Fog and Edge Computing are two computing paradigms that aim to solve the Cloud limitations by bringing its applications close to IoT devices at the edge of the network. As a result, they decrease both the latency and the Cloud workload. However, despite the research advancement in this field, there is still a lack of simulation tools, especially in the case of Pure Edge Computing where heterogeneous and constrained devices cooperate and share their resources in a peer to peer manner [9]. In this paper, we present PureEdgeSim, a simulation toolkit that enables the simulation of resource management strategies and the performance evaluation of Cloud, Fog, and Pure Edge Computing environments in terms of delays, energy consumption, network congestion, resource utilization, and tasks success rate as well. We also propose a load balancing algorithm that is based on Fuzzy Decision Tree. It leverages from reinforcement learning that allows it to adapt to the IoT environmental changes and enables the IoT self-capabilities [15]. Finally, we describe a case study that simulates an IoT environment in order to demonstrate PureEdgeSim capabilities and to evaluate the performance of those computing paradigms and the impact of the adopted load balancing algorithm. The simulation results show the effectiveness of PureEdgeSim in simulating Cloud, Fog, and Edge Computing environments. The results also highlight the advantages of adopting the Pure Edge Computing in terms of scalability and delays as compared to the commonly used architectures that use the Fog and the Cloud. On the other hand, the proposed algorithm has reduced the tasks completion delay by nearly 25.4%, the energy consumption by 57.2%, and the failure rate by 39%, as compared to the state of the art algorithm.