Distributed Authentication Platform

Enterprise-grade authentication platform built for high-scale systems: 7K+ TPS, multi-region resiliency, and zero-downtime operation.

Program case study based on a production platform. Metrics below are from that program. The Terraform under infra/ is an illustrative skeleton of the topology, not the production code.

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The problem

Large-scale platforms run authentication as a single point of failure. Under traffic spikes and regional outages, tightly coupled legacy auth services fail, causing login outages, poor user experience, and high operational risk during incidents and migrations.

The solution

A cloud-native authentication platform on Kubernetes with multi-region active/passive topology, high-availability data replication, intelligent failover routing, and a degraded login mode that preserves access during partial outages.

Architecture

flowchart TD
    U[Global users] --> GLB[Global load balancer]
    GLB --> RA[Region A - Active<br/>GKE cluster - Auth services]
    GLB -. failover .-> RB[Region B - Passive<br/>GKE cluster - Standby services]
    RA --> REP[(HA data replication)]
    RB --> REP
    RA --> OBS[Observability<br/>metrics - logs - alerts]
    RB --> OBS

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How it works

Auth services run on multi-region GKE clusters behind a global load balancer. A replication layer keeps identity data consistent across regions. On failure, traffic reroutes to the healthy region and degraded-mode logic preserves core login. Observability surfaces real-time health and drives failover decisions.

Evidence and measurement

• 7K+ TPS / LPS: measured under sustained load tests against the active region during capacity validation, over a fixed observation window. Peak production traffic informed the target.
• 99.9% availability: computed from monthly successful-request ratio across regions, excluding planned maintenance windows.
• These figures are from the production program and are reported here as program outcomes, not as live metrics of this repository.

Tradeoffs and decisions

Active/passive over active/active: chosen to reduce complexity and data-consistency risk during initial rollout. The cost is idle standby capacity and a short failover window.

What I would do differently: move to active/active with global load balancing and anomaly-driven proactive failover.

What I learned

• Designing for failure first improves reliability far more than adding capacity.
• Cross-team dependencies are harder than the technical problem.
• Degraded modes are essential for real-world resilience.

Next steps

• Active/active multi-region topology
• Intelligent, latency-aware traffic routing
• Anomaly detection for proactive failover

Built with

Kubernetes (GKE) | Terraform | Cloud IAM | Global load balancing | Observability stack

Repository contents

• README.md - this case study
• infra/main.tf - illustrative Terraform skeleton of the multi-region GKE topology

Author

Gaurav Kumar | LinkedIn