Phrase based browsing for simulation traces of network protocols

Most discrete event simulation frameworks are able to out-put simulation runs as a trace. The Network Simulator 2 (NS2) is a prominent example that does so to decouple generation of dynamic behavior from its evaluation. If a modeler is interested in the specific details and confronted with lengthy traces from simulation runs, support is needed to identify relevant pieces of information. In this paper, we present a new phrase-based browser that has its roots in information retrieval, language acquisition and text com-pression which is refined to work with trace data derived from simulation models. The browser is a new navigation feature of Traviando, a trace visualizer and analyzer for sim-ulation traces. The browsing technique allows a modeler to investigate particular patterns seen in a trace, that may be of interest due to their frequent or rare occurrence. We demonstrate how this approach applies to traces generated with NS2.

A flexible architecture for modeling and simulation of diffusional association

Up to now, it is not possible to obtain analytical solutions for complex molecular association processes (e.g. Molecule recognition in Signaling or catalysis). Instead Brownian Dynamics (BD) simulations are commonly used to estimate the rate of diffusional association, e.g. to be later used in mesoscopic simulations. Meanwhile a portfolio of diffusional association (DA) methods have been developed that exploit BD. However, DA methods do not clearly distinguish between modeling, simulation, and experiment settings. This hampers to classify and compare the existing methods with respect to, for instance model assumptions, simulation approximations or specific optimization strategies for steering the computation of trajectories. To address this deficiency we propose FADA (Flexible Architecture for Diffusional Association) - an architecture that allows the flexible definition of the experiment comprising a formal description of the model in SpacePi, different simulators, as well as validation and analysis methods. Based on the NAM (Northrup-Allison-McCammon) method, which forms the basis of many existing DA methods, we illustrate the structure and functioning of FADA. A discussion of future validation experiments illuminates how the FADA can be exploited in order to estimate reaction rates and how validation techniques may be applied to validate additional features of the model