Limit theorems and estimation for structural and aggregate teletraffic models

The thesis proposes models for aggregate data network traffic which incorporate the additional randomness arising from the randomness in the number of data sources. A conditionally-Gaussian scale mixture process is shown to be a limit for the cumulative work from a random superposition of alternating on-off processes. Sub-Fractional Brownian Motion is shown to be the limit in a particular case. Queueing and estimation results for processes which are conditionally Fractional Gaussian Noise are included. A model with a superposition of alternating on-off processes with independent lifetimes is also considered.