In healthcare, the ability to scale services and ensure reliable delivery of applications is highly crucial. HealthTech platforms, tasked with handling sensitive patient data and providing critical services, face unique challenges that demand innovative solutions. This is where automated Kubernetes optimization comes into play.
This case study talks about Azilen’s engagement with one of its clients for collaborative DevSecOps Engineering for an AI-enabled diagnostic platform that detects cognitive impairments early on, allowing for faster diagnosis and patient care.
Let us discover some details about the DevSecOps enterprise practices that we pursued to configure advanced operations monitoring for more than 100+ clinics and hospitals running with SaaS-based deployment.
Automated Deployment with Zero Down Time
Serverless Architecture to Meet the Scale
Zero Trust Infrastructure Creation
Real-Time Log Monitoring with Alerts
Automated Scripts Using Python
Container Orchestration Using Kubernetes
The client is a research and innovation-focused company that provides cognitive screening solutions through the most sensitive test available for detecting neurological disease.
A collaborative deployment ecosystem was developed for container orchestration to ensure efficient resource utilization and eliminate Kubernetes node scaling lag by leveraging pod priority and over-provisioning strategies to maintain consistent performance even during peak demand.
Faster time-to-market
with automated deployment
Robust cluster scaling
with resource utilization
Improved availability with reduced downtime
Before adopting Kubernetes, the client had a monolithic architecture with a traditional deployment approach. Their infrastructure was hosted on Azure Cloud, making it challenging to scale, manage, and maintain. This reliance on manual intervention for updates or feature additions led to downtime and a slow development cycle.
As a DevSecOps Engineering Partner, Azilen comprehensively assessed their existing infrastructure and architecture, we proposed a transition to a Microservices Architecture and adopted Kubernetes to streamline deployment processes. This approach ensures scalability, reliability, and competitive advantage through the effective deployment of microservices.
The aim was to achieve a world-class HealthTech solution that is scalable, maintainable, and works in highly automated mode from deployment to operations. Considering the Microsoft tech stack and the Azure environment and to ensure cutting-edge technology for scaling, the platform was propelled with Kubernetes-based implementation using Azure Kubernetes service.
“Kubernetes helps Pinterest deploy projects faster & build failover policies.”
Michael Benedict, Product Manager at Pinterest
Below is a comprehensive overview of the Kubernetes implementation, designed to streamline DevSecOps processes and manage the risk of human errors in deployment. We independently scaled each service to achieve granular control over workload performance through pod optimization and node optimization.
1️⃣ The Kubernetes clusters were auto-scaled, and node pools were managed through node auto-provisioning, which includes Horizontal Scaling, Vertical Scaling, and Cluster/Multidimensional Scaling.
2️⃣ The Azure Kubernetes service was augmented with state-of-the-art tools and mechanisms, right from automated deployment and role-based access control to monitoring the deployment operations.
Key Highlights of the Kubernetes Implementation
✅ We have Implemented taint tolerance in Kubernetes to ensure that nodes can handle varying workloads from 0.5M to 1 M requests monthly, without compromising overall cluster stability.
✅ The Node affinity in our Kubernetes setup directs pods to specific nodes based on defined criteria, optimizing resource allocation and performance.
✅ We have configured auto-scaling for both pods and clusters in Kubernetes, allowing our infrastructure to adapt dynamically to changing demand loads from 10K to 15K requests per minute.
✅ Created a private repository within Kubernetes to ensure secure storage and access control for sensitive application images and resources.
✅ We have utilized disposable build agents in Kubernetes to streamline our development process by providing ephemeral environments for continuous integration and delivery.
✅ Kubernetes RBAC (Role-Based Access Control) to ensure that only authorized users and services have the necessary permissions to interact with cluster resources.
✅ We have implemented the deployment strategy in Kubernetes that involves orchestrating pods to efficiently roll out updates and manage application instances.
✅ The Configured Ingress in Kubernetes allows us to route external traffic to the appropriate services within the cluster, enhancing accessibility and security.
✅ We have used replicas in Kubernetes to enable us to maintain high availability and scalability by running multiple instances of pods to handle increased workloads.
✅ The Integration of monitoring with Data Dog in Kubernetes provides comprehensive visibility into cluster health and performance, facilitating proactive management and troubleshooting.
We have followed a security-first approach for code quality control with auto-scalable code deployments in CI/ CD pipelines, ensuring comprehensive and robust code quality across the organization’s projects.
Below are the Key Highlights of the Security First Approach:
➡️ Automated code vulnerability detection (SonarQube with Jenkins).
➡️ Fully automated static code analysis along with detailed reports.
➡️ Custom quality gates based on different project requirements.
➡️ Improved code consistency along with early bug detection & prevention.
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