This paper examines the self-similarity of network traffic, particularly focusing on World Wide Web (WWW) traffic. The authors argue that self-similarity in network traffic can be explained by the heavy-tailed distributions of WWW document sizes, caching effects, user preferences, and user "think time." They use extensive data from WWW traffic, collected from NCSA Mosaic browser traces, to analyze the characteristics of WWW traffic. The data shows that WWW traffic exhibits behavior consistent with self-similar traffic models, which are characterized by long-range dependence and heavy-tailed distributions. The authors use four methods to test for self-similarity: variance-time plots, R/S plots, periodogram plots, and the Whittle estimator. These methods indicate that the Hurst parameter H for WWW traffic is significantly different from 0.5, consistent with self-similarity. The results show that WWW traffic is self-similar during busy hours, but not during less busy hours. The paper also discusses the reasons behind the self-similarity of WWW traffic. It suggests that the heavy-tailed distributions of file sizes, user request intervals, and user "think time" contribute to the self-similarity of WWW traffic. The authors argue that the heavy-tailed nature of file sizes and user behavior is a fundamental characteristic of information storage and processing, rather than a result of network protocols or user preference. The study concludes that the self-similarity of WWW traffic is not a machine-induced artifact but rather a result of the underlying characteristics of information organization and retrieval. The heavy-tailed distributions observed in WWW traffic are similar to those found in other domains, such as the distribution of book lengths and word lengths in texts. The results suggest that the self-similarity of WWW traffic is a fundamental property of the system, and changes in protocol processing or document display are unlikely to fundamentally remove this property.This paper examines the self-similarity of network traffic, particularly focusing on World Wide Web (WWW) traffic. The authors argue that self-similarity in network traffic can be explained by the heavy-tailed distributions of WWW document sizes, caching effects, user preferences, and user "think time." They use extensive data from WWW traffic, collected from NCSA Mosaic browser traces, to analyze the characteristics of WWW traffic. The data shows that WWW traffic exhibits behavior consistent with self-similar traffic models, which are characterized by long-range dependence and heavy-tailed distributions. The authors use four methods to test for self-similarity: variance-time plots, R/S plots, periodogram plots, and the Whittle estimator. These methods indicate that the Hurst parameter H for WWW traffic is significantly different from 0.5, consistent with self-similarity. The results show that WWW traffic is self-similar during busy hours, but not during less busy hours. The paper also discusses the reasons behind the self-similarity of WWW traffic. It suggests that the heavy-tailed distributions of file sizes, user request intervals, and user "think time" contribute to the self-similarity of WWW traffic. The authors argue that the heavy-tailed nature of file sizes and user behavior is a fundamental characteristic of information storage and processing, rather than a result of network protocols or user preference. The study concludes that the self-similarity of WWW traffic is not a machine-induced artifact but rather a result of the underlying characteristics of information organization and retrieval. The heavy-tailed distributions observed in WWW traffic are similar to those found in other domains, such as the distribution of book lengths and word lengths in texts. The results suggest that the self-similarity of WWW traffic is a fundamental property of the system, and changes in protocol processing or document display are unlikely to fundamentally remove this property.