Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance
Md Nurul Absur, Sourya Saha, Sifat Nawrin Nova, Kazi Fahim Ahmad Nasif, Md Rahat Ul Nasib
TL;DR
This work addresses CDN scalability and ultra-low latency in edge and distributed environments by evaluating dynamic server selection, bandwidth throttling, real-time $RTT$ analysis, and programmatic delay simulation. It introduces a dynamic, multi-algorithm CDN framework and a multimetric evaluation workflow tested on the FABRIC testbed and edge-like systems, with an open RTT/$CPU$ dataset. The key contributions include algorithmic designs for DASH, MPD throttling, RTT-based server selection, and Linux delay modulation, plus a comprehensive multi-metric trade-off analysis across 4-, 8-, and 12-server configurations. The findings reveal clear trade-offs between scalability and resource consumption: larger server pools improve load stability but incur higher $RTT$ overhead due to inter-server coordination, underscoring the need for multi-metric decision-making in practical CDN deployments. This work provides actionable guidance for deploying resilient, scalable CDN architectures at the edge and in distributed networks.
Abstract
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.