Project Name
How a Digital Payments Platform Improved Performance and Cut Costs with Optimized OpenShift Cluster Sizing
A fast-growing digital payments provider handling UPI transactions, wallet payments, merchant settlements, and recurring billing was operating on a microservices-driven architecture deployed on Red Hat OpenShift.
Daily peak load routinely crossed millions of transactions, and unpredictable traffic spikes during sales events caused performance degradation and higher operational overhead. To control infrastructure costs while ensuring ultra-low payment latency, the client needed a right-sized OpenShift architecture that balanced performance, scale, and cost efficiency.
The client’s OpenShift environment was functional but faced several operational and performance hurdles that hindered scalability, increased costs, and affected transaction throughput.
- Over-Provisioned and Inefficient Worker Nodes: The cluster’s nodes were sized for worst-case traffic scenarios. This led to high idle CPU during off-peak periods, nearly 25% wasted infrastructure spend, and poor workload distribution across nodes.
- Fragmented Resource Usage & Low Workload Density: Many microservices had incorrect CPU and memory requests and limits. This caused high resource fragmentation, frequent node pressure warnings, and delayed Horizontal Pod Autoscaler (HPA) responses during traffic spikes.
- Lack of Event-Based Autoscaling: Autoscaling relied solely on CPU usage, ignoring critical application-level signals such as Kafka consumer lag, queue depth for payment and settlement services, and request rate (RPS) for payment APIs. This led to delayed scaling actions during peak loads.
- Latency and Throughput Degradation During Peak Traffic: During heavy traffic bursts, several services experienced increased tail latency, timeouts in settlement workflows, and unstable throughput across critical payment microservices.
As an OpenShift consulting company, we implemented a comprehensive optimization strategy to right-size the cluster, improve throughput, and reduce costs while ensuring high availability and regulatory compliance.
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Cluster Utilization & Architecture Assessment
- Performed a full analysis of node utilization patterns, pod CPU/memory usage at the 95th percentile, HPA behavior, and throughput versus resource consumption for all payment services.
- Evaluated Kafka lag and queue metrics to identify scaling gaps.
- Used tools like OpenShift Monitoring, Prometheus, and KEDA metrics adapters, along with custom dashboards.
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Node Right-Sizing & Scheduling Efficiency
- Redesigned node pools to match actual workload behavior.
- Introduced optimized mixed-size nodes.
- Replaced oversized nodes with medium nodes for better horizontal spread
- Balanced workloads across nodes to eliminate performance hotspots. This ensured uniform CPU and memory utilization throughout the cluster.
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Resource Requests & Limits Optimization
- Recalibrated CPU and memory requests/limits for over 40 microservices based on real runtime metrics.
- Reduced resource fragmentation, increased pod density by 35–40%, and smoothed HPA scaling.
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Event-Driven Autoscaling with KEDA
- Implemented KEDA-based scaling for transaction-aware autoscaling.
- Scaling triggers were based on Kafka consumer lag, queue depth for settlement services, and RPS for APIs, ensuring resources were provisioned before latency issues could occur.
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High Availability, Stability, and Deployment Safety
- Applied pod anti-affinity rules for critical services.
- Spread workloads across availability zones.
- Configured PreStop hooks to avoid terminating live payment sessions.
- Optimized rolling update strategies to maintain uninterrupted operations.
- 37% Reduction in Infrastructure Costs: Achieved by right-sizing nodes, increasing pod density, and removing idle resources.
- 3X Improvement in Peak Throughput Handling: High-volume events like festive sales and merchant batch processing were handled without performance degradation.
- 40% Lower Latency During Peak Load: Event-driven autoscaling and resource optimization reduced tail latency across critical payment pathways.
- Stable Node Pressure and Predictable Scaling: CPU throttling and pod evictions were eliminated, and scaling became smoother and faster.
- Higher Developer and SRE Productivity: Teams reported fewer incidents, reduced firefighting during peak events, and better visibility for capacity planning.
By combining workload analysis, cluster right-sizing, resource optimization, and event-driven autoscaling with KEDA, we transformed the client’s OpenShift platform into a highly efficient, scalable, and resilient infrastructure. The company now runs a high-throughput, low-latency payment platform capable of processing millions of transactions daily while significantly reducing infrastructure costs.
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