Bounds for quasi-lumpable Markov chains
Performance '93 Proceedings of the 16th IFIP Working Group 7.3 international symposium on Computer performance modeling measurement and evaluation
A compositional approach to performance modelling
A compositional approach to performance modelling
SIAM Journal on Matrix Analysis and Applications
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decomposability, instabilities, and saturation in multiprogramming systems
Communications of the ACM
An Efficient Algorithm for Aggregating PEPA Models
IEEE Transactions on Software Engineering
Computing Bounds for the Performance Indices of Quasi-Lumpable Stochastic Well-Formed Nets
IEEE Transactions on Software Engineering
An Efficient Kronecker Representation for PEPA Models
PAPM-PROBMIV '01 Proceedings of the Joint International Workshop on Process Algebra and Probabilistic Methods, Performance Modeling and Verification
Fluid Flow Approximation of PEPA models
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
Bound-Preserving Composition for Markov Reward Models
QEST '06 Proceedings of the 3rd international conference on the Quantitative Evaluation of Systems
Bounds based on lumpable matrices for partially ordered state space
SMCtools '06 Proceeding from the 2006 workshop on Tools for solving structured Markov chains
PRISM: probabilistic model checking for performance and reliability analysis
ACM SIGMETRICS Performance Evaluation Review
An information-theoretic framework to aggregate a Markov chain
ACC'09 Proceedings of the 2009 conference on American Control Conference
Analysis of non-product form parallel queues using Markovian process algebra
Network performance engineering
Compositional Abstractions for Long-Run Properties of Stochastic Systems
QEST '11 Proceedings of the 2011 Eighth International Conference on Quantitative Evaluation of SysTems
Hi-index | 0.00 |
Approximate Markov chain aggregation involves the construction of a smaller Markov chain that approximates the behaviour of a given chain. We discuss two different approaches to obtain a nearly optimal partition of the state-space, based on different notions of approximate state equivalence. Both approximate aggregation methods require an explicit representation of the transition matrix, a fact that renders them inefficient for large models. The main objective of this work is to investigate the possibility of compositionally applying such an approximate aggregation technique. We make use of the Kronecker representation of PEPA models, in order to aggregate the state-space of components rather than of the entire model.