Cloud simulation environments today are largely employed to model and simulate complex systems for remote accessibility and variable capacity requirements. In this regard, scalability issues in Modeling and Simulation (M\&S) computational requirements can be tackled through the elasticity of on-demand Cloud deployment. However, implementing a high performance cloud M\&S framework following these elastic principles is not a trivial task as parallelizing and distributing existing architectures is challenging. Indeed, both the parallel and distributed M\&S developments have evolved following separate ways. Parallel solutions has always been focused on ad-hoc solutions, while distributed approaches, on the other hand, have led to the definition of standard distributed frameworks like the High Level Architecture (HLA) or influenced the use of distributed technologies like the Message Passing Interface (MPI). Only a few developments have been able to evolve with the current resilience of computing hardware resources deployment, largely focused on the implementation of Simulation as a Service (SaaS), albeit independently of the parallel ad-hoc methods branch. In this paper, we present a unified parallel and distributed M\&S architecture with enough flexibility to deploy parallel and distributed simulations in the Cloud with a low effort, without modifying the underlying model source code, and reaching important speedups against the sequential simulation, especially in the parallel implementation. Our framework is based on the Discrete Event System Specification (DEVS) formalism. The performance of the parallel and distributed framework is tested using the xDEVS M\&S tool, Application Programming Interface (API) and the DEVStone benchmark with up to eight computing nodes, obtaining maximum speedups of $15.95\times$ and $1.84\times$, respectively.

翻译：当前的云仿真环境主要用于对复杂系统进行建模与仿真，以满足远程可访问性和可变容量需求。为此，建模与仿真（M&S）计算需求中的可扩展性问题可通过按需云部署的弹性化得到解决。然而，遵循这些弹性原则实现高性能云M&S框架并非易事，因为并行化与分布式现有架构具有挑战性。事实上，并行与分布式M&S的发展路径各不相同：并行解决方案始终专注于专用定制方案，而分布式方法则促成了诸如高层体系结构（HLA）等标准分布式框架的定义，或推动了消息传递接口（MPI）等分布式技术的应用。仅有少数进展能够适应当前计算硬件资源部署的弹性能力，且主要集中于仿真即服务（SaaS）的实现，但独立于并行专用方法分支。本文提出一种统一的并行与分布式M&S架构，该架构具有足够的灵活性，可在无需修改底层模型源代码的前提下，以较低工作量在云中部署并行与分布式仿真，并相较于顺序仿真实现显著的加速效果，尤其在并行实现中。我们的框架基于离散事件系统规范（DEVS）形式化方法。通过使用xDEVS M&S工具、应用程序编程接口（API）以及包含最多八个计算节点的DEVStone基准测试，并行与分布式框架的性能得到了验证，分别获得了$15.95\times$和$1.84\times$的最大加速比。