost-efficient systems. By dynamically adjusting CPU and memory allocations to match actual usage patterns, organisations can eliminate the over-provisioning that often leads to unnecessary expenses. Studies indicate that adopting these strategies can reduce cloud spending by 30–50%, all while boosting performance [1].

Pairing vertical scaling through Vertical Pod Autoscaler (VPA) with horizontal scaling via Horizontal Pod Autoscaler (HPA) creates a well-rounded resource management strategy. This combination lays the groundwork for a gradual and controlled implementation process.

A phased rollout is crucial for success. Start by thoroughly assessing your current infrastructure and collecting baseline metrics over complete sprint cycles. Introduce changes incrementally - begin with recommendation mode in the development environment, then move to automated scaling in staging, and finally deploy cautiously in production. This step-by-step approach minimises disruptions and fosters confidence in the new configurations. The potential benefits are substantial, with reduced deployment times, fewer errors, and less downtime [1].

Track progress using measurable metrics like deployment speeds, resource usage, and cost per deployment. Allow sufficient time after each phase to gather stable data, accounting for variations in workloads and seasonal trends. Share the results across teams to encourage a collaborative understanding of the improvements. Sustainable success comes from collective insight, not just meeting targets. These adjustments build on earlier efforts to ensure consistency and efficiency across clusters.

For organisations looking to refine their DevOps practices and optimise cloud infrastructure, Hokstad Consulting (https://hokstadconsulting.com) offers expertise in implementing these strategies effectively.

Vertical scaling isn’t a one-time task - it’s an ongoing process of adjustment and optimisation. By continuously fine-tuning resource allocation, your pipelines can remain scalable, reliable, and cost-efficient as workloads evolve. Combining right-sizing, automation, and intelligent resource management ensures your pipelines deploy smoothly, scale effectively, and operate cost-efficiently across all environments.

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