High-Performance Telecommunications Service Delivery Platform

The Challenge

The Service Delivery Platform (SDP) is a critical telecommunications infrastructure handling SMS, USSD, and Billing As Service (BAS) transactions. The legacy system faced severe performance bottlenecks that threatened service reliability during peak traffic periods.

The Solution

Led a comprehensive performance optimization initiative spanning 5+ years, rebuilding core components with modern best practices and data-driven optimization strategies.

Platform Architecture Overview

Architectural Decisions

1. Microservices Architecture: Split monolithic components into focused modules:

   • core-logic: Business rules and message routing
   • integration-layer: Protocol transformation and integration
   • session-gateway: Session management and gateway communication
   • ha-client: High-availability HTTP client with multi-host support
   • validator: Subscriber validation service

2. Persistent Buffering: Replaced in-memory queues with persistent embedded key-value storage:

   • Handles message bursts during traffic spikes
   • Survives system restarts with message recovery
   • Optimized with large write buffers and block cache

3. Multi-Host High Availability: Implemented round-robin load balancing:

   • Automatic failover between primary/secondary hosts
   • Configurable retry strategies
   • Health check monitoring

4. Transaction Deduplication: Database-level duplicate detection prevents reprocessing:

   • Unique transaction ID validation
   • Immediate error response for duplicates
   • Reduces unnecessary downstream calls

Key Contributions & Problem Solutions

Tech Stack

Core Technologies:

• Runtime: OpenJDK, Spring Boot
• Database: Relational Database System, Embedded Key-Value Store
• Integration: ESB, SOAP, REST
• Build: Maven
• Monitoring: Custom metrics, Log4j2

Impact & Results

Performance Improvements

Throughput:

• Before: Low throughput (artificially limited)
• After: High throughput (500+ msg/sec)
• Improvement: 10x+ increase

Latency:

• Queue Wait: Reduced by 75%
• DB Writes: Reduced by 88% (Optimized bulk ops)
• Overall Processing: Reduced by 95%

Resource Efficiency:

• CPU Usage: Reduced by 50%
• GC Overhead: Reduced by 75% through tuning
• Memory: Optimized heap usage, reduced allocations

System Reliability

• Zero Downtime: Handled peak traffic during major events
• Scalability: System can now handle massive traffic spikes without degradation
• Cost Reduction: Significant reduction in infrastructure costs through efficient resource utilization
• Error Rate: Maintained <0.1% error rate under high load
• Security: End-to-end encryption across all services

Key Learnings

1. Data-Driven Optimization: Comprehensive metrics collection revealed the real bottlenecks (GC thrashing) rather than assumed ones.
2. Runtime Tuning: Proper GC configuration can yield massive resource savings without code changes.
3. Architecture Matters: Decoupling components and using appropriate storage engines (KV store for buffering) is critical for high throughput.
4. Security Integration: Proactive security enhancements should be integral to the development lifecycle.