Scalable multi agent system middleware for HPC of Big Data Applications

The field of multi agent systems (MAS) presents a multitude of middlewares allowing an ease to create and deploy applications of MAS. These middlewares are designed with programming models that strongly couple the communication framework of the agent and its cognitive pattern. Usually, more the number of agents used is large, more the communication model of the middleware is highly used and so the performance is impacted and perturbed.We present in this article a scalable multi-agent system middleware for High Performance Computing (HPC) of big data applications. Our proposed model is based on the principle of the separation between the learning pattern of the agent, its communication pattern and the data and processing distribution aspect. Our model is built around a set of layers based on APIs each having different implementations allowing the construction of agents, the communication of agents, the learning of agents, the distribution of data, the distribution of treatments, the construction and monitoring of the cluster.