Approximating stationary measures of structured continuous-time Markov models using matrix diagrams

Authors:
Gianfranco Ciardo;Andrew S. Miner;Min Wan;Andy Jinqing Yu
Affiliations:
University of California at Riverside;Iowa State University;University of California at Riverside;University of California at Riverside
Venue:
ACM SIGMETRICS Performance Evaluation Review
Year:
2007

Citing 6
Cited 1

Efficient descriptor-vector multiplications in stochastic automata networks

Journal of the ACM (JACM)
Using the exact state space of a Markov model to compute approximate stationary measures

Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Complexity of Memory-Efficient Kronecker Operations with Applications to the Solution of Markov Models

INFORMS Journal on Computing
A Data Structure for the Efficient Kronecker Solution of GSPNs

PNPM '99 Proceedings of the The 8th International Workshop on Petri Nets and Performance Models
Efficient Solution of GSPNs Using Canonical Matrix Diagrams

PNPM '01 Proceedings of the 9th international Workshop on Petri Nets and Performance Models (PNPM'01)
Logic and stochastic modeling with SMART

Performance Evaluation - Modelling techniques and tools for computer performance evaluation

Advanced features in SMART: the stochastic model checking analyzer for reliability and timing

ACM SIGMETRICS Performance Evaluation Review

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the stationary solution of large ergodic continuous-time Markov chains (CTMCs) with a finite state space S, i.e., the computation of π as solution of π · Q = 0 subject to ∑iεsπ[i] = 1, where Q coincides with transition rate matrix R except in its diagonal elements, Q[i, i] = - ∑jεs R [i, j].