A highly resilient backend deployment orchestrated on Kubernetes. This project demonstrates advanced
cluster management, including Horizontal Pod Autoscaling (HPA) driven by custom metrics.
The system effectively handles traffic spikes while optimizing cost by scaling down during
low-activity periods.
- Containerization: Optimized Docker images for microservices, ensuring
lightweight and secure runtime environments.
- Helm Charts: Templated deployment configurations for consistent releases across
different namespaces and clusters.
- Monitoring Stack: Integrated Prometheus for metric collection and Grafana for
real-time visualization of cluster health and application performance.
- Auto-Scaling Logic: Configured HPA to scale pods based not just on CPU/RAM, but
also on request latency and queue depth.
Achieved 99.99% uptime during high-load stress testing. The auto-scaling mechanism reduced cloud
operational costs by approximately 30% by dynamically adjusting resource allocation.