Performance modeling of dynamic network-based decision systems

Dynamic network-based decision systems search the information in the distributed databases and provide an appropriate solution for the design-supplier-manufacturing planning problem using evolutionary algorithms. The paper focuses on the development of a performance model to support such enterprise-level decision-making in network based scalable systems. Generalized Stochastic Petri Nets (GSPNs) are introduced to characterize network traffic and evolutionary algorithms. The network traffic model is based on the hyperexponential transition for analytical tractability. The algorithm model transforms the execution of the program into a stochastic activity net. The performance evaluation of the system can be explored in two directions: first, analyze and reconfigure the network connection for a specific algorithm, and second, given the network configuration, predict the performance of the algorithm. The results show that transient analysis is more important than steady-state analysis in the heavy-tailed network traffic. The paper also compares performance of the algorithms under different network configurations.