Hyperexponential-based network traffic model for distributed manufacturing

Abstract

Agile electronics manufacturing requires integrated design, supply and manufacturing planning for modular products where suppliers and manufacturing resources is network distributed. This research is concerned with the modeling of such a distributed manufacturing network. The performance of network-based distributed decision systems is dominated by the network configuration and related access delays. Traffic measurement studies observed heavy-tails in the network traffic. Our basic approach is based on the quantile match to fit hyperexponential distributions to heavy-tailed distribution. We adopt the ON/OFF network traffic model and compare the fitted hyperexponential model with the heavy-tailed model. The results validate our fitting hyperexponentials. Generalized stochastic Petri nets (GSPNs) are introduced to analyze both the steady state and the transient behaviors of the distributed system. The results indicate that the transient analysis is more important than the steady-state analysis in network traffic modeling, since in most cases the distributed network does not reach the steady state.