Integration of streaming services and TCP data transmission in the Internet

TL;DR

This paper derives a power series expansion for the mean bit rate of data transfers sharing bandwidth with streaming traffic in IP networks, revealing the impact of streaming variability on data transfer performance.

Contribution

It introduces a mathematical model that computes the mean data transfer rate considering streaming traffic as a small perturbation, providing explicit formulas and insights into system behavior.

Findings

First-order approximation confirms the reduced service rate hypothesis.

Second-order term quantifies the negative impact of streaming variability.

Closed-form formulas enable precise analysis of bandwidth sharing effects.

Abstract

We study in this paper the integration of elastic and streaming traffic on a same link in an IP network. We are specifically interested in the computation of the mean bit rate obtained by a data transfer. For this purpose, we consider that the bit rate offered by streaming traffic is low, of the order of magnitude of a small parameter \eps \ll 1 and related to an auxiliary stationary Markovian process (X(t)). Under the assumption that data transfers are exponentially distributed, arrive according to a Poisson process, and share the available bandwidth according to the ideal processor sharing discipline, we derive the mean bit rate of a data transfer as a power series expansion in \eps. Since the system can be described by means of an M/M/1 queue with a time-varying server rate, which depends upon the parameter \eps and process (X(t)), the key issue is to compute an expansion of the area swept under the occupation process of this queue in a busy period. We obtain closed formulas for the power series expansion in \eps of the mean bit rate, which allow us to verify the validity of the so-called reduced service rate at the first order. The second order term yields more insight into the negative impact of the variability of streaming flows.