Camille Coti

Dina Faneva Andriantsiory, Camille Coti, Joseph Ben Geloun et al.

Machine Learning approaches like clustering methods deal with massive datasets that present an increasing challenge. We devise parallel algorithms to compute the Multi-Slice Clustering (MSC) for 3rd-order tensors. The MSC method is based on spectral analysis of the tensor slices and works independently on each tensor mode. Such features fit well in the parallel paradigm via a distributed memory system. We show that our parallel scheme outperforms sequential computing and allows for the scalability of the MSC method.

Camille Coti

This paper presents a fully distributed resource discovery and reservation system. Verification of such a system is important to ensure the execution of distributed applications on a set of resources in appropriate conditions. A semi-formal model for his system is presented using probabilistic timed automata. This model is timed, parametric and probabilistic, making it a challenge to the parameter synthesis community.