Case Study: Multi-Zone Node Pool Optimisation

Managing Kubernetes clusters in a single-zone setup leads to risks like service outages, high costs, and inefficient resource use. By switching to a multi-zone node pool strategy, workloads are distributed across multiple cloud availability zones, ensuring better uptime, improved resource usage, and reduced cloud expenses.

Key results from this approach:

• 87.5% reduction in downtime (from 4 hours to 30 minutes per month).
• 20% lower cloud costs, saving £30,000 annually.
• 25% improvement in resource utilisation (from 60% to 85%).
• 60% faster recovery from incidents.

The solution involved reconfiguring Kubernetes clusters with zone-aware scheduling, zone-redundant storage, and automated scaling. This setup not only improved resilience but also simplified maintenance and allowed engineers to focus on innovation. The transition was supported by expert consulting, which helped avoid common pitfalls and ensured compliance with UK regulations.

This approach highlights how businesses can improve reliability, cut costs, and meet regulatory demands through smarter cloud infrastructure management.

Deploying a Multi-Region Kubernetes Cluster

Identifying the Core Problems

Before rolling out their multi-zone strategy, the business took a hard look at their existing infrastructure. This deep dive revealed inefficiencies that were not only inflating costs but also undermining service reliability. These insights highlighted the urgent need to address several key issues.

Single-Zone Setup Risks

Relying on a single-zone architecture turned out to be a significant weak spot. With all workloads confined to one availability zone, the system faced a single point of failure, leading to frequent service disruptions during maintenance or unexpected outages [5][6].

This setup made it difficult to meet service-level agreements, a critical issue in the UK market where customer trust and strict regulations are non-negotiable [5][6]. The single-zone approach also raised red flags with regulators, especially in sectors like finance and healthcare, where resilience and business continuity are essential [5][6].

On top of that, resource utilisation was far from optimal. Workloads couldn’t spread out efficiently, leaving some nodes overloaded while others sat underused. Without the benefits of multi-zone autoscaling, this imbalance led to higher operational costs and wasted resources [2][7].

High Costs and Poor Performance

The inefficiencies didn’t stop there. Poorly configured node pools were pushing monthly cloud expenses up by more than 20% [2][4]. Instead of tailoring nodes to specific workloads, the business relied on general-purpose nodes, which created a costly, one-size-fits-all approach.

They also missed out on savings opportunities by not using spot or reserved instance pools, which could have cut costs for non-critical workloads by as much as 60% [2][4]. Over-provisioning became the default way to address performance concerns, driving up costs without achieving the desired reliability.

Performance issues were another headache. Memory-intensive applications often clashed with CPU-heavy processes, creating resource contention and slowing everything down. Inefficient resource allocation meant businesses were paying for unused capacity in partially filled node pools [3]. These combined financial and performance challenges made it clear that a major overhaul was overdue.

Business Requirements for Change

To tackle these problems, the business needed to pivot to a multi-zone strategy that was resilient, scalable, and cost-efficient. Their new requirements focused on ensuring high availability, dynamic scalability, and strict compliance with UK regulations, all while improving operational efficiency.

The infrastructure had to handle fluctuating demand automatically, eliminating the need for manual interventions or excessive over-provisioning. Flexibility to scale resources dynamically while maintaining consistent service performance was a must.

UK-specific requirements for data residency and robust disaster recovery came into play as well [5][6]. The business also realised that better optimisation could slash cloud costs by 30–50%, all while boosting performance [1].

Operational efficiency was another priority. Developers needed to shift their focus away from managing infrastructure and towards innovation and building new features, rather than constantly putting out fires.

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

This analysis made it clear why a multi-zone transformation wasn’t just an option - it was a necessity, both from a technical and business standpoint.

Solution Design and Implementation

The business collaborated with specialists to create a multi-zone strategy that spreads workloads across three availability zones. This approach not only boosted resilience and performance but also aligned with UK regulations while keeping costs in check. Below is an outline of the technical configurations that made these improvements possible.

Multi-Zone Node Pool Approach

To tackle risks and inefficiencies, the team restructured the Kubernetes cluster, extending it across three availability zones in the same region. By moving away from a single-zone setup that caused bottlenecks, they ensured workloads remained operational even during zone-level issues.

A key element of this strategy was node labelling. Automatic zone labelling enabled precise scheduling, ensuring pods were evenly distributed and avoiding over-reliance on a single zone.

