On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
SIGCOMM '95 Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Experimental queueing analysis with long-range dependent packet traffic
IEEE/ACM Transactions on Networking (TON)
Proof of a fundamental result in self-similar traffic modeling
ACM SIGCOMM Computer Communication Review
Self-similarity and heavy tails: structural modeling of network traffic
A practical guide to heavy tails
Real-time estimation of the parameters of long-range dependence
IEEE/ACM Transactions on Networking (TON)
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 3
Wavelet analysis of long-range-dependent traffic
IEEE Transactions on Information Theory
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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Good traffic modeling is a basic requirement for accurate capacity planning. The recent discovery of heavy-tails, and long-range dependence (LRD) in traffic has heralded a new, and more elegant way to model data traffic, particularly characteristics such as extreme burstiness across many time scales. However, most of the measurements used to populate such models have been fine grained packet traces. In reality we are far from being able to obtain such traces from more than a small subset of the Internet, and this is likely to remain true at least in the immediate future. The only source of ubiquitous data is Simple Network Management Protocol (SNMP), but SNMP has many limitations which make it difficult to work with for traffic modeling. These limitations make it impossible to use standard LRD models. However, we show here that for broadband access, SNMP is capable of capturing the most important features of the data traffic. We base this analysis on a large volume (more than 2 months) of SNMP data obtained from a large operating broadband access network. The model is approximate, but is nonetheless quite useful for capacity planning. The results validate our intuition about LRD in data traffic, while allowing the key parameters of the model to be computed solely from SNMP traffic utilization data.