Does the dwarf galaxy system of the Milky Way originate from Andromeda?

The Local Group is often seen to be a quiescent environment without significant merger events. However an ancient major merger may have occurred in the most massive galaxy. Numerical simulations have shown that tidal tails formed during gas-rich major mergers are long-lived and could be responsible for old stellar streams and likely induce the formation of tidal dwarf galaxies (TDGs). Using several hydrodynamical simulations we have investigated the most prominent tidal tail formed during the first passage, which is gas-rich and contains old and metal poor stars. We discovered several striking coincidences after comparing its location and motion to those of the Milky Way (MW) and of the Magellanic Clouds (MCs). First, the tidal tail is sweeping a relatively small volume in which the MW precisely lies. Because the geometry of the merger is somehow fixed by the anisotropic properties of the Giant Stream (GS), we evaluate the chance of the MW to be at such a rendez-vous with this gigantic tidal tail to be 5 %. Second, the velocity of the tidal tail matches the LMC proper motion, and reproduce quite well the geometrical and angular momentum properties of the MW dwarfs, i.e. the so-called disk of satellites, better called Vast Polar Structure (VPOS). Third, the simulation of the tidal tail reveals one of the formed TDG with mass and location almost comparable to those of the LMC. Our present modeling is however too limited to study the detailed interaction of gas-rich TDGs with the potential of the MW, and a complementary study is required to test whether the dwarf intrinsic properties can be accounted for by our scenario. Nevertheless this study suggests a causal link between an ancient, gas-rich major merger at the M31 location, and several enigma in the Local Group, the GS, the VPOS, and the presence of the MCs.Comment: 17 pages accepted MNRA

Weaving Rules into [email protected] for Embedded Smart Systems

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware.Comment: pre-print version, published in the proceedings of MOMO-17 Worksho

Could M31 be the result of a major merger?

We investigated a scenario in which M31 could be the remnant of a gas-rich major merger. Galaxy merger simulations, highly constrained by observations, were run using GADGET 2 in order to reproduce M31. We succeeded in reproducing the global shape of M31, the thin disk and the bulge, and in addition some of the main M31 large-scale features, such as the thick disk, the 10kpc ring and the Giant Stream. This lead to a new explanation of the Giant Stream which could be caused by tidal tail stars that have been captured by the galaxy potential.Comment: Proceedings for the conference "Assembling the puzzle of the Milky Way", 2 page

The vast thin plane of M31 co-rotating dwarfs: an additional fossil signature of the M31 merger and of its considerable impact in the whole Local Group

The recent discovery by Ibata et al. (2013) of a vast thin disk of satellites (VTDS) around M31 offers a new challenge for the understanding of the Local Group properties. This comes in addition to the unexpected proximity of the Magellanic Clouds (MCs) to the Milky Way (MW), and to another vast polar structure (VPOS), which is almost perpendicular to our Galaxy disk. We find that the VTDS plane is coinciding with several stellar, tidally-induced streams in the outskirts of M31, and, that its velocity distribution is consistent with that of the Giant Stream (GS). This is suggestive of a common physical mechanism, likely linked to merger tidal interactions, knowing that a similar argument may apply to the VPOS at the MW location. Furthermore, the VTDS is pointing towards the MW, being almost perpendicular to the MW disk, as the VPOS is. We compare these properties to the modelling of M31 as an ancient, gas-rich major merger, which has been successfully used to predict the M31 substructures and the GS origin. We find that without fine tuning, the induced tidal tails are lying in the VTDS plane, providing a single and common origin for many stellar streams and for the vast stellar structures surrounding both the MW and M31. The model also reproduces quite accurately positions and velocities of the VTDS dSphs. Our conjecture leads to a novel interpretation of the Local Group past history, as a gigantic tidal tail due to the M31 ancient merger is expected to send material towards the MW, including the MCs. Such a link between M31 and the MW is expected to be quite exceptional, though it may be in qualitative agreement with the reported rareness of MW-MCs systems in nearby galaxies.Comment: Accepted for publication in MNRAS, 8 pages, 3 figure

A Process for Continuous Validation of Self-Adapting Component Based Systems

International audienceIn this paper we propose an approach to integrate the use of time-related stochastic properties in a continuous design process based on models at runtime. Time-related specifica-tion of services are an important aspect of component-based architectures, for instance in distributed, volatile networks of computation nodes. The models at runtime approach eases the management of such architectures by maintaining abstract models of architectures synchronized with the physical, distributed execution platform. For self-adapting systems, prediction of delays and throughput of a component assembly is of utmost importance to take adaptation decision and accept evolutions that conform to time specifications. To this aim we define a metamodel extension based on stochastic Petri nets as an internal time model for prediction. We design a library of patterns to ease the specification and prediction of common time properties of models at runtime and make the synchronization of behaviors and structural changes easier. Our prediction engine is fast enough to perform prediction at runtime in a realistic setting and validate models at runtime

The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling

Machine learning algorithms are designed to resolve unknown behaviors by extracting commonalities over massive datasets. Unfortunately, learning such global behaviors can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems andInternet of Things applications. Instead, to make smart deci-sions, such systems have to continuously refine the behavior on a per-element basis and compose these small learning units together. However, combining and composing learned behaviors from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviors and domain knowledge together in a flexible way. In this paper we propose to weave machine learning into domain modeling. More specifically, we suggest to decompose machine learning into reusable, chainable, and independently computable small learning units, which we refer to as microlearning units.These micro learning units are modeled together with and at the same level as the domain data. We show, based on asmart grid case study, that our approach can be significantly more accurate than learning a global behavior, while the performance is fast enough to be used for live learning

Designing resource-aware distriubted system based on system level containers

International audienceResource management is critical for application domains where components share their execution environments but belong to dif-ferent stakeholders, such as smart homes or cloud systems. Yet, current middleware and application containers often hide system-level details needed for dynamic resource management. In particular, they tend to hide resource usage by offering automatic management of these resources (e.g., CPU, memory and I/O). In contrast, system-level containers, such as Linux Containers (LXC), allow fine-grain resource management. How-ever, they lack knowledge about the application's structure and its re-quirements in order to provide fine tuned resource management. In this tutorial, we will expose Squirrel: a new middleware which aims at combining the benefits from the component based software engineering to design flexible and modular application and the system level contain-ers to manage resources. Squirrel follows an approach where develop-ers specifies contracts on components and connections to describe the expected behavior of their application regarding resource consumption. These high level contracts are then used to automatically configure the system level containers which will hosts the running applications. At the end of this tutorial, applicants will be able to design applications and contracts using Squirrel and run their application inside system level containers to ensure a correct behavior of their application regarding resource consumption

Kevoree (Model@Runtime pour le développement continu de systèmes adaptatifs distribués hétérogènes)

La complexité croissante des systèmes d'information modernes a motivé l'apparition de nouveaux paradigmes (objets, composants, services, etc), permettant de mieux appréhender et maîtriser la masse critique de leurs fonctionnalités. Ces systèmes sont construits de façon modulaire et adaptable afin de minimiser les temps d'arrêts dus aux évolutions ou à la maintenance de ceux-ci. Afin de garantir des propriétés non fonctionnelles (par ex. maintien du temps de réponse malgré un nombre croissant de requêtes), ces systèmes sont également amenés à être distribués sur différentes ressources de calcul (grilles). Outre l'apport en puissance de calcul, la distribution peut également intervenir pour distribuer une tâche sur des nœuds aux propriétés spécifiques. C'est le cas dans le cas des terminaux mobiles proches des utilisateurs ou encore des objets et capteurs connectés proches physiquement du contexte de mesure. L'adaptation d'un système et de ses ressources nécessite cependant une connaissance de son état courant afin d'adapter son architecture et sa topologie aux nouveaux besoins. Un nouvel état doit ensuite être propagé à l'ensemble des nœuds de calcul. Le maintien de la cohérence et le partage de cet état est rendu particulièrement difficile à cause des connexions sporadiques inhérentes à la distribution, pouvant amener des sous-systèmes à diverger. En réponse à ces défi scientifiques, cette thèse propose une abstraction de conception et de déploiement pour systèmes distribués dynamiquement adaptables, grâce au principe du Model@Runtime. Cette approche propose la construction d'une couche de réflexion distribuée qui permet la manipulation abstraite de systèmes répartis sur des nœuds hétérogènes. En outre, cette contribution introduit dans la modélisation des systèmes adaptables la notion de cohérence variable, permettant ainsi de capturer la divergence des nœuds de calcul dans leur propre conception. Cette couche de réflexion, désormais cohérente "à terme", permet d'envisager la construction de systèmes adaptatifs hétérogènes, regroupant des nœuds mobiles et embarqués dont la connectivité peut être intermittente. Cette contribution a été concrétisée par un projet nommé ''Kevoree'' dont la validation démontre l'applicabilité de l'approche proposée pour des cas d'usages aussi hétérogènes qu'un réseau de capteurs ou une flotte de terminaux mobiles.The growing complexity of modern IT systems has motivated the development of new paradigms (objects, components, services,...) to better cope with the critical size of their functionalities. Such systems are then built as a modular and dynamically adaptable compositions, allowing them to minimise their down-times while performing evolutions or fixes. In order to ensure non-functional properties (i.e. request latency) such systems are distributed across different computation nodes. Besides the added value in term of computational power (cloud), this distribution can also target nodes with dedicated properties such as mobile nodes and sensors (internet of things), physically close to users for interactions. Adapting a system requires knowledge about its current state in order to adapt its architecture to its evolving needs. A new state must be then disseminated to other nodes to synchronise them. Maintaining its consistency and sharing this state is a difficult task especially in case of sporadic connexions which lead to divergent state between sub-systems. To tackle these scientific problems, this thesis proposes an abstraction to design and deploy distributed adaptive systems following the Model@Runtime paradigm. From this abstraction, the proposed approach allows defining a distributed reflexive layer to manipulate heterogeneous distributed nodes. In particular, this contribution introduces variable consistencies in model definition and divergence in system conception. This reflexive layer, eventually consistent allows the construction of distributed adapted systems even on mobile nodes with intermittent connectivity. This work has been realized in an open source project named Kevoree, and validated on various distributed systems ranging from sensor networks to cloud computing.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF