On Non-Markovian Performance Models

TL;DR

This paper introduces a method for analyzing network or system performance models that do not follow the Markov property, addressing cases with long-range dependencies and complex behaviors.

Contribution

The paper proposes a novel approach for modeling non-Markovian performance systems, expanding analysis capabilities beyond traditional Markov-based methods.

Findings

Applicable to systems with long-range dependencies

Provides insights into non-Markovian performance behaviors

Enhances modeling flexibility for complex systems

Abstract

We present an approach that can be useful when the network or system performance is described by a model that is not Markovian. Although most performance models are based on Markov chains or Markov processes, in some cases the Markov property does not hold. This can occur, for example, when the system exhibits long range dependencies. For such situations, and other non-Markovian cases, our method may provide useful help.