Mean-Field Analysis of Markov Models with Reward Feedback

On-o Markov Reward Models

The analysis of Markov Reward Models with preemptive resume policy usually results in a double transform expression, whose solution is based on an inverse transformation, both in the time and in the reward variable domains. This paper discusses the case when the reward rates can be described only by 0 or positive c values. These on-o Markov Reward Models are analyzed and a symbolic solution is ...

Asymptotic Behavior of Multivariate Reward Processes with Nonlinear Reward Functions

A Minimal-Cost Inherent-Feedback Approach for Low-Power MRF-Based Logic Gates

The Markov random field (MRF) theory has been accepted as a highly effective framework for designing noise-tolerant nanometer digital VLSI circuits. In MRF-based design, proper feedback lines are used to control noise and keep the circuits in their valid states. However, this methodology has encountered two major problems that have limited the application of highly noise immune MRF-based circui...

Reward Processes and Performance Simulation in Supermarket Models with Different Servers

Supermarket models with different servers become a key in modeling resource management of stochastic networks, such as, computer networks, manufacturing systems and transportation networks. While these different servers always make analysis of such a supermarket model more interesting, difficult and challenging. This paper provides a new novel method for analyzing the supermarket model with dif...

Composite Performance and Dependability Analysis

Trivedi, K.S., J.M. Muppala, S.P. Woolet and B.R. Haverkort, Composite performance and dependability analysis, Performance Evaluation 14 (1992) 197-215. Composite performance and dependability analysis is gaining importance in the design of complex, fault-tolerant systems. Markov reward models are most commonly used for this purpose. In this paper, an introduction to Markov reward models includ...