Web缓存中基于更新风险的TTL估计方法

Jeong-Joon Lee, K. Whang, B. Lee, Ji-Woong Chang
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引用次数: 24

摘要

Web缓存是通过本地缓存访问加速Web应用程序并减少Web服务器和网络负载的一项重要技术。与传统的数据缓存一样,Web缓存也带来了维护缓存一致性的问题。然而,Web缓存的优点是在Web服务器更新原始数据时延迟缓存刷新,也就是说,Web缓存在允许可容忍的不一致的情况下尝试获得更好的性能。这种弱一致性需求在面对未来更新时引入了生存时间(TTL:缓存数据项预期有效的时间)的概念。随后,发明了许多方法来让缓存服务器估计TTL。然而,两种著名的TTL估计方法——固定TTL方法和启发式方法——不允许直观地理解估计过程,并且缺乏背后的理论推理,不允许管理员按其意图配置缓存服务器。为了弥补这些不足,我们提出了基于更新风险的TTL估计方法。该方法使用一种基于概率分析的形式化但直观的方法。在提出的方法中,用户将更新风险作为原始数据在估计的TTL内更新的概率。然后,根据我们的模型,缓存服务器使用更新风险计算TTL的值。我们使用真实缓存服务器的日志执行的实验结果实验表明,测量的更新风险与用于估计TTL的风险非常接近。此外,更新风险的概念在其意图和语义上是明确的。这些都证实了我们的方法相对于传统方法的优越性。我们还展示了更新风险对性能和一致性的影响,以帮助管理员为更新风险选择合适的值,从而获得所需的性能和一致性。此外,我们根据我们的方法对上述两种传统方法进行了重新评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An update-risk based approach to TTL estimation in Web caching
Web caching is an important technique for accelerating Web applications and reducing the load on the Web server and the network through local cache accesses. As in traditional data caching, Web caching poses the well-recognized problem of maintaining cache consistency. Web caching, however, has the advantage of delaying the refreshment of caches when the Web server updates the original data, i.e., Web caching tries to get better performance allowing tolerable inconsistency. This weak consistency requirement introduced the concept of time-to-live (TTL: the time during which the cached data item is expected to be valid) in the face of future updates. Subsequently, a number of methods have been invented to have the cache server estimate the TTL. However, the two well-known TTL estimation methods - the fixed TTL method and the heuristic method - do not allow intuitive understanding of the estimation processes and lack theoretical reasoning behind them, disallowing administrators from configuring the cache server by their intention. To mend these deficiencies, we propose the update-risk based TTL estimation method. This method uses a formal, yet intuitive, approach based on probabilistic analysis. In the proposed method, users provide the update risk as the probability that the original data will be updated within the estimated TTL. Then, based on our model, the cache server calculates the value of TTL using the update risk. The results of our experiments, performed using logs of a real cache server, show experimentally that the measured update risk closely matches that used to estimate TTL. Moreover, the notion of update risk is clear in its intention and semantics. These confirm the superiority of our method to conventional ones. We also show the impact of update risk on performance and consistency in order to help administrators select an appropriate value for update risk to obtain performance and consistency desired. In addition, we reassess the two aforementioned conventional methods in light of our method.
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