jEQN a Java-Based Language for the Distributed Simulation of Queueing Networks

A software architecture to ease the development of distributed simulation systems

“…For this category jEQN defines a framework and a taxonomy within which each policy must be classified by the use and by the data type of the following four parameter types: 18,20 I, type of implicit input (i.e. the input taken at policy instantiation time); S, type of policy state (i.e.…”

“…SimArch obtains this by defining a software architecture that organizes simulation systems into a set of layers, each having defined scope, data, and service interfaces. [17][18][19][20] Figure 2 illustrates the SimArch layered architecture, which consists of the following five layers: distributed computing infrastructure (Layer 0), distributed discrete event simulation (DES) services (Layer 1), DES services (Layer 2), simulation components (Layer 3), and simulation model (Layer 4).…”

Model-driven performance prediction of HLA-based distributed simulation systems

“…Figure 1 illustrates the proposed method, which consists of the following steps: 1) system model partitioning: a manual step that partitions the system model to identify which SysML blocks are to be transformed into simulation components of the DS model ; 2) DS model building: a manual, but potentially automated, step that takes as input the partitioned system model and derives the DS model. This model is specified as a UML model consisting of a Component Diagram, a Deployment Diagram and a set of Activity Diagrams, one for each federate of the DS system; 3) performance model building: an automated step that takes as input the DS model and yields as output the performance model, specified according to the metamodel introduced in , specifically for what concerns the EQN topology; 4) performance model parameterization: an automated step that takes as input the DS model and yields as output the parameters of the EQN-based performance model obtained at step 3; 5) performance model implementation: an automated step that transforms the EQN-based performance model into the corresponding implementation, by use of EQN implementation technologies -e.g., jEQN (D'Ambrogio et al 2006;Gianni et. al 2008) or OMNET++ (www.omnetpp.org/); 6) performance model evaluation: an automated step that executes the model implementation obtained at step 5 to yield the indices for the prediction of the DS system performance, in terms of execution time.…”

“…Predictive performance engineering methodologies have been introduced to estimate the execution time of a DS system, thus supporting the evaluation of design alternatives and minimizing the risk that the DS system will not meet the expected performance (Chu-Cheow et al 1999;Perumalla et al 2005; Ewald 978-1-4673-4781-5/12/$31.00 ©2012 IEEE Gianni, Bocciarelli, and D'Ambrogio et al 2006;Gianni et al 2010). However, barriers to the wide adoption of these methodologies still exist.…”

An Architecture of Effective Discrete-Event Simulation Engine for Early Validation of Avionics Systems

“…There are some instruments that are using seldom used in avionics area functional languages (for example, Scala and Haskell used by Facsimile [7] and Aivika [8], [9]) or specific custom languages (for example, jEQN [10] for SimArch [11]). …”