Content delivery networks (CDNs) are the backbone of the Internet and are key
in delivering high quality video on demand (VoD), web content and file services
to billions of users. CDNs usually consist of hierarchically organized content
servers positioned as close to the customers as possible. CDN operators face a
significant challenge when analyzing billions of web server and proxy logs
generated by their systems. The main objective of this study was to analyze the
applicability of various clustering methods in CDN error log analysis. We
worked with real-life CDN proxy logs, identified key features included in the
logs (e.g., content type, HTTP status code, time-of-day, host) and clustered
the log lines corresponding to different host types offering live TV, video on
demand, file caching and web content. Our experiments were run on a dataset
consisting of proxy logs collected over a 7-day period from a single, physical
CDN server running multiple types of services (VoD, live TV, file). The dataset
consisted of 2.2 billion log lines. Our analysis showed that CDN error
clustering is a viable approach towards identifying recurring errors and
improving overall quality of service.

本研究分析了在 CDN 错误日志分析中采用各种聚类方法的适用性。我们使用真实的 CDN 代理日志，针对不同的主机类型，如直播电视，视频点播，文件缓存和 Web 内容，对相应的日志行进行了聚类。我们的分析表明，CDN 错误聚类是识别重复错误和提高服务总体质量的可行方法。