The effectiveness of request redirection on CDN robustness

Author:

Wang Limin1,Pai Vivek1,Peterson Larry1

Affiliation:

1. Princeton University

Abstract

It is becoming increasingly common to construct network services using redundant resources geographically distributed across the Internet. Content Distribution Networks are a prime example. Such systems distribute client requests to an appropriate server based on a variety of factors---e.g., server load, network proximity, cache locality--in an effort to reduce response time and increase the system capacity under load. This paper explores the design space of strategies employed to redirect requests, and defines a class of new algorithms that carefully balance load, locality, and proximity. We use large-scale detailed simulations to evaluate the various strategies. These simulations clearly demonstrate the effectiveness of our new algorithms, which yield a 60--91% improvement in system capacity when compared with the best published CDN technology, yet user-perceived response latency remains low and the system scales well with the number of servers.

Publisher

Association for Computing Machinery (ACM)

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Dissecting the Applicability of HTTP/3 in Content Delivery Networks;2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS);2024-07-23

2. Rentable CDN Using Blockchain and Proof of Provenance;Applied Sciences;2020-09-20

3. A Client Bootstrapping Protocol for DoS Attack Mitigation on Entry Point Services in the Cloud;Security and Communication Networks;2020-07-23

4. UiTiOt-Vlab: A Low Cost Physical IoTs Testbed Based on Over-The-Air Programming Approach;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2019

5. Evolution and challenges of DNS-based CDNs;Digital Communications and Networks;2018-11

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3