DocumentCode
3373951
Title
Assessing the performance of multiprocessor architectures through SWN models simulation: a case study in the field of plant automation systems
Author
Botti, Oliver ; Donatelli, Susanna ; Franceschinis, Giuliana
Author_Institution
ENEL, Milan, Italy
fYear
1996
fDate
8-11 Apr 1996
Firstpage
118
Lastpage
127
Abstract
Presents a simulation study of a hypercube architecture of complex nodes. The goal of the simulation experiments is to compare two possible architectural solutions for the complex nodes, and to study the hypercube behaviour under different types of workload. The models have been expressed in the formalism of stochastic well-formed nets (SWNs), which is a class of high-level stochastic Petri nets, and the results have been obtained using a general-purpose simulator for SWN models. In this study, the availability of a general-purpose simulator and the flexibility and parametricity of the SWN formalism has indeed been a crucial point in what we think is a successful example of “benchmarking by models”: a characterization of an architecture in terms of models, rather than by measurement
Keywords
Petri nets; factory automation; hypercube networks; industrial plants; parallel architectures; performance evaluation; stochastic systems; virtual machines; availability; benchmarking by models; case study; complex nodes; flexibility; general-purpose simulator; high-level stochastic Petri nets; hypercube architecture simulation; multiprocessor architecture performance assessment; parametricity; plant automation systems; stochastic well-formed nets; workload; Automation; Computer aided software engineering; Control systems; Delta modulation; Hardware; Hypercubes; Parallel architectures; Petri nets; Predictive models; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Simulation Symposium, 1996., Proceedings of the 29th Annual
Conference_Location
New Orleans, LA
ISSN
1080-241X
Print_ISBN
0-8186-7432-6
Type
conf
DOI
10.1109/SIMSYM.1996.492159
Filename
492159
Link To Document