ACM Transactions on Computer Systems (TOCS)
Linear algebraic calculation of deadlocks and traps
Concurrency and nets: advances in Petri nets
Time Scale Decomposition of a Class of Generalized Stochastic Petri Net Models
IEEE Transactions on Software Engineering
A comparative study of different techniques for semi-flows computation in place/transition nets
Advances in Petri nets 1989
APN 90 Proceedings on Advances in Petri nets 1990
Solution techniques for stochastic Petri nets
Solution techniques for stochastic Petri nets
Analyzing queueing networks with simultaneous resource possession
Communications of the ACM
Generalized Stochastic Petri Nets: A Definition at the Net Level and its Implications
IEEE Transactions on Software Engineering
Analysing Nets by the Invariant Method
Proceedings of an Advanced Course on Petri Nets: Central Models and Their Properties, Advances in Petri Nets 1986-Part I
Iterative Decomposition and Aggregation of Stochastic Marked Graph Petri Nets
Papers from the 12th International Conference on Applications and Theory of Petri Nets: Advances in Petri Nets 1993
Proceedings of the 14th International Conference on Application and Theory of Petri Nets
Well-Defined Generalized Stochastic Petri Nets: A Net-Level Method to Specify Priorities
IEEE Transactions on Software Engineering
Improve the efficiency of traditional scheduling system with GA-Petri net model
International Journal of Computer Applications in Technology
Hi-index | 14.98 |
Complete decomposition is a new strategy for evaluating the performance of a network of generalized service centers, represented in the notation of Generalized Stochastic Petri Nets (GSPNs). Each service center can have arbitrary internal structure (including internal parallelism), but it must conserve tokens at the boundaries, and its inputs must be i/o-connected to its outputs. Routing between centers can depend on the state of the departure center. The new method adapts a delay equivalence decomposition technique used previously. Within this framework, it reduces the solution complexity of the auxiliary models which must be solved in the iteration. This new method is applied to a scalable model for computer system performance.