We introduce CD ++ Builder, an open-source environment that aims at providing easy-to-use graphical modeling tools to simplify the construction of models and the execution of simulations of complex Discrete Event System Specification (DEVS) models. The architecture and implementation of CD ++ Builder focuses on providing simple definition and reuse of components, offering easy extensibility to support new features. CD ++ Builder includes graphical editors for DEVScoupled models, DEVS-Graphs and C ++ atomic models; it provides code templates that are synchronized with their graphical versions, and it greatly simplifies the software installation and update procedures. We show how this environment can be used to build and simulate DEVS models, and we compare the process with previous versions and other simulation tools, showing that CD ++ Builder can improve model development by creating DEVS models in a completely assisted manner, including advanced graphical interfaces.

R obust engineering methodologies offering product lifecycle control have proved to be a cornerstone in modern software development projects. Simultaneously, various modeling and simulation (M&S) techniques have become increasingly adopted in complex system design, particularly in scenarios in which it's difficult to predict system behavior as changes are introduced.The DEVS (Discrete Event Systems Specification) framework is the most general formalism for modeling discrete event systems 1-3 and has been adopted in several disciplines for complex software and hardware system design and analysis. 4,5 In addition to providing an unambiguous mathematical formalism to define model behavior and structure, DEVS provides a clear framework for system analysis, experimental frame definition, model-to-simulator verification, and model-to-system validation.We present a DEVS-based methodology for M&S-driven engineering projects that integrates software development best practices tailored to a large-scale networked data acquisition system in a physics experiment (specifically, the ATLAS particle detector 6 at CERN 7 ). This project poses M&S challenges from several viewpoints, including system complexity, tight delivery times, the quality and flexibility of the developed models and tools, interdisciplinary communication of results to collaborators (mostly scientists), and big data-scale analysis.

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Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This allows to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector at CERN. 1 INTRODUCTION Operational computer networks are subjected to frequent reconfigurations in an effort to maintain their quality of service under uncertain conditions (produced by hardware, software or human failures). Meanwhile, the rapidly emerging Software Defined Networks (SDNs) technology offers an unprecedented capability to automatically and programmatically reconfigure large network topologies without the intervention of human operators. This new flexibility comes at the price of error proneness: the point of failure gets now shifted to the software that decides on network reconfiguration actions. The verification and validation of SDN-based design options becomes key to minimize the risk of deploying a faulty system. Network simulation has long been an efficient tool to gain confidence with networks at design time. When network simulation models are built based on real, changing topologies, each modification implies the need for updating the simulation model accordingly. The standard practice is to upgrade topology descriptions manually for a given modeling and simulation tool of choice. Such manual changes can get considerably time-consuming and error-prone, in particular for medium-to large-sized networks.