We live in a digital era, in a world connected by technology. The incredible capabilities of our mobile phones and computers let us communicate and get data from all over the globe, in the instance of a millisecond. However, technological progress doesn’t stop. We persist in looking for faster connections, innovative applications and platforms, more efficient, scalable, and resilient. Distributed systems are the fundamental basis driving this progress in several scientific and industry fields. Epidemic protocols are crucial to ensure efficient data dissemination on these systems, providing fault tolerance, scalability, and availability. Its relevance grows as networks become more dynamic and distributed, playing a main role in ensuring the reliability and efficient operation of these systems. Progress is not possible without studies and experimental evaluation of proposed algorithms. Although, as they are projected to systems compromising millions of nodes and processes, these studies are almost impossible at this scale, so most rely on simulation. Discrete-event simulation is one of the major experi mental methodologies in several scientific and engineering domains. The used simulator is often seen as a technical detail, and many researchers develop their custom tool. Simulation tools vary in complexity and application, catering to a wide range of industries and research domains. The choice of a specific tool depends on the nature of the simulation, the problem being addressed, and the preferences and expertise of the user. In this dissertation, we present, analyze, and compare a set of selected simulation tools, to choose the one that better fits epidemic protocol simulations in P2P systems. After choosing the most adequate simulation tool, we defined a generic simulation framework for epidemic protocols, and implementations of two different peer sampling services and one dissemination protocol. Leveraging this framework, we perform a extensive evaluation of the different protocols.