Maya: a multi-paradigm network modeling framework for emulating distributed applications

This paper presents Maya, a multi-paradigm, scalable and extensible network modeling framework for emulating distributed applications. A novel three-tier architecture is proposed to integrate three disparate modeling paradigms, namely, discrete event models, analytical models and physical network interfaces into one unified framework of Maya. As the first effort to integrate all three paradigms into one framework, this paper discusses the implementations of Maya using Qualnet, fluid flow based TCP model and physical network interface. It addresses the performance issues involved in attaining the real time constraints imposed by distributed applications and demonstrates the effectiveness of using analytical models in Maya. Furthermore, it identifies the negative impact on real time performance through the computation intensive ordinary differential equation (ODE) solver in the fluid flow model. A new approach to interleaved executions of the fluid flow model is proposed to hide ODE solver turnaround time. As a result, the percentage of packets missing their deadlines has been reduced from more than 6% to less than 0.2%.