Designed and implemented a scalable infrastructure solution enabling a media company to handle 10 million concurrent users during peak events.
10M users
Concurrent Users
1000% capacity increase
120ms average
Response Time
75% improvement
99.99%
Uptime
99.5% to 99.99%
The Challenge
StreamGlobal Entertainment needed to handle massive traffic spikes during live events, with their existing infrastructure failing to support more than 1 million concurrent users.
Infrastructure failures during high-traffic events
Poor user experience with buffering and timeouts
Limited auto-scaling capabilities
High costs during peak usage periods
Global content delivery challenges
Lack of real-time performance monitoring
Our Approach
Built a globally distributed, auto-scaling infrastructure with edge computing, advanced caching, and real-time monitoring capabilities.
AWSCloudFlareKubernetesRedisMongoDBElasticsearchPrometheus
Implementation Timeline
Total Duration: 24 weeks implementation
1
Architecture Design
4 weeks
- Traffic pattern analysis and forecasting
- Global infrastructure architecture design
- Technology selection and capacity planning
- Performance benchmarking and testing strategy
2
Core Infrastructure
8 weeks
- Multi-region cloud infrastructure setup
- Auto-scaling and load balancing implementation
- Database clustering and optimization
- Caching layer implementation
3
Content Delivery & Edge
6 weeks
- Global CDN configuration and optimization
- Edge computing deployment
- Content caching strategies
- Performance optimization
4
Monitoring & Optimization
6 weeks
- Real-time monitoring system deployment
- Performance analytics implementation
- Automated scaling policies setup
- Load testing and optimization
Technical Architecture
Globally distributed, cloud-native architecture with auto-scaling, edge computing, and advanced caching for optimal performance.
Multi-region AWS deployment with auto-scaling
CloudFlare CDN for global content delivery
Kubernetes for container orchestration
Redis for high-performance caching
MongoDB for distributed data storage
Prometheus for metrics and monitoring
Results & Impact
10M users
Concurrent Users
1000% capacity increase
120ms average
Response Time
75% improvement
99.99%
Uptime
99.5% to 99.99%
45% reduction
Cost Efficiency
Per-user cost optimization
<200ms worldwide
Global Latency
60% improvement
Business Benefits
Handled record-breaking live event traffic
Improved user experience and engagement
Reduced infrastructure costs through optimization
Enhanced global content delivery performance
Increased platform reliability and availability
Enabled real-time analytics and insights
“The infrastructure transformation enabled us to deliver our biggest live event ever to 10 million concurrent viewers without a single issue. The platform performed flawlessly under extreme load.”
Jennifer Martinez
VP of Engineering, StreamGlobal Entertainment
Key Learnings
Global distribution is essential for media applications
Caching strategies significantly impact performance at scale
Auto-scaling policies must be carefully tuned for traffic patterns
Real-time monitoring is crucial for managing large-scale events
Recommendations
Implement aggressive caching at multiple layers
Design for peak load, not average load
Use global CDN with intelligent traffic routing
Invest in comprehensive real-time monitoring and alerting
InfrastructureScalingMediaCDNPerformance