优化CDN架构：面向边缘与分布式性能的多度量算法突破
Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance
AI总结：
该研究针对CDN面临的内容量增长与跨地域扩展难题，通过多类算法与两类实验评估CDN性能，揭示了可扩展性与资源消耗的权衡，为边缘分布式环境下CDN算法部署优化提供参考。
摘要：
内容分发网络（CDN）是由分布式缓存服务器组成的强大系统，旨在高效、快速地加速高清视频、物联网应用和超低延迟服务等内容的交付，这在后疫情时代变得至关重要。当内容量呈指数级增长，且需要跨不同地理位置实现可扩展性时，挑战随之而来。本文针对CDN算法开展数据驱动评估，研究方向包括：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，以及用于模拟各类场景的程序化网络延迟仿真。研究仔细分析了往返时间（RTT）和CPU使用率等关键性能指标，通过两种实验设置评估可扩展性与算法效率：一种是受限的类边缘本地系统，另一种是可扩展的FABRIC测试床。结果呈现了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算法提供了可行见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
