Cloud Computing researches involve a tremendous amount of entities such as users, applications, and virtual machines. Due to the limited access and often variable availability of such resources, researchers have their prototypes tested against the simulation environments, opposed to the real cloud environments. Existing cloud simulation environments such as CloudSim and EmuSim are executed sequentially, where a more advanced cloud simulation tool could be created extending them, leveraging the latest technologies as well as the availability of multi-core computers and the clusters in the research laboratories. While computing has been evolving with multi-core programming, MapReduce paradigms, and middleware platforms, cloud and MapReduce simulations still fail to exploit these developments themselves. This research develops Cloud2Sim, which tries to fill the gap between the simulations and the actual technology that they are trying to simulate. First, Cloud2Sim provides a concurrent and distributed cloud simulator, by extending CloudSim cloud simulator, using Hazelcast in-memory key-value store. Then, it also provides a quick assessment to MapReduce implementations of Hazelcast and Infinispan, adaptively distributing the execution to a cluster, providing means of simulating MapReduce executions. The dynamic scaler solution scales out the cloud and MapReduce simulations to multiple nodes running Hazelcast and Infinispan, based on load. The distributed execution model and adaptive scaling solution could be leveraged as a general purpose auto scaler middleware for a multi-tenanted deployment.