Topology spread constraints were employed to evenly distribute pods across zones, reducing the chance of all replicas of a critical application being placed in one zone. Additionally, taints and tolerations were applied to ensure critical applications ran on dedicated nodes, while PodDisruptionBudgets maintained a minimum number of replicas during maintenance or scaling activities.

Storage and Network Configuration

The team upgraded from Locally Redundant Storage (LRS) to Zone Redundant Storage (ZRS) for persistent volumes. This ensured that data remained accessible even if a pod was rescheduled to a different zone, without introducing cross-zone latency. Storage classes were also updated to automatically allocate ZRS-backed volumes for new applications, keeping storage aligned with pod placements.

The network setup was reconfigured to be zone-aware, with load balancers designed to route traffic to healthy pods, regardless of their zone. Health checks and DNS settings were optimised to detect and bypass zone failures automatically. Inter-zone communication was fine-tuned to minimise latency while maintaining secure boundaries for sensitive workloads.

Implementation Process

The migration was carried out in a structured, low-disruption manner. It began with a thorough audit of the existing single-zone cluster to identify dependencies, bottlenecks, and applications requiring special attention during the transition.

In the planning phase, the team designed the new architecture, anticipated challenges, and created detailed migration plans for each application. This stage included testing storage failover scenarios to ensure applications could handle cross-zone scheduling.

New multi-zone node pools were deployed alongside the existing setup. Applications were then migrated in waves, with rigorous testing before and after each step to ensure smooth operation across zones. Monitoring and alerting systems were implemented to track performance and availability, providing insights into fault tolerance improvements and areas for further refinement.

Finally, the old single-zone infrastructure was decommissioned, freeing up resources and cutting costs. Expert input from Hokstad Consulting was instrumental in tailoring the migration to meet both technical and business goals.

This carefully planned migration showcased how businesses can significantly improve reliability and efficiency through multi-zone node pool optimisation. The process not only enhanced fault tolerance but also set the stage for better performance and reduced costs, paving the way for further advancements.

Results and Business Impact

Optimising multi-zone node pools brought notable improvements in technical performance while cutting costs. The business not only saved money but also achieved a higher level of system reliability and operational efficiency.

Measured Results

Downtime per month dropped dramatically, going from 4 hours to just 30 minutes - a reduction of 87.5%. Resource usage surged from 60% to 85%, a 25% boost, allowing the organisation to make better use of its existing infrastructure and reduce inefficiencies.

Cloud expenses saw a significant drop, with monthly costs reduced by £2,500, from £12,500 to £10,000 - a 20% saving. This translates to an annual saving of £30,000, freeing up funds for other strategic priorities.

Mean Time to Recovery (MTTR) improved by 60%, cutting down the time and effort spent on resolving incidents. This allowed technical teams to shift their focus from reactive problem-solving to more strategic, value-driven tasks. These gains set the stage for further process improvements and operational efficiency.

Operational Improvements

Automated scaling removed the need for manual intervention during traffic surges, while workloads seamlessly redistributed across healthy zones during disruptions. This self-healing mechanism reduced the strain on on-call engineers, improving team morale by eliminating late-night emergency responses caused by zone-level failures.

Maintenance became much simpler, with rolling updates and patches implemented without service interruptions. Planned maintenance windows became less disruptive, enabling the business to uphold security and performance standards without impacting customers or requiring complex cross-team coordination.

With enhanced automation and resilience, the team could handle a larger infrastructure and more applications, positioning the organisation for scalable growth without straining resources.

Before vs After Comparison

The improvements are clear when comparing key metrics:

The 83% drop in monthly incidents highlights how the updated architecture prevented most issues before they could affect services. This freed up valuable engineering time, allowing the team to focus on innovation and new feature development instead of constant firefighting.

Key Learnings and Best Practices

Working with multi-zone node pools unearthed several key lessons that can help organisations sidestep common pitfalls and achieve better outcomes right from the start.

Common Multi-Zone Node Pool Mistakes

As the technical implementation unfolded, a few recurring challenges became apparent.

Storage misconfiguration emerged as a major issue. Initially, Locally Redundant Storage was used for persistent volumes. This setup caused pods to fail when rescheduled across zones, as they couldn't reattach to their storage. Applications experienced failures as a result. Switching to Zone Redundant Storage and updating persistent volume claims resolved this issue, enabling seamless failover and improving reliability.

Overcomplicated affinity and anti-affinity rules also caused problems. At first, the team implemented overly restrictive rules that limited pod scheduling flexibility. This led to situations where pods couldn't be scheduled despite available resources. Simplifying these rules, while still meeting resilience requirements, improved scheduling efficiency and reduced resource fragmentation.

Inadequate pod topology spread constraints left clusters susceptible to failures. Without proper constraints, multiple replicas of critical applications ended up in the same zone, negating the benefits of a multi-zone deployment. By introducing granular constraints, the team ensured that replicas were distributed across zones, boosting fault tolerance.

Single-replica workloads posed another challenge, especially during node scaling and maintenance. These workloads experienced downtime when aggressive consolidation policies moved them between nodes. Implementing PodDisruptionBudgets and applying specific node annotations protected these critical workloads while still enabling cost optimisation across the cluster.

Best Practices for Continuous Optimisation

Regular monitoring and rebalancing proved essential to maintaining efficiency. The team frequently reviewed node utilisation, workload costs, and zone availability. This proactive approach helped identify underused resources and bottlenecks before they could impact performance or drive up costs.

Smart scheduling and bin-packing strategies were key to maximising resource use while maintaining resilience. Deschedulers were used to redistribute workloads across nodes, improving cluster efficiency. Combining automated scaling with intelligent workload placement reduced idle resources and delivered monthly savings.

Standardising storage classes helped prevent configuration drift and minimised storage-related issues. By creating standardised storage classes for different workloads, the team ensured consistency across deployments and made troubleshooting much simpler. This approach significantly reduced storage-related incidents and streamlined maintenance.

Automated node provisioning with well-balanced taints and tolerations allowed clusters to dynamically allocate resources based on demand. This automation reduced manual intervention while ensuring critical workloads had access to the resources they needed. Policies were fine-tuned to reserve high-performance nodes for latency-sensitive applications, while batch jobs were assigned to more cost-effective instances.

The Value of Expert Consulting

Facing these challenges, it became clear that expert guidance was crucial. Hokstad Consulting played a key role in avoiding costly mistakes and accelerating the implementation process.

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

Their expertise in DevOps transformation and cloud cost management was instrumental in designing effective storage and network configurations. By leveraging their knowledge of Infrastructure as Code and automated CI/CD pipelines, the team eliminated manual bottlenecks that had previously caused delays and errors.

Hokstad’s tailored solutions addressed the organisation’s specific compliance and regulatory needs without sacrificing performance or cost efficiency. Instead of relying on generic best practices, they adapted their approach to the organisation’s unique infrastructure, business context, and growth plans, ensuring a sustainable optimisation strategy.

The no savings, no fee model aligned the consultants' goals with the organisation’s outcomes, keeping the focus on measurable results. Their ongoing support and knowledge transfer empowered the internal team to maintain and build on the optimised configuration independently, reducing reliance on external expertise over time.

Conclusion

This case study highlights how multi-zone node pool optimisation can significantly enhance Kubernetes resilience while cutting costs. The organisation tackled key issues such as single-zone vulnerabilities, inefficient resource use, and rising cloud expenses head-on, achieving impressive results.

By redistributing Kubernetes nodes across multiple zones, implementing zone-redundant storage, and enforcing topology spread constraints, they achieved over a 20% improvement in resource utilisation, reduced cloud costs by up to 30%, and bolstered service availability through improved fault tolerance [2][4][7].

Additional benefits included faster deployment times, less manual intervention, and adherence to UK data residency standards. These changes allowed engineers to focus on innovation, while predictable billing in pounds simplified budget planning [2][5][6]. The financial clarity provided by billing in UK pounds sterling also aligned with local financial reporting requirements [2][4][8].

For businesses in the UK’s highly regulated and competitive markets, this approach offers a clear path to service reliability and cost control. By reducing the risk of correlated failures by up to 90% compared to single-zone setups, the strategy ensures business continuity, even during major cloud provider outages [9].

Hokstad Consulting played a pivotal role in this transformation, providing targeted expertise to overcome challenges, avoid costly missteps, and accelerate results. Their tailored DevOps and cloud cost engineering solutions achieved compliance while cutting cloud expenses by 30–50% [1]. Their no savings, no fee model underscored their commitment to delivering measurable outcomes.

Ultimately, multi-zone node pool optimisation has proven to be a strategic game-changer - offering resilience, scalability, and cost efficiency to meet the demands of modern, highly regulated businesses.

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