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The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

The rapid evolution of IT ecosystems significantly challenges the security models our infrastructures rely on. Beyond the old dichotomy between open and closed systems, it is now necessary to handle securely the interaction between heterogeneous devices building dynamic ecosystems. To this regard, bio-inspired approaches provide a rich set of conceptual tools, but they have failed to lay the basis for robust and efficient solutions. Our research effort intends to revisit the contribution of artificial immune system research to bring immune properties: security, resilience, distribution, memory, into IT infrastructures. Artificial immune ecosystems support a comprehensive model for anomaly detection and characterization, but their cognitive capacity are limited by the state of the art in machine learning and the rapid evolution of cybersecurity threats so far. We therefore propose to enrich the cognitive process with expert-based learning for reinforcement, classification and investigation. Application to system supervision using system logs and supervision time series confirms the relevance and performance of this model.

International audience; Monitoring and visualisation tools are currently attracting more and more attention in order to understand how search spaces are explored by complex optimisation ecosystems such as parallel evolutionary algorithms based on island models. Multilevel visualisation is actually a desirable feature for facilitating the monitoring of computationally expensive runs involving several hundreds of computers during hours or even days. In this paper we present two components of a future multilevel monitoring system: MusEAc, a high level, audio monitoring allowing to listen to a run and tune it in real time and GridVis, a lower lever, more precise a posteriori visualisation tool that lets the user understand why the algorithm has performed well or bad.

This work presents our contribution about the distribution of complex system simulations in open distributed environments. We consider these environments as computational ecosystems, of whose we describe properties and characteristics, wherein simulations will evolve, by their execution. An interaction network models these simulations, that we represent as a dynamic graph. Considering the different kinds of possible dynamics, we propose a global formalism that can be used to describe these graphs, along with a software framework, GraphStream, allowing their manipulation and their study.The dynamic graph is then the object of a study wherein we are looking for organisations, following them through time, in order to minimise communications's costs between machines, and to balance the computation load. We bring an improvement aiming to reduce results's oscillations of AntCo2, which is algorithm using digital ants colonies to detect organisations, through competition and collaboration mechanisms. Stability of these organisations is determined by a dynamic and distributed heuristic to find centroid of graph.We propose a middleware allowing a dynamic and decentralized distribution of simulations in a computational ecosystem, by favouring organisations and respecting the load balancing.; Nous présentons dans ces travaux une contribution concernant la distribution de simulations de système complexe dans des environnements distribués ouverts. Nous considérons ces environnements comme des écosystèmes computationnels, dont nous décrivons les propriétés et les caractéristiques, dans lesquels évoluent, de par leur exécution, les simulations. Elles sont modélisées sous la forme d'un réseau d'interactions représenté à l'aide d'un graphe dynamique. En considérant les différentes dynamiques possibles, nous proposons un formalisme général représentant ces graphes, ainsi qu'une interface de programmation, GraphStream, permettant de les manipuler et de les étudier.Le graphe dynamique est alors un sujet d'étude dans lequel nous recherchons des organisations, que nous suivons dans le temps, afin de minimiser les coûts de communication entre les machines et d'équilibrer la charge de calcul. Nous apportons une amélioration visant à réduire les oscillations des résultats de l'algorithme AntCo2 utilisant des colonies de fourmis numériques qui, grâce à des mécanismes de compétition et de collaboration, détecte des organisations. La stabilité de ces dernières est déterminée par l'intermédiaire d'une heuristique de recherche distribuée et dynamique de centroïdes.Un intergiciel est proposé permettant de distribuer de manière décentralisée et dynamique les simulations dans un écosystème computationnel en favorisant les organisations et en respectant l'équilibrage de charge.

This work presents our contribution about the distribution of complex system simulations in open distributed environments. We consider these environments as computational ecosystems, of whose we describe properties and characteristics, wherein simulations will evolve, by their execution. An interaction network models these simulations, that we represent as a dynamic graph. Considering the different kinds of possible dynamics, we propose a global formalism that can be used to describe these graphs, along with a software framework, GraphStream, allowing their manipulation and their study.The dynamic graph is then the object of a study wherein we are looking for organisations, following them through time, in order to minimise communications's costs between machines, and to balance the computation load. We bring an improvement aiming to reduce results's oscillations of AntCo2, which is algorithm using digital ants colonies to detect organisations, through competition and collaboration mechanisms. Stability of these organisations is determined by a dynamic and distributed heuristic to find centroid of graph.We propose a middleware allowing a dynamic and decentralized distribution of simulations in a computational ecosystem, by favouring organisations and respecting the load balancing., Nous présentons dans ces travaux une contribution concernant la distribution de simulations de système complexe dans des environnements distribués ouverts. Nous considérons ces environnements comme des écosystèmes computationnels, dont nous décrivons les propriétés et les caractéristiques, dans lesquels évoluent, de par leur exécution, les simulations. Elles sont modélisées sous la forme d'un réseau d'interactions représenté à l'aide d'un graphe dynamique. En considérant les différentes dynamiques possibles, nous proposons un formalisme général représentant ces graphes, ainsi qu'une interface de programmation, GraphStream, permettant de les manipuler et de les étudier.Le graphe dynamique est alors un sujet d'étude dans lequel nous recherchons des organisations, que nous suivons dans le temps, afin de minimiser les coûts de communication entre les machines et d'équilibrer la charge de calcul. Nous apportons une amélioration visant à réduire les oscillations des résultats de l'algorithme AntCo2 utilisant des colonies de fourmis numériques qui, grâce à des mécanismes de compétition et de collaboration, détecte des organisations. La stabilité de ces dernières est déterminée par l'intermédiaire d'une heuristique de recherche distribuée et dynamique de centroïdes.Un intergiciel est proposé permettant de distribuer de manière décentralisée et dynamique les simulations dans un écosystème computationnel en favorisant les organisations et en respectant l'équilibrage de charge.