Web请求预测模型在移动平台上的可行性评估
Assessing the Feasibility of Web-Request Prediction Models on Mobile Platforms
摘要：
预取网页是一种研究得很好的解决方案，它通过预测用户过去的行为来减少网络延迟。然而，这些技术在移动平台上基本上是未开发的。今天的隐私法规使得使用通常的策略来探索预取变得不可行，即在长时间内积累大量数据并构建传统的“大型”预测模型。我们的工作是基于这样一种观察，即这可能是不必要的：考虑到以前报告的移动设备使用趋势（例如，重复的行为在短暂的突发），我们假设预取应该有效地与“小”模型训练的移动用户的请求在更短的时间内收集。为了验证这一假设，我们构建了一个自动评估预测模型的框架，并使用它进行了一项广泛的实证研究，该研究基于在24小时内从近11，500名移动用户收集的超过1500万个HTTP请求，产生了超过700万个模型。我们的研究结果证明了在移动平台上使用小模型进行预取的可行性，直接激励了这一领域的未来工作。我们进一步介绍了几种策略，以改善预测模型，同时减少模型的大小。最后，我们的框架为未来在一系列使用场景中探索有效的预测模型提供了基础。
英文摘要：
Prefetching web pages is a well-studied solution to reduce network latency by predicting users' future actions based on their past behaviors. However, such techniques are largely unexplored on mobile platforms. Today's privacy regulations make it infeasible to explore prefetching with the usual strategy of amassing large amounts of data over long periods and constructing conventional, "large" prediction models. Our work is based on the observation that this may not be necessary: Given previously reported mobile-device usage trends (e.g., repetitive behaviors in brief bursts), we hypothesized that prefetching should work effectively with "small" models trained on mobile-user requests collected during much shorter time periods. To test this hypothesis, we constructed a framework for automatically assessing prediction models, and used it to conduct an extensive empirical study based on over 15 million HTTP requests collected from nearly 11,500 mobile users during a 24-hour period, resulting in over 7 million models. Our results demonstrate the feasibility of prefetching with small models on mobile platforms, directly motivating future work in this area. We further introduce several strategies for improving prediction models while reducing the model size. Finally, our framework provides the foundation for future explorations of effective prediction models across a range of usage scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
