Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance
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| Vydáno v: | arXiv.org (Dec 12, 2024), p. n/a |
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| Další autoři: | , , , |
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Cornell University Library, arXiv.org
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| On-line přístup: | Citation/Abstract Full text outside of ProQuest |
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3144197703 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3144197703 | ||
| 045 | 0 | |b d20241212 | |
| 100 | 1 | |a Md Nurul Absur | |
| 245 | 1 | |a Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 12, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Central processing units--CPUs | ||
| 653 | |a Algorithms | ||
| 653 | |a Performance measurement | ||
| 653 | |a Throttling | ||
| 653 | |a Content delivery networks | ||
| 653 | |a Resource management | ||
| 653 | |a Real time | ||
| 653 | |a Geographical locations | ||
| 653 | |a Distributed processing | ||
| 653 | |a Optimization | ||
| 653 | |a High definition | ||
| 653 | |a Network latency | ||
| 700 | 1 | |a Saha, Sourya | |
| 700 | 1 | |a Nova, Sifat Nawrin | |
| 700 | 1 | |a Kazi Fahim Ahmad Nasif | |
| 700 | 1 | |a Md Rahat Ul Nasib | |
| 773 | 0 | |t arXiv.org |g (Dec 12, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3144197703/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.09474 |