Abstract
Simulation models are elements of science that use software tools to solve complex mathematical problems. They are beneficial in areas such as performance engineering and communications systems. Nevertheless, to achieve more accurate results, researchers should use more detailed models. Having an analysis of the system operations in the early modeling phases could help one make better decisions relating to the solution. In this paper, we introduce the use of the QPME tool, based on queueing Petri nets, to model the system stream generator. This formalism was not considered during the first tool development. As a result of the analysis, an alternative design model is proposed. By comparing the behavior of the proposed generator against the one already developed, a better adjustment of the stream to the customer’s needs was obtained. The study results show that appropriately adjusting queueing Petri net models can help produce better streams of data (tokens).
Highlights
Related WorkPerformance evaluation is applied in communications system development and assessment
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The modeling approach presented in this paper differs from that of previous works [9], in regard to performance analysis, because we modeled different stream behaviors; they are similar to [7], but we prepared the stream generator (SG) based on another formalism
Summary
Performance evaluation is applied in communications system development and assessment. Falko Bause et al in [20] describe these kinds of Petri nets, named QPNs. Kounev et al [21] provide an introduction to QPNs. The modeling approach presented in this paper differs from that of previous works [9], in regard to performance analysis, because we modeled different stream behaviors; they are similar to [7], but we prepared the SG based on another formalism. The modeling approach presented in this paper differs from that of previous works [9], in regard to performance analysis, because we modeled different stream behaviors; they are similar to [7], but we prepared the SG based on another formalism This approach could be treated as an extension of the paper [7]. QPN model was used to check the behavior of the token and it was developed using the QPME tool [22]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
