Stochastic ordering for Markov processes on partially ordered spaces
Mathematics of Operations Research
Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
An Algorithmic Approach to Stochastic Bounds
Performance Evaluation of Complex Systems: Techniques and Tools, Performance 2002, Tutorial Lectures
NECESSARY AND SUFFICIENT CONDITIONS FOR THE STOCHASTIC COMPARISON OF JACKSON NETWORKS
Probability in the Engineering and Informational Sciences
Performance measure bounds in mobile networks by state space reduction
MASCOTS '05 Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Aggregated bounding Markov processes applied to the analysis of tandem queues
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
Weak Stochastic Comparisons for Performability Verification
ASMTA '09 Proceedings of the 16th International Conference on Analytical and Stochastic Modeling Techniques and Applications
Strong and Weak orderings for an accurate resource dimensioning
VECoS'11 Proceedings of the Fifth international conference on Verification and Evaluation of Computer and Communication Systems
Weak stochastic ordering for multidimensional Markov chains
Operations Research Letters
| Hi-index | 0.00 |
We study queueing networks similar to Jackson networks, modelled by a multidimensional Markov chain. The performance analysis may be very difficult or intractable, if there is no specific solution form. We explain how stochastic comparisons of Markov chains can be used to overcome this problem. We build new queueing which are easier to analyse and providing stochastic bounds upper or lower for the original model. In this paper, we propose different queueing systems in the sense of the strong and weak stochastic ordering for a general queueing network model in order to compute performance measure bounds as blocking probabilities. We discuss the accuracy of the bounds under different input parameter values.