Stochastic process algebras and stochastic Petri nets

Ruan Jian Xue Bao/Journal of Software

Volumn 13, Issue 2, 2002, Pages 203-213

  Lin, Chuang ^a   Wei, Ya Ya ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Composition; Equivalence; Lumpability; Stochastic Petri nets; Stochastic process algebras

Indexed keywords

ALGEBRA; COMPOSITION; COMPUTER SIMULATION; EQUIVALENCE CLASSES; MATHEMATICAL MODELS; PETRI NETS; PROBABILITY; SEMANTICS;

LUMPABILITY; STOCHASTIC PETRI NETS; STOCHASTIC PROCESS ALGEBRA;

RANDOM PROCESSES;

EID: 0036485966     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (53)

1. Converging towards a timed LOTOS standard
2. An introduction to ET-LOTOS for the description of time sensitive systems
3. An operational semantics for timed CSP
4. A temporal calculus of communication systems
5. Real-time behaviour of asynchronous agents
6. Formal description, time and performance analysis: A framework
7. The PEPAWorkbench: A tool to support a process algebra-based approach to performance modeling
8. Stochastic process algebras-between LOTOS and Markov chains
9. Exploiting symmetries in stochastic process algebras
10. A simple time scale decomposition technique for SPAs
11. Stochastic process algebra -- A formal approach to performance modeling
12. Stochastic process algebras as a tool for performance and dependability modeling
13. A compositional approach to performance modeling
14. A tutorial on EMPA: A theory of concurrent processes with nondeterminism, priorities, probabilities and time
15. Compositional performance modeling with the TIPPTOOL
16. A construction and analysis tool based on the stochastic process algebra TIPP
17. Experiences with the PEPA performance modeling tools
18. TWOTOWERS: A tool integrating functional and performance analysis of concurrent systems
19. Priority and maximal progress are completely aximatisable
20. Stochastic process algebras: Integrating qualitative and quantitative modeling
21. Stochastic process algebra -- Foundations and relations with SPN
22. Stochastic process algebras: A new approach to performance modeling
23. Bisimulation through probabilistic testing
24. Compositional Markovian modeling using a process algebra
25. Compositional minimal semantics for the stochastic process algebra: TIPP
26. Tip and the spectral expansion method
27. Quantitative modeling in parallel systems, chapter TIPP and the spectral expansion method
28. Exploiting quasi-reversible structures in Markovian process algebra models models
29. Towards a product form solution for stochastic process algebra
30. Product form solution for class of PEPA models
31. A spectral expansion method for random walks on semi-infinite strips
32. Towards a product form solution for stochastic process algebras
33. Exploiting structure in solution: Decomposing composed models
34. Embedded processes in stochastic Petri nets
35. Approximate analysis methods for stochastic process algebras
36. Throughput approximation of decision free processes using decomposition
37. Experience with the PEPA performance modeling tools
38. A comparison of performance evaluation process algebra and generalized stochastic Petri nets
39. Towards a theory of GSPA
40. GSPN and SPA compared in practice
41. Stochastic Petri net semantics for process algebras
42. Giving a net semantics to Markovian process
43. A notion of equivalence for stochastic Petri nets
44. Markovian process algebra: Composition and equivalence
45. Syntax, semantics, equivalence, and axioms for MTIPP
46. Stochastiche prozebalgebren mit zeitlosen akivitaten und probabilistischen verzweigungen
47. Exploiting stochastic process algebra achievements for generalized stochastic Petri nets.
48. Stochastic high-level Petri nets and applications
49. On the aggregation techniques in stochastic Petri nets and stochastic process algebras
50. Lumpability and nearly-lumpability in hierarchical queueing networks
51. Stochastic process algebras: Linking process descriptions with performance
52. Markovian analysis of large finite-state machines