Temporal Correlations of Local Network Losses

TL;DR

This paper presents a continuum model for data losses in network nodes that captures critical behavior, fluctuations, and long-range correlations, providing insights into network loss dynamics.

Contribution

The introduced model uniquely preserves the discrete nature of data loss and reveals critical phenomena and non-Markovian correlations in network loss processes.

Findings

Model exhibits a sharp transition from small to finite losses.

Loss rate fluctuations are strong at the critical point.

The model shows non-Markovian power-law correlations in time.

Abstract

We introduce a continuum model describing data losses in a single node of a packet-switched network (like the Internet) which preserves the discrete nature of the data loss process. {\em By construction}, the model has critical behavior with a sharp transition from exponentially small to finite losses with increasing data arrival rate. We show that such a model exhibits strong fluctuations in the loss rate at the critical point and non-Markovian power-law correlations in time, in spite of the Markovian character of the data arrival process. The continuum model allows for rather general incoming data packet distributions and can be naturally generalized to consider the buffer server idleness statistics.