首页> 外文会议>IEEE 35th Annual IEEE International Conference on Computer Communications >Characterizing caching workload of a large commercial Content Delivery Network
【24h】

Characterizing caching workload of a large commercial Content Delivery Network

机译:表征大型商业内容交付网络的缓存工作负载

获取原文
获取原文并翻译 | 示例

摘要

Content Delivery Networks (CDNs) have emerged as a dominant mechanism to deliver content over the Internet. Despite their importance, to our best knowledge, large-scale analysis of CDN cache performance is lacking in prior literature. A CDN serves many content publishers simultaneously and thus has unique workload characteristics; it typically deals with extremely large content volume and high content diversity from multiple content publishers. CDNs also have unique performance metrics; other than hit ratio, CDNs also need to minimize network and disk load on cache servers. In this paper, we present measurement and analysis of caching workload at a large commercial CDN. Using detailed logs from four geographically distributed CDN cache servers, we analyze over 600 million content requests accounting for more than 1.3 petabytes worth of traffic. We analyze CDN workload from a wide range of perspectives, including request composition, size, popularity, and temporal dynamics. Using real-world logs, we also evaluate cache replacement algorithms, including two enhancements designed based on our CDN workload analysis: N-hit and content-aware caching. The results show that these enhancements achieve substantial performance gains in terms of cache hit ratio, disk load, and origin traffic volume.
机译:内容交付网络(CDN)已成为一种通过Internet交付内容的主要机制。尽管它们很重要,但据我们所知,在先前的文献中还缺乏对CDN缓存性能的大规模分析。 CDN同时为许多内容发布者提供服务,因此具有独特的工作负载特征;它通常处理来自多个内容发布者的极大的内容量和高度的内容多样性。 CDN还具有独特的性能指标;除了命中率之外,CDN还需要最大程度地减少缓存服务器上的网络和磁盘负载。在本文中,我们介绍了大型商业CDN上缓存工作量的测量和分析。使用来自四个地理位置分散的CDN缓存服务器的详细日志,我们分析了超过6亿个内容请求,这些请求的流量超过1.3 PB。我们从广泛的角度分析CDN工作负载,包括请求组成,大小,受欢迎程度和时间动态。使用真实日志,我们还评估了缓存替换算法,包括基于CDN工作负载分析设计的两项增强功能:N-hit和内容感知缓存。结果表明,这些增强功能在缓存命中率,磁盘负载和原始流量方面获得了可观的性能提升。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号