摘要:随着移动互联网的飞速发展，用户越来越多地通过移动设备访问Web应用.浏览器为Web应用提供基本的计算、渲染等运行时支撑，其缓存机制可以支持Web应用直接从本地而不是通过网络来获取可复用资源，不仅能够减少整体的执行时间从而提升应用加载速度，还能够减少网络流量使用和电池电量消耗，从而保证移动Web用户体验.近年来，围绕面向移动Web应用的浏览器缓存优化得到了国内外学术界和工业界的广泛关注.然而，现有研究工作大多都是从网络层面关注浏览器缓存的整体性能，未充分考虑移动互联网用户访问行为的差异性和动态性，以及Web应用自身持续演化对浏览器实际缓存性能的影响.针对这一问题，首先设计了一种新型主动式缓存度量实验，通过仿真用户的访问行为来分析移动Web应用实际资源使用情况，揭示了浏览器缓存的理论性能上限和实际性能之间的巨大差距，并发现了造成这一差距的3个主要原因：重复请求别名资源、启发式过期时间和保守的过期时间配置.基于此发现，从应用层和平台层分别提出了两种浏览器缓存性能优化方案，并实现了原型系统.实验结果表明，采用两种方法分别平均可减少8%~51%和4%~58%的网络流量，且系统开销较小.



Abstract:With the rapid development of the mobile Internet, users are increasingly accessing Web applications through mobile devices. Browsers provide the runtime support for Web applications, such as computation and rendering. The browser cache supports Web applications to obtain reusable resources directly from the local storage rather than downloading from the network. It not only improves the application loading speed, but also reduces network traffic usage and battery power consumption, ensuring the experience of mobile Web users. In recent years, attentions have been paid by both industry and academy on optimizing the browser cache performance of mobile Web applications. However, most of the existing research work focuses on the overall performance of the browser cache from the network level, and does not fully consider the impact of user access behaviors and application evolution on the performance of the browser cache. To address the issue, this study designs a proactive measurement experiment, which simulates the user access behavior and obtains the resources of the mobile Web applications. Experiment results reveal the huge gap between the ideal and actual performance of the browser cache, and dig out three main root causes to the gap:Resource aliases, heuristic caching strategies, and conservative cache time configuration. Based on the findings, in order to improve the browser cache performance of mobile Web applications, this study also proposes two optimization methods from the application layer and the platform layer, respectively, and implements the corresponding prototype systems. Evaluation results show that the two proposed methods can save the network traffic by 8%~51% and 4%~58% on average, respectively, and the system overhead is small.



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