Project Name
How Ksolves Optimized OpenShift with KEDA to Handle Millions of Concurrent Streams
The client is a global video streaming platform serving millions of users across North America, Europe, and Asia-Pacific. Their platform delivers on-demand and live content, often witnessing unpredictable spikes in traffic during major releases and events. To meet growing user expectations for seamless streaming and zero downtime, the client needed to re-engineer their OpenShift cluster architecture for better scalability, high availability, and cost efficiency.
The client’s existing OpenShift environment was functional but not fully optimized for large-scale concurrency. Key challenges included:
- Resource Bottlenecks: The cluster experienced CPU and memory contention during peak streaming hours. This led to degraded performance and intermittent service disruptions.
- Suboptimal Node Utilization: Workloads were unevenly distributed across worker nodes. This caused imbalanced resource consumption and inefficient scaling.
- Networking Overheads: The existing ingress configuration and service mesh setup caused latency under high loads, especially during live-streaming events.
- Scalability Constraints: The Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler were not fine-tuned for workload patterns. As a result, they experienced delayed scaling responses.
- Observability Gaps: Lack of comprehensive monitoring and alerting made it difficult to proactively detect and mitigate performance bottlenecks before they affected users.
- Cost Inefficiency: Overprovisioning compute resources for peak loads led to increased operational expenses during normal traffic periods.
Ksolves, as a leading OpenShift consulting services company, redesigned the client’s entire cluster architecture to support event-driven, intelligent autoscaling using KEDA (Kubernetes Event-Driven Autoscaler) alongside traditional Kubernetes tools. The solution was executed in multiple stages:
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Cluster Architecture Optimization:
- Redesigned the OpenShift topology to ensure high availability across multiple zones using control plane redundancy.
- Implemented node pools with workload-specific taints and tolerations for efficient resource allocation.
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Event-Driven Autoscaling with KEDA:
- Integrated KEDA to dynamically scale microservices based on external event metrics, specifically, the number of incoming messages from Kafka topics that represented live user sessions, playback requests, and metadata updates.
- Combined KEDA with HPA and Cluster Autoscaler for a layered scaling approach, ensuring the system reacted instantly to message spikes rather than waiting for CPU or memory thresholds.
- Introduced Vertical Pod Autoscaler (VPA) for memory-intensive services such as content caching and recommendation engines.
- Configured KEDA ScaledObjects and ScaledJobs to manage streaming workloads and asynchronous message queues, ensuring elastic resource allocation during peak and idle times.
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Network and Ingress Optimization:
- Implemented HAProxy-based ingress controllers with connection reuse and caching optimizations.
- Optimized service mesh configuration (Istio) for reduced network hops and latency under load.
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Monitoring and Observability:
- Integrated Prometheus, Grafana, and Loki for real-time performance visibility and log analytics.
- Built custom KEDA and Kafka dashboards to visualize event-driven scaling activity and system responsiveness.
- Set up custom alerts for latency spikes, pod evictions, and API server saturation.
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Continuous Performance Testing:
- Simulated load tests with up to 5 million concurrent sessions using K6 and JMeter, validating system stability and throughput.
- Integrated performance tests into the CI/CD pipeline to monitor regression over time.
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Cost and Efficiency Improvements:
- Enabled cluster autoscaling on demand, reducing node count by 30% during off-peak hours.
- Introduced resource quotas and limits to prevent overconsumption by non-critical services.
- 5 Million+ Concurrent Users Supported: Achieved stable performance and zero downtime during global event streams.
- 40% Reduction in Latency: Optimized network and ingress configurations enhanced streaming responsiveness.
- 30% Infrastructure Cost Savings: Dynamic autoscaling and right-sizing reduced overall compute consumption.
- 3x Faster Scaling: Improved autoscaler responsiveness enabled rapid resource allocation during sudden traffic spikes.
- Full Observability: End-to-end monitoring empowered proactive issue resolution before user experience was impacted.
With Ksolves’ deep expertise in OpenShift performance tuning and cluster architecture and integration of KEDA for event-driven autoscaling, the client’s streaming platform evolved into a highly resilient, scalable, and cost-efficient infrastructure capable of supporting millions of concurrent users worldwide. This transformation not only enhanced user experience but also gave the client the confidence to launch global live events without performance concerns.
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