  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.
