6533b856fe1ef96bd12b2646
RESEARCH PRODUCT
Verification of Web traffic burstiness and self-similarity for multiple online stores
Grażyna SuchackaAlicja Dembczaksubject
Web serverSelf-similarityComputer scienceSelf-Similarity02 engineering and technologyE-commerceWeb trafficcomputer.software_genreE-Commerce01 natural sciences010104 statistics & probabilityHurst parameterWeb trafficWeb server0202 electrical engineering electronic engineering information engineeringRange (statistics)Web storeBurstiness0101 mathematicsLog analysisbusiness.industry020206 networking & telecommunicationsHurst indexBurstinessHTTP trafficbusinesscomputerComputer networkdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-09-02 |