Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
On the self-similar nature of Ethernet traffic
SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
Queue response to input correlation functions: discrete spectral analysis
IEEE/ACM Transactions on Networking (TON)
Queue response to input correlation functions: continuous spectral analysis
IEEE/ACM Transactions on Networking (TON)
Analysis, modeling and generation of self-similar VBR video traffic
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
An algorithm for lossless smoothing of MPEG video
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Link capacity allocation and network control by filtered input rate in high-speed networks
IEEE/ACM Transactions on Networking (TON)
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 2)-Volume - Volume 2
The linearity of low frequency traffic flow: an intrinsic I/O property in queueing systems
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 2)-Volume - Volume 2
Timescale of interest in traffic measurement for link bandwidth allocation design
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
IEEE Journal on Selected Areas in Communications
Trace data characterization and fitting for Markov modeling
Performance Evaluation
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This paper develops fast algorithms for construction of circulant modulated rate process to match with two primary traffic statistical functions: distribution $f(x)$ and autocorrelation $R(\tau)$ of the rate process. Using existing modeling techniques, $f(x)$ has to be limited to certain forms such as Gaussian or binomial; $R(\tau)$ can only consist of one or two exponential terms which are often real exponentials rather than complex. In reality, these two functions are collective from real traffic traces and generally expressed in much complicated form. Our emphasis here is placed on the algorithmic design for matching complicated $R(\tau)$ in traffic modeling. The typical CPU time for the traffic modeling with $R(\tau)$ consisting of five or six complex exponential terms is found in the range of a few minutes by the proposed algorithms. Our study further shows an excellent agreement between original traffic traces and sequences generated by the matched analytical model.