On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Connection-level analysis and modeling of network traffic
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Congestion control for high bandwidth-delay product networks
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
On the characteristics and origins of internet flow rates
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
On the relationship between file sizes, transport protocols, and self-similar network traffic
ICNP '96 Proceedings of the 1996 International Conference on Network Protocols (ICNP '96)
Scalable TCP: improving performance in highspeed wide area networks
ACM SIGCOMM Computer Communication Review
Understanding Internet traffic streams: dragonflies and tortoises
IEEE Communications Magazine
Wide-area Internet traffic patterns and characteristics
IEEE Network: The Magazine of Global Internetworking
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We shed light on the effect of network resources and user behavior on network traffic through a physically motivated model. The classical on-off model successfully captures the long-range, second-order correlations of traffic, allowing us to conclude that transport protocol mechanisms have little influence at time scales beyond the round trip time. However, the on-off model fails to capture the short-range spikiness of traffic, where protocols and congestion control mechanisms have greater influence. Based on observations at the connection-level we conclude that small rate sessions can be characterized by independent duration and rate, while large rate sessions have independent file size and rate. In other words, user patience is the limiting factor of small bandwidth connections, while users with large bandwidth freely choose their files. We incorporate these insights into an improved two-component on-off model--which we call the alpha-beta on-off model-comprising an aggressive alpha component (high rate, large transfer) and passive beta component (residual). We analyze the performance of our alpha-beta on-off model and use it to better understand the causes of burstiness and long-range dependence in network traffic. Our analysis yields new insights on Internet traffic dynamics, the effectiveness of congestion control, the performance of potential future network architectures, and the key parameters required for realistic traffic synthesis.