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LSPERF14-BP02 Evaluate multi-CDN architectures with intelligent traffic routing capabilities - Life Sciences Lens
Implementation guidance

LSPERF14-BP02 Evaluate multi-CDN architectures with intelligent traffic routing capabilities

Evaluate content delivery network (CDN) providers focusing on their performance in academic and research regions. Create a dynamic multi-CDN system that routes content based on real-time performance, content type, and destination. Test features like origin shields, cache optimization for scientific data, and API configuration. Assess providers' capability to handle specialized research needs including HD microscopy streaming, large dataset syncing, and real-time collaborative visualization. Consider combining commercial CDNs with research networks like Internet2 or GÉANT for optimal performance.

Desired outcome: You have a multi-CDN architecture optimized for research content delivery, with intelligent traffic routing and comprehensive performance monitoring. This enables efficient distribution of specialized research data while maintaining high performance across academic networks and research locations.

Level of risk exposed if this best practice is not established: Medium

Implementation guidance

Evaluate and select CDN providers based on their performance in academic and research regions. Analyze capabilities for handling specialized research content types and assess integration with research networks. Create comprehensive provider profiles documenting performance metrics, features, and integration capabilities with academic networks like Internet2 or GÉANT.

Design dynamic routing mechanisms that direct content based on real-time performance metrics, content type, and destination requirements. Implement routing policies that consider factors like latency, throughput, and regional performance patterns. Establish automated failover mechanisms for continuous content availability across multiple providers.

Configure CDN settings specifically for research and academic content types, including cache optimization for scientific datasets and streaming configurations for HD microscopy. Implement specialized handling for large dataset synchronization and real-time collaborative visualization tools. Create content-specific delivery rules that optimize performance for different research workflows.

Deploy comprehensive monitoring systems to track CDN performance across academic regions and research networks. Establish real-time performance dashboards and automated alerting systems for quick issue identification. Implement regular performance analysis cycles to continuously optimize content delivery across the multi-CDN architecture.

Implementation steps

  1. Deploy Amazon CloudFront as primary CDN with origin shields and cache optimization for research data, while implementing additional CDN providers with Internet2 connections and configuring origin failover for continuous availability.

  2. Set up Amazon Route 53 traffic flow policies for intelligent performance-based routing, deploy health checks for endpoint monitoring across academic regions, and implement latency-based routing to optimize delivery paths.

  3. Configure specialized cache policies for scientific datasets, API configurations for efficient research application requests, and streaming optimizations to support HD microscopy and real-time visualization needs.

  4. Establish comprehensive monitoring with performance tracking across academic regions, cost analysis for usage pattern optimization, and customized dashboards for content delivery efficiency visibility.

  5. Implement automated testing to regularly validate CDN performance across different research data types and geographic locations.

  6. Document CDN architecture with failover procedures and optimization strategies for different content categories and research applications.

  7. Conduct quarterly performance reviews to identify improvement opportunities and adjust configurations based on evolving research needs.