Implementing Auto-Scaling with KEDA for Enhanced Healthcare Service Delivery

Client Background

Our client, a prominent healthcare provider in the UK, faced challenges in managing the fluctuating demands on their digital services. To address this, we implemented an auto-scaling solution using Kubernetes-based Event Driven Autoscaling (KEDA). This case study details the journey from the discovery phase through to the successful implementation and outcomes.

Discovery Phase

Client Challenges

During the discovery phase, we conducted several workshops and interviews with the client’s IT and operations teams to understand their pain points and requirements. The key challenges identified were:

Unpredictable Workloads

The client’s applications experienced significant fluctuations in demand, especially during peak hours and emergencies. The healthcare sector requires systems that can handle sudden surges in traffic when patients need urgent care or during health crises.

Resource Inefficiency

Static scaling led to either over-provisioning, resulting in wasted resources and unnecessary costs, or under-provisioning, causing performance bottlenecks that could impact patient care.

Manual Scaling Limitations

The existing manual scaling processes were slow and error-prone, impacting the system’s reliability and the team’s productivity. Healthcare services require immediate response, which manual processes couldn’t provide.

Requirements Gathering

Based on the challenges, we identified the following requirements for the auto-scaling solution:

• Dynamic Scaling: Ability to automatically scale resources up or down based on real-time demand
• Cost Efficiency: Optimize resource usage to reduce operational costs
• Seamless Integration: The solution must integrate with existing infrastructure without major overhauls
• Reliability and Performance: Ensure consistent application performance and high availability

Design Phase

Solution Architecture

We proposed an auto-scaling solution leveraging KEDA, an open-source project that provides event-driven auto-scaling for Kubernetes workloads. The architecture included:

Core Components

• Kubernetes Cluster: Foundation for deploying and managing containerized applications
• KEDA: Event-driven auto-scaling based on various event sources
• Monitoring and Metrics: Integration with Prometheus and Grafana
• CI/CD Pipeline: Enhanced pipelines for streamlined updates and scaling policies

Proof of Concept (PoC)

We developed a PoC to demonstrate the feasibility and benefits of KEDA:

• Deployed a sample application simulating the client’s workload
• Configured KEDA scalers to respond to various metrics
• Ran stress tests to observe auto-scaling behavior
• Validated performance improvements and cost optimization

Implementation Phase

Setting Up the Environment

Cluster Configuration

• Provisioned Kubernetes cluster tailored to client’s needs
• Ensured high availability and security configurations
• Implemented network policies and access controls
• Established backup and disaster recovery procedures

KEDA Deployment

• Installed KEDA with necessary permissions
• Configured access to metrics sources
• Set up KEDA operators and controllers
• Validated KEDA installation and functionality

Application Migration

• Containerized client’s applications
• Deployed applications to Kubernetes cluster
• Configured service mesh for communication
• Implemented health checks and readiness probes

Auto-Scaling Configuration

Defining Metrics

We collaborated with the client to identify critical metrics that would drive scaling decisions:

• Queue depth for asynchronous processing
• CPU and memory utilization
• Custom application metrics (e.g., active sessions)
• Response time thresholds

Scaler Configuration

Set up KEDA scalers for various event sources:

• Azure Queue Storage for message-driven scaling
• Prometheus metrics for custom scaling rules
• HTTP triggers for API workloads
• CPU/Memory scalers as baseline protection

Policy Tuning

Fine-tuned scaling policies to balance performance and cost:

• Configured cooldown periods to prevent flapping
• Set appropriate scaling thresholds
• Established minimum and maximum replica limits
• Implemented predictive scaling for known patterns

Testing and Optimization

Extensive Testing

Load Testing

• Simulated peak loads to observe scaling responsiveness
• Validated application stability during scale-up
• Tested scale-down behavior during low-traffic periods
• Verified graceful handling of pod terminations

Failure Scenarios

• Tested behavior during node failures
• Validated recovery from network issues
• Simulated KEDA controller failures
• Ensured data integrity during disruptions

Performance Monitoring

• Used Prometheus and Grafana for real-time monitoring
• Analyzed scaling patterns and optimization opportunities
• Identified bottlenecks and configuration issues
• Made iterative adjustments based on data

Feedback and Iteration

Throughout the testing phase, we maintained close communication with the client, gathering feedback and iterating on the configuration to address any issues and optimize performance further.

Deployment and Training

Rollout

Staged Deployment

• Gradual rollout to minimize risks
• Started with non-critical workloads
• Expanded to critical healthcare services
• Maintained fallback procedures throughout

Monitoring and Support

• Continuous monitoring during initial weeks
• Quick response to any issues or anomalies
• Performance tuning based on real-world traffic
• Regular review meetings with stakeholders

Client Training

Knowledge Transfer

• Detailed training sessions for IT team
• Comprehensive documentation on KEDA management
• Hands-on workshops for troubleshooting
• Best practices for Kubernetes operations

Ongoing Support

• Established support channels
• Created runbooks for common scenarios
• Set up alerting and escalation procedures
• Provided access to our expert team

Results and Benefits

Improved Performance and Availability

The auto-scaling solution significantly improved the performance and availability of the client’s applications, especially during peak times, ensuring reliable service delivery.

Cost Savings

By dynamically adjusting resources based on demand, the client achieved substantial cost savings, reducing over-provisioning and optimizing resource usage.

Enhanced Operational Efficiency

Automation of the scaling process reduced the manual workload on the IT team, allowing them to focus on more strategic initiatives rather than reactive infrastructure management.

Conclusion

The implementation of KEDA for auto-scaling has transformed the client’s ability to manage fluctuating demands efficiently. This case study underscores the importance of a tailored, well-executed solution in addressing specific industry challenges and achieving operational excellence in the healthcare sector.

The healthcare provider can now confidently deliver digital services knowing their infrastructure will automatically adapt to patient needs, whether during routine operations or emergency situations.