Performance Evaluation - Special issue: 6th international conference on modelling techniques and tools for computer performance evaluation
GreatSPN 1.7: graphical editor and analyzer for timed and stochastic Petri nets
Performance Evaluation - Special issue: performance modeling tools
IEEE Transactions on Software Engineering - Special issue: best papers of the sixth international workshop on Petri nets and performance models (PNPM'95)
An efficient disk-based tool for solving large Markov models
Performance Evaluation - Special issue on tools for performance evaluation
"On-the-Fly" Solution Techniques for Stochastic Petri Nets and Extensions
IEEE Transactions on Software Engineering
Iterative analysis of Markov regenerative models
Performance Evaluation
Integrating synchronization with priority into a Kronecker representation
Performance Evaluation
Hybrid analysis of SGSPNs with time-dependent transition rates
Performance Evaluation
Bound Computation of Dependability and Performance Measures
IEEE Transactions on Computers
Process algebra for performance evaluation
Theoretical Computer Science
Fluid Stochastic Petri Nets Augmented with Flush-out Arcs: Modelling and Analysis
Discrete Event Dynamic Systems
INFORMS Journal on Computing
PNPM '97 Proceedings of the 6th International Workshop on Petri Nets and Performance Models
Performance Analysis of the CORBA Event Service Using Stochastic Reward Nets
SRDS '00 Proceedings of the 19th IEEE Symposium on Reliable Distributed Systems
Compact Representations of Probability Distributions in the Analysis of Superposed GSPNs
PNPM '01 Proceedings of the 9th international Workshop on Petri Nets and Performance Models (PNPM'01)
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Stochastic modeling formalisms such as stochastic Petri nets, generalized stochastic Petri nets, and stochastic reward nets can be used to model and evaluate the dynamic behavior of realistic computer systems. Once we translate the stochastic system model to the underlying corresponding Markov Chain (MC), the developed MC grows wildly to several hundred thousands states. This problem is known as the largeness problem. To tolerate the largeness problem of Markov models, several iterative and direct methods have been proposed in the literature. Although the iterative methods provide a feasible solution for most realistic systems, a major problem appears when these methods fail to reach a solution. Unfortunately, the direct method represents an undesirable numerical technique for tolerating large matrices due to the fill-in problem. In order to solve such problem, in this paper, we develop a disk-based segmentation (DBS) technique based on modifying the Gauss Elimination (GE) technique. The proposed technique has the capability of solving the consequences of the fill-in problem without making, assumptions about the underlying structure of the Markov processes of the developed model. The DBS technique splits the matrix into a number of vertical segments and uses the hard disk to store these segments. Using the DBS technique, we can greatly reduce the memory required as compared to that of the GE technique. To minimize the increase in the solution time due to the disk accessing processes, the DBS utilizes a clever management technique for such processes. The effectiveness of the DBS technique has been demonstrated by applying it to a realistic model for the Kanban manufacturing system.