Adaptive pull-based policies for wide area data delivery
Adaptive pull-based policies for wide area data delivery
复制标题
用于广域数据传输的自适应拉动策略
DOI：
--
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
L. Raschid
中科院分区：
文献类型：
--
作者：
Laura Bright;A. Gal;L. Raschid
广域数据传输需要通过广域网及时将最新信息传播给数千个客户端。应用包括网络缓存、RSS源监测以及通过移动网络访问电子邮件。数据源的更新模式差异很大，在不同时间可能有不同的更新速率，或者更新模式会发生意外变化。传统的数据传输解决方案要么是基于推送的，这需要服务器将更新推送给客户端，要么是基于拉取的，这需要客户端在服务器上检查更新。虽然基于推送的解决方案能确保数据及时传输，但实施起来并不总是可行的，而且可能无法扩展到大量客户端。在本文中，我们提出了自适应的基于拉取的策略，与现有的基于拉取的策略相比，这些策略明确旨在减少与远程服务器联系的开销，同时满足时效性要求。我们使用更新历史对数据源的更新进行建模，并提出了两种基于历史的新策略来估计更新发生的时间；它们基于个体历史和总体历史。这些策略是在一个支持在客户端或服务器端部署的架构框架内提出的。我们还进一步开发了两种自适应策略来处理最初可能历史数据不足的对象或更新模式发生变化的对象。使用来自不同应用的三个数据轨迹进行的大量实验评估表明，与现有的基于拉取的策略相比，基于历史的策略可以将客户端和服务器之间的联系减少多达60%，同时提供相当的数据时效性。我们的实验还进一步证明，我们的自适应策略能够选择最佳策略来匹配对象的行为，并且比任何单一策略的性能都要好，因此它们优于独立的策略。
Wide area data delivery requires timely propagation of up-to-date information to thousands of clients over a wide area network. Applications include web caching, RSS source monitoring, and email access via a mobile network. Data sources vary widely in their update patterns and may experience different update rates at different times or unexpected changes to update patterns. Traditional data delivery solutions are either push-based, which requires servers to push updates to clients, or pull-based, which require clients to check for updates at servers. While push-based solutions ensure timely data delivery, they are not always feasible to implement and may not scale to a large number of clients. In this article, we present adaptive pull-based policies that explicitly aim to reduce the overhead of contacting remote servers, compared to existing pull-based policies, while meeting freshness requirements. We model updates to data sources using update histories, and present two novel history-based policies to estimate when updates occur; they are based on individual history and aggregate history. These policies are presented within an architectural framework that supports their deployment either client-side or server-side. We further develop two adaptive policies to handle objects that initially may have insufficient history or objects that experience changes in update patterns. Extensive experimental evaluation using three data traces from diverse applications shows that history-based policies can reduce contact between clients and servers by up to 60% compared to existing pull-based policies while providing a comparable level of data freshness. Our experiments further demonstrate that our adaptive policies can select the best policy to match the behavior of an object and perform better than any individual policy, thus they dominate standalone policies.