A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments.

翻译：内容分发网络（CDN）是一种强大的分布式缓存服务器系统，旨在高效、高速地加速高清视频、物联网应用和超低延迟服务等内容的分发。这在后疫情时代变得至关重要。当内容量呈指数级增长且需要在不同地理位置实现可扩展性时，挑战随之而来。本文研究了CDN算法在以下方面的数据驱动评估：用于降低延迟的动态服务器选择、用于高效资源管理的带宽节流、用于自适应路由的实时往返时间分析，以及用于模拟各种条件的可编程网络延迟仿真。通过两个实验设置——受限的边缘式本地系统和可扩展的FABRIC测试平台——仔细分析了往返时间（RTT）和CPU使用率等关键性能指标，以评估可扩展性和算法效率。结果展示了RTT趋势与CPU利用率的统计验证。优化过程揭示了可扩展性与资源消耗之间的显著权衡，为在边缘和分布式计算环境中有效部署和增强CDN算法提供了可行的见解。