An Efficient Algorithm Based on Weak Synchronization for Distributed in Virtuo Biological Experiments.

Virtual Reality is becoming increasingly necessary to study complex systems such as biological systems. Thanks to Virtual Reality, the user is placed at the heart of biological simulations and can carry out experiments as if he were under the same experimental conditions as in vivo or in vitro. We usually call this kind of experiments in virtuo experiments.In order to rapidly develop Virtual Reality applications related to biology, we have already proposed the ReISCOP meta model which makes it possible to easily design biological simulations and undertake in virtuo experiments. This meta model allows to describe a biological system as a composition of its sub-systems and the interactions between the constituents of these sub-systems.Unfortunately, when using a single computer, the number of simulated entities is far from what is needed in biological simulations. It seemed thus necessary to extend the ReISCOP meta model so that it allows distributed computing on a grid. We made this choice because the structure of the ReISCOP meta model is well adapted to a distribution on a grid where the sub-systems which compose a system can be dispatched on different nodes, the synchronization and the coherence of the system being ensured by a Peer-to-Peer architecture. Unlike traditional approaches which propose a spatial distribution, the method we describe in this paper is based on an "organizational" distribution linked to the ReISCOP meta model. This "organizational" distribution is mainly ensured by using two efficient algorithms based on a dead reckoning method, one for a data consistency between nodes and one for a weak synchronization of the nodes involved. These two algorithms are integrated into the behaviors of agents DIVAs which are located on each node of the grid. These agents are able to communicate by using a Peer-to-Peer architecture upon the grid. In order to validate our approach, we implement three distributed simulations with increasing complexities and we compare the results with the results obtained in the non-distributed simulations. We get very similar results for the distributed and the non-distributed simulations.