Adaptive Pull-Based Data Freshness Policies for Diverse Update Patterns
Adaptive Pull-Based Data Freshness Policies for Diverse Update Patterns
复制标题
适用于多种更新模式的自适应拉式数据新鲜度策略
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
L. Raschid
中科院分区：
文献类型：
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作者：
Laura Bright;A. Gal;L. Raschid
在广域环境中有效数据交付的一个重要挑战是，使用能够扩展到大量客户端且不会产生大量服务器开销的解决方案来保持对象的数据新鲜度。保持数据新鲜度的策略传统上要么是基于推送的，要么是基于拉取的。基于推送的策略涉及服务器推送数据更新；它们可能无法扩展到大量客户端。基于拉取的策略要求客户端联系服务器检查更新；它们的有效性受到预测更新难度的限制。预测更新的模型通常依赖于过去更新的一些知识。它们的预测准确性可能会有所不同，确定最合适的模型并非易事。在本文中，我们针对这一挑战提出了一种自适应的基于拉取的解决方案。我们首先提出了几种利用更新历史来估计缓存对象新鲜度的技术，并确定每种技术最有效的更新模式。然后我们引入自适应策略，这些策略可以（自动）根据观察到的对象更新模式为其选择一种策略。我们提出的策略提高了缓存数据的新鲜度，减少了与远程服务器的昂贵联系，且不会产生基于推送的策略所带来的大量服务器开销，并且能够扩展到大量客户端。通过使用来自一个数据密集型网站的跟踪数据以及两个电子邮件日志，我们表明我们的自适应策略能够适应不同的更新模式，并且与单一策略相比有显著的改进。
An important challenge to e ective data delivery in wide area environments is maintaining the data freshness of objects using solutions that can scale to a large number of clients without incurring signi cant server overhead. Policies for maintaining data freshness are traditionally either push-based or pull-based. Push-based policies involve pushing data updates by servers; they may not scale to a large number of clients. Pull-based policies require clients to contact servers to check for updates; their e ectiveness is limited by the diAEculty of predicting updates. Models to predict updates generally rely on some knowledge of past updates. Their accuracy of prediction may vary and determining the most appropriate model is non-trivial. In this paper, we present an adaptive pull-based solution to this challenge. We rst present several techniques that use update history to estimate the freshness of cached objects, and identify update patterns for which each technique is most e ective. We then introduce adaptive policies that can (automatically) choose a policy for an object based on its observed update patterns. Our proposed policies improve the freshness of cached data and reduce costly contacts with remote servers without incurring the large server overhead of push-based policies, and can scale to a large number of clients. Using trace data from a data-intensive website as well as two email logs, we show that our adaptive policies can adapt to diverse update patterns and provide signi cant improvement compared to a single policy.