Verification of Web Traffic Burstiness and Self-similarity for Multiple Online Stores

被引：4

作者：

Suchacka, Grazyna ^{[1
]}

Dembczak, Alicja ^{[1
]}

机构：

[1] Univ Opole, Inst Math & Comp Sci, Oleska 48, PL-45052 Opole, Poland

来源：

INFORMATION SYSTEMS ARCHITECTURE AND TECHNOLOGY, PT I | 2018年 / 655卷

关键词：

Web traffic; HTTP traffic; Web server; E-Commerce; Web store; Log analysis; Burstiness; Self-Similarity; Hurst parameter; Hurst index; LONG-RANGE DEPENDENCE; PERFORMANCE; INTERNET; BURSTY;

D O I：

10.1007/978-3-319-67220-5_28

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Developing realistic Web traffic models is essential for a reliable Web server performance evaluation. Very significant Web traffic properties that have been identified so far include burstiness and self-similarity. Very few relevant studies have been devoted to e-commerce traffic, however. In this paper, we investigate burstiness and self-similarity factors for seven different online stores using their access log data. Our findings show that both features are present in all the analyzed e-commerce datasets. Furthermore, a strong correlation of the Hurst parameter with the average request arrival rate was discovered (0.94). Estimates of the Hurst parameter for the Web traffic in the online stores range from 0.6 for low traffic to 0.85 for heavy traffic.

引用

页码：305 / 314

页数：10

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