François Fouquet

Generating realistic Smart Grid communication topologies based on real-data

Todays electricity grid must undergo substantial changes in order to keep pace with the rising demand for energy. The vision of the smart grid aims to increase the efficiency and reliability of todays electricity grid, e.g. by integrating renewable energies and distributed micro-generations. The backbone of this effort is the facilitation of information and communication technologies to allow two-way communication and an automated control of devices. The underlying communication topology is essential for the smart grid and is what enables the smart grid to be smart. Analyzing, simulating, designing, and comparing smart grid infrastructures but also optimizing routing algorithms, and predicating impacts of failures, all of this relies on deep knowledge of a smart grids communication topology. However, since smart grids are still in a research and test phase, it is very difficult to get access to real-world topology data. In this paper we provide a comprehensive analysis of the power-line communication topology of a real-world smart grid, the one currently deployed and tested in Luxembourg. Building on the results of this analysis we implement a generator to automatically create random but realistic smart grid communication topologies. These can be used by researchers and industrial professionals to analyze, simulate, design, compare, and improve smart grid infrastructures.

A Native Versioning Concept to Support Historized Models at Runtime

Models@run.time provides semantically rich reflection layers enabling intelligent systems to reason about themselves and their surrounding context. Most reasoning processes require not only to explore the current state, but also the past history to take sustainable decisions e.g. to avoid oscillating between states. Models@run.time and model-driven engineering in general lack native mechanisms to efficiently support the notion of history, and current approaches usually generate redundant data when versioning models, which reasoners need to navigate. Because of this limitation, models fail in providing suitable and sustainable abstractions to deal with domains relying on history-aware reasoning. This paper tackles this issue by considering history as a native concept for modeling foundations. Integrated, in conjunction with lazy load/storage techniques, into the Kevoree Modeling Framework, we demonstrate onto a smart grid case study, that this mechanisms enable a sustainable reasoning about massive historized models.

Generic cloud platform multi-objective optimization leveraging models@run.time

Cloud computing promises scalable hosting by offering an elastic management of virtual machines which run on top of hardware data centers. This elastic management as a cornerstone of PaaS (Platform As A Service) has to deal with trade-offs between conflicting requirements such as cost and quality of service. Solving such trade-offs is a challenging problem. Indeed, most of PaaS providers consider only one optimization axis or ad-hoc multi-objective resolution techniques using domain specific heuristics. This paper aims at proposing a generic approach to build cloud optimization by combining modeling and search based paradigms. Our approach is two-fold: 1) To reason about a cloud environment, we use a Models@run.time approach to have an abstraction layer of a cloud configuration that supports monitoring capabilities and represents cloud intrinsic parameters like cost, load information, etc. 2) We use a search-based algorithm to navigate through cloud candidate configuration solutions in order to solve the Cloud Multi-objective Optimization Problem (CMOP). We validate our approach based on a case study that we define with our cloud provider partner EBRC as representative of a dynamic management problem of heterogeneous distributed cloud nodes. We implement a prototype of our PaaS supervision framework using Kevoree, a Models@run.time platform. The prototype shows the efficiency of our approach in terms of finding possible cloud configurations in reasonable time. The prototype is flexible since it enables an easy reconfiguration of the cloud customer optimization objectives.

Integrating IoT and IoS with a Component-Based Approach

There is a growing interest in leveraging Service Oriented Architectures (SOA) in domains such as home automation, automotive, mobile phones or e-Health. With the basic idea (supported in e.g. OSGi) that components provide services, it makes it possible to smoothly integrate the Internet of Things (IoT) with the Internet of Services (IoS). The paradigm of the IoS indeed offers interesting capabilities in terms of dynamicity and interoperability. However in domains that involve “things” (e.g. appliances), there is still a strong need for loose coupling and a proper separation between types and instances that are well-known in Component-Based approaches but that typical SOA fail to provide. This paper presents how we can still get the best of both worlds by augmenting SOA with a Component-Based approach. We illustrate our approach with a case study from the domain of home automation.

Stream my models: Reactive peer-to-peer distributed models@run.time

The models@run.time paradigm promotes the use of models during the execution of cyber-physical systems to represent their context and to reason about their runtime behaviour. However, current modeling techniques do not allow to cope at the same time with the large-scale, distributed, and constantly changing nature of these systems. In this paper, we introduce a distributed models@run.time approach, combining ideas from reactive programming, peer-to-peer distribution, and large-scale models@run.time. We define distributed models as observable streams of chunks that are exchanged between nodes in a peer-to-peer manner. A lazy loading strategy allows to transparently access the complete virtual model from every node, although chunks are actually distributed across nodes. Observers and automatic reloading of chunks enable a reactive programming style. We integrated our approach into the Kevoree Modeling Framework and demonstrate that it enables frequently changing, reactive distributed models that can scale to millions of elements and several thousand nodes.

Scapegoat: An Adaptive Monitoring Framework for Component-Based Systems

Modern component frameworks support continuous deployment and simultaneous execution of multiple software components on top of the same virtual machine. However, isolation between the various components is limited. A faulty version of any one of the software components can compromise the whole system by consuming all available resources. In this paper, we address the problem of efficiently identifying faulty software components running simultaneously in a single virtual machine. Current solutions that perform permanent and extensive monitoring to detect anomalies induce high overhead on the system, and can, by themselves, make the system unstable. In this paper we present an optimistic adaptive monitoring system to determine the faulty components of an application. Suspected components are finely instrumented for deeper analysis by the monitoring system, but only when required. Unsuspected components are left untouched and execute normally. Thus, we perform localized just-in-time monitoring that decreases the accumulated overhead of the monitoring system. We evaluate our approach against a state-of-the-art monitoring system and show that our technique correctly detects faulty components, while reducing overhead by an average of 80%.

Reactive Security for Smart Grids Using Models@run.time-Based Simulation and Reasoning

Smart grids leverage modern information and communication technology to offer new perspectives to electricity consumers, producers, and distributors. However, these new possibilities also increase the complexity of the grid and make it more prone to failures. Moreover, new advanced features like remotely disconnecting meters create new vulnerabilities and make smart grids an attractive target for cyber attackers. We claim that, due to the nature of smart grids, unforeseen attacks and failures cannot be effectively countered relying solely on proactive security techniques. We believe that a reactive and corrective approach can offer a long-term solution and is able to both minimize the impact of attacks and to deal with unforeseen failures. In this paper we present a novel approach combining a Models@run.time-based simulation and reasoning engine with reactive security techniques to intelligently monitor and continuously adapt the smart grid to varying conditions in near real-time.

Analyzing Complex Data in Motion at Scale with Temporal Graphs

Modern analytics solutions succeed to under- stand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics algorithms to deal with more and more complex data, which can be structured as graphs and evolve over time. However, the underlying data storage systems that support large-scale data analytics, such as time-series or graph databases, fail to accommodate both dimensions, which limits the integration of more advanced analysis taking into account the history of complex graphs, for example. This paper therefore introduces a formal and practical definition of temporal graphs. Temporal graphs provide a compact representation of time-evolving graphs that can be used to analyze complex data in motion. In particular, we demonstrate with our open-source implementation, named GreyCat, that the performance of temporal graphs allows analytics solutions to deal with rapidly evolving large-scale graphs.

Polymer: A model-driven approach for simpler, safer, and evolutive multi-objective optimization development

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used to optimize various domains such as finance, science, engineering, logistics and software engineering. Nevertheless, MOEAs are still very complex to apply and require detailed knowledge about problem encoding and mutation operators to obtain an effective implementation. Software engineering paradigms such as domain-driven design aim to tackle this complexity by allowing domain experts to focus on domain logic over technical details. Similarly, in order to handle MOEA complexity, we propose an approach, using model-driven software engineering (MDE) techniques, to define fitness functions and mutation operators without MOEA encoding knowledge. Integrated into an open source modelling framework, our approach can significantly simplify development and maintenance of multi-objective optimizations. By leveraging modeling methods, our approach allows reusable optimizations and seamlessly connects MOEA and MDE paradigms. We evaluate our approach on a cloud case study and show its suitability in terms of i) complexity to implement an MOO problem, ii) complexity to adapt (maintain) this implementation caused by changes in the domain model and/or optimization goals, and iii) show that the efficiency and effectiveness of our approach remains comparable to ad-hoc implementations.

Beyond discrete modeling: A continuous and efficient model for IoT

Internet of Things applications analyze our past habits through sensor measures to anticipate future trends. To yield accurate predictions, intelligent systems not only rely on single numerical values, but also on structured models aggregated from different sensors. Computation theory, based on the discretization of observable data into timed events, can easily lead to millions of values. Time series and similar database structures can efficiently index the mere data, but quickly reach computation and storage limits when it comes to structuring and processing IoT data. We propose a concept of continuous models that can handle high-volatile IoT data by defining a new type of meta attribute, which represents the continuous nature of IoT data. On top of traditional discrete object-oriented modeling APIs, we enable models to represent very large sequences of sensor values by using mathematical polynomials. We show on various IoT datasets that this significantly improves storage and reasoning efficiency.