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 of algorithms. The network traffic model is based on the 1 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.