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Pre-fetching and multiple pre-fetching: Multi-dimensional matrices and simple web object relationship model.

机译:预取和多次预取:多维矩阵和简单的Web对象关系模型。

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摘要

The rapidly increasing usage of the World Wide Web has resulted in an increase in load on the web servers, and the amount of data transferred over the network. In order to minimize network traffic, web-caching techniques are used. To complement web-caching and improve users' experience, the concept of pre-fetching has been introduced. Various pre-fetching techniques and models have been proposed in the recent years, however, each of these models have their limitations and advantages.;The proposed pre-fetching models and a web-cache simulator have, been implemented in Java to measure the efficiency of the models. In conclusion, no single model turns out to be the best in all situations. For caches sizes between 40/0-25%, the multi-dimensional matrix models achieve satisfactory results; whereas, for cache sizes as small as 2%, the two-dimensional single-prefetching model achieves better hit rates.;Keywords: Web Cache, Web-caching, Pre-Fetching, Multiple Pre-fetching, Hit Rate, Web Cache Simulator, User Perceived Latency (UPL), Matrix, Multi-Dimensional Matrices, Markov Tree;In this thesis, we have approached pre-fetching from two angles: simplicity and computational efficiency. We have built a Simple Web Object Relationship Model that is able to avoid complexity and the costs involved in black box models based on data mining, and other models that require frequent and intensive training. Inspired by the Markov Tree model for pre-fetching, we begin by building a simple tree structure for the Simple Web Object Relationship Model and then propose and build Multi-Dimensional Matrix models. From the best of my knowledge, the use of multi-dimensional matrices for pre-fetching models has been proposed for the first time.
机译:万维网的迅速增长导致Web服务器上的负载增加,并且导致通过网络传输的数据量增加。为了最小化网络流量,使用了Web缓存技术。为了补充Web缓存并改善用户体验,已经引入了预取的概念。近年来,已经提出了各种预取技术和模型,但是,每种模型都有其局限性和优势。;已在Java中实现了所提议的预取模型和Web缓存模拟器,以测量效率。的模型。总之,在所有情况下,没有哪个模型是最好的。对于大小在40 / 0-25%之间的缓存,多维矩阵模型可获得令人满意的结果。而对于大小仅为2%的缓存,二维单预取模型可实现更好的命中率。关键字:Web缓存,Web缓存,预取,多次预取,命中率,Web缓存模拟器,用户感知延迟(UPL),矩阵,多维矩阵,马尔可夫树;在本文中,我们从两个角度进行了预取:简单性和计算效率。我们已经建立了一个简单的Web对象关系模型,该模型能够避免基于数据挖掘的黑匣子模型以及其他需要频繁且深入培训的模型的复杂性和成本。受到马尔可夫树模型进行预取的启发,我们首先为简单Web对象关系模型构建一个简单的树结构,然后提出并构建多维矩阵模型。据我所知,首次提出将多维矩阵用于预取模型。

著录项

  • 作者

    Vij, Karan.;

  • 作者单位

    Trent University (Canada).;

  • 授予单位 Trent University (Canada).;
  • 学科 Computer Science.
  • 学位 M.Sc.
  • 年度 2010
  • 页码 127 p.
  • 总页数 127
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:36:51

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