Steady state availability general equations of decision and sequential processes in Continuous Time Markov Chain models

TL;DR

This paper develops general equations for steady state availability in Continuous Time Markov Chain models, simplifying the process of analyzing system performance and capacity in various fields.

Contribution

It introduces a unified approach with general equations for decision and sequential processes in CTMC models, including the novel Closed Decision Process.

Findings

Derived general equations for decision processes in CTMCs

Formulated the Closed Decision Process equation

Facilitates easier steady state availability analysis

Abstract

Continuous Time Markov Chain (CMTC) is widely used to describe and analyze systems in several knowledge areas. Steady state availability is one important analysis that can be made through Markov chain formalism that allows researchers generate equations for several purposes, such as channel capacity estimation in wireless networks as well as system performance estimations. The problem with this kind of analysis is the complex process to generating these equations. In this letter, we have developed general equations for decision and sequential processes of CMTC Models, aiming to help researchers to develop steady state availability equations. We also have developed the general equation here termed as Closed Decision Process.