【24h】

About the Heterogeneity of Web Prefetching Performance Key Metrics

机译:关于Web预取性能关键指标的异构性

获取原文
获取原文并翻译 | 示例

摘要

Web prefetching techniques have pointed to be especially important to reduce web latencies and, consequently, an important set of works can be found in the open literature. But, in general, it is not possible to do a fair comparison among the proposed prefetching techniques due to three main reasons: ⅰ) the underlying baseline system where prefetching is applied differs widely among the studies; ⅱ) the workload used in the presented experiments is not the same; ⅲ) different performance key metrics are used to evaluate their benefits. This paper focuses on the third reason. Our main concern is to identify which the main meaningful indexes are when studying the performance of different prefetching techniques. For this purpose, we propose a taxonomy based in three categories, which permits us to identify analogies and differences among the indexes commonly used. In order to check, in a more formal way, the relation between them, we run experiments and estimate statistically the correlation among a representative subset of those metrics. The statistical results help us to suggest which indexes should be selected when performing evaluation studies depending on the different elements in the considered web architecture. The choice of the appropriate key metric is of paramount importance for a correct and representative study. As our experimental results show, depending on the metric used to check the system performance, results can not only widely vary but also reach opposite conclusions.
机译:Web预取技术已指出对于减少Web延迟特别重要,因此,可以在公开文献中找到一组重要的工作。但是,总的来说,由于以下三个主要原因,不可能在提议的预取技术之间进行公平的比较:pre)研究中应用预取的基础基线系统在研究中差异很大; ⅱ)实验中所使用的工作量不一样; ⅲ)不同的绩效关键指标用于评估其优势。本文重点讨论第三个原因。我们主要关注的是在研究不同预取技术的性能时,确定哪些主要有意义的索引是。为此,我们提出了基于三类的分类法,这使我们能够识别常用指标之间的类比和差异。为了以更正式的方式检查它们之间的关系,我们进行了实验并统计地估算了这些指标的代表性子集之间的相关性。统计结果可帮助我们建议在进行评估研究时,根据所考虑的Web体系结构中的不同元素,应选择哪些指标。对于正确和具有代表性的研究,选择适当的关键指标至关重要。如我们的实验结果所示,根据检查系统性能的指标,结果不仅会有很大差异,而且得出相反的结论。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号