International audience

With the growth of the worldwide number of IoT devices, finding the right placement and storage of the massive created data is a major challenge. In fact, most IoT applications are time-sensitive applications implemented in critical sectors where any delay can hinder their decision-making, operational execution and decrease their performance. Fog Computing is a better solution to reduce latency by storing and processing real-time data locally on the edge network. Efficient placement strategies are required to map data to distributed infrastructure nodes. Current solutions do not consider the mobility of IoT devices which exists in almost all the real systems. In this paper, we use the random waypoint model to shape the mobility of an IoT system. Furthermore, we formulate our data placement using multiple replicas as a multi-objective linear programming problem aimed at minimizing the overall system latency and the number of data replicas. Then, we propose a greedy heuristic algorithm for IoT devices mobility modeling and data assignments in a feasible time. It uses a refined method to filter possible solutions in order to find the final solution with minimum overall latency while fulfilling producer data storage resource requirements and taking into account data sharing between the distributed consumers in Fog infrastructure. We conducted the experiments with iFogSim simulator respecting two data consistency models for data replicas synchronization. The results show that the proposed data placement strategy improves significantly the overall system latency and the number of data replicas to use.

The growing adoption of Fog computing for the sensitive-time IoT applications allows to facilitate the real-time actions and to enhance their efficiency and performance. In fact, keeping the data in the distributed Fog network brings the advantages and power of the Cloud closer to where data are generated while saving network bandwidth and reducing latency and operational costs. However, due to the diversity of the Fog nodes, IoT system distribution and data sharing, how and where to place the produced data with low latency is a main challenge. Moreover, a data placement based on a single replica cannot meet the data access requirements of all data consumers that have different topology positions. Thus, in this paper, we propose a multi-objective optimization data placement model in a hybrid Fog-Cloud environment based on multiple data replicas. It aims to find better distributed data storage while optimizing the overall system latency and the used storage space by minimizing the data replicas and following full and partial data replication methods. Further, we propose a greedy algorithm $iFogDP_h$ which uses a refined method to find a solution for assigning the IoT data to the appropriate data hosts in polynomial time by reducing the time required to transfer data for storage, access and replication. We conducted the experiments on iFogSim, a toolkit for modeling and simulation of Fog environments. The experimental results show the effectiveness of our proposed solution in terms of latency, storage overhead and the number of data replicas compared to the existing strategies.

The growing complexity of socio-technical systems makes setting their long-term valid resilience assessment approaches a very challenging task. In this context, the Functional Resonance Analysis Method (FRAM) has been previously developed to enable resilience engineers to model and evaluate complex socio-technical systems. It is one valid qualitative method generating a functional (rather than structural) model of the relationships between sub/systems and has the potential to produce inputs suitable for safety assurance and risk analysis methods. The main objective of this paper is to propose a new qualitative-quantitative resilience assessment approach to quantify the resilience of a complex socio-technical system. The proposed approach extends the qualitative FRAM, with a quantitative and analytic method. Our motivation is to combine the confirmed advantages of FRAM and graph theory in socio-technical systems assessment and decision-making support. In particular, this study contributes to the evolution of FRAM by i) considering the structural as well as the functional dimensions of a socio-technical system and, ii) proposing a set of key performance indicators aggregated at the end into a unique system resilience indicator that expresses its total variability during time. By the end of this paper, the proposed approach is illustrated with a simulated case study in the context of crisis management.

Summary The cloud market is nowadays a complex environment where cloud application providers need to maximize their monetary profit and end users look for the most efficient services with the lowest prices. For efficient cloud provisioning, both providers and users should be satisfied in spite of their conflicting needs. Negotiation is the most flexible solution to solve conflicts and enable reaching win‐win agreements. Cloud application provisioning has 2 main properties that highly impact the negotiation decision‐making models: (1) The interdependence between the business layer and the resource layer, and (2) the dynamic provisioning context (eg, negotiators’ preferences and application scheduler). Despite the importance of these 2 properties, the current negotiation models do not take these properties into account. This paper addresses both issues. We propose a multilayer negotiation framework, which also encompasses the potential for dynamic provisioning context. For that, we propose, first, a generic negotiation model dedicated to cloud provisioning. And second, we present its instantiation among cloud layers for efficient SaaS provisioning, to maximize provider profit and increase user satisfaction. The experiments show the benefits of adding negotiation to the provisioning process by improving it for provider profit, number of accepted requests, and client satisfaction.

Collaborative learning environments bring together learners from different sociocultural contexts, around a common task. Besides, these environments are emotional places where learners frequently experience emotions and bring emotions that concern events from outside the learning environment. Moreover, learners express, handle and regulate their emotion differently according to the sociocultural context to which they belong. And as it was proven by empirical research studies, emotions can have important effects on students’ learning and achievement. Therefore, Detecting, understanding, handling and regulating the learner emotion and understanding their cultural differences is a key issue that need to be tackled to enhance collaborative learning. To do so, we propose the emoculture ontology, a domain ontology for representing relevant aspects of affective phenomena and their culture differences in collaborative learning environments. In this paper, we will discuss first the concept of emotion and its relations with learning, collaborative learning and culture. Second, we will present a set of selected existing emotion ontologies which will be compared in the same section according to criteria relevant to our study. Third, we will describe the process upon which EmoCulture was built. Finally, we will discuss the quality of the proposed ontology and how it will be used in future works to guide the building of an emotional and cultural aware collaborative learning environment.

The performance of an organization depends mainly on the performance of actors' coordination. In this paper, we consider coordination through organizational structures (OS) defined by roles and relations between them. The aim of this paper is to recommend performant OS combining Agent-based methodology with graph theory. The performance of an OS can be measured by different criteria such as robustness, flexibility and efficiency. However, the OS of an organization is not stable and could evolve through time in order to adapt to changes and perturbations of the environment. So, OS recommendations should be done not only at design time but also dynamically at run time. In this context, we propose a hybrid approach to recommend in a coherent way performant OS at these two different stages. At design time, from a set of roles and their authorized relationships, we recommend the most performing organizational structure corresponding to a trade-off between robustness, flexibility and efficiency. At run time, based on the monitoring of an organization structure and its evolution, we recommend OS adaptations to improve its performance. This approach has been implemented and evaluated through a crisis management case study where organization performance is of paramount importance.

To fulfill its complex business demands, an organization may require the integration of many SaaS applications. A main challenge in this regard is to select the suitable SaaS offerings that satisfy all the business requirements. Due to the diversity of QoS needs and the large number of SaaS providers, selecting the suitable set of SaaS applications to be integrated may result in QoS degradation and higher costs. In this paper, we propose to use a System-of-Systems (SoS)-based approach for the selection and integration of SaaS. We believe that the use of SoS and its dynamic properties could improve the service selection at SaaS level. To do so, we propose three Mixed Integer Programming models that optimize the global SaaS behavior by minimizing the overall delivery costs, and maximizing the global functional and QoS utilities. The implementation of the approach and the experimental results highlight the efficiency of our proposed solution.

Relationships multiplicity and diversity within realworld social network have brought new challenges in community discovery for multilayer social network. In this context, many approaches have been made to cope with this issue. However, most of the proposed ones until recently ignore a key characteristic of social network which is dynamicity. In the scope of this paper, we propose a new approach to discover stable communities in dynamic multilayer social networks. To do so, a hybrid algorithm is proposed in order to discover communities at each time. This algorithm aims to optimize a generalized combined metric which considers simultaneously the network structure and the homophily of participants. Then, we propose to use the temporal information so that we find stable communities. Finally, a comparison with other community discovery approaches shows that the approach is competitive and it is able to extract high quality and stable communities in practical cases.

As an extended cloud solution, the cloud federation is designed to address the highly increasing complexity of business needs by providing capabilities on demand, at a better service level agreement (SLA). In fact, Cloud federation could not only be an appropriate way for managing the intensive-resource requirements but also it may be a powerful coordination environment for meeting complex business needs by easily integrating different distributed SaaS systems. Building a SaaS system that could satisfy a more complex need, upon existing ones deployed in a federated cloud environment could be treated as a System of Systems (SoS). The individual SaaS systems behave independently and, cooperate with each other to emerge a new resulting behavior. Thus, building an overall SaaS system from a set of distributed SaaS systems, deployed on heterogeneous cloud environments may increase the overall delivery costs. In this paper, we focus on how building a large SaaS system from a set of existing ones, while maximizing the desirable resulting behavior and minimizing the total delivery costs. Thus, we propose a cost-efficient composition approach for building a federated cloud-based SoS, by considering both the functional and non-functional requirements to obtain the best composition in terms of delivery costs. We implement an agent based SoS composition simulator providing composition scenarios and their related QoS and evaluating the different special cloud criterion that can influence the overall SoS delivery cost.

Within multilayer social networks, finding relevant communities for each specific situation has been a challenging task. Thus, modeling these social networks is the key issue for the process of contextualized community detection. However, traditional formalisms for representing multilayer social networks suffer from the lack of semantics. In the scope of this paper, we propose a hybrid modeling approach to represent participants and community detection context in multilayer social network. This approach combines a semantically rich description of social data (Ontology-based model) with a powerful mathematical abstraction (Graph-based model). Furthermore, we present a modeling scenario in the field of emergency management to illustrate how the proposed model can be used to contextualize community detection within a real social network. Finally, a comparison with another modeling approach is given in order to evaluate the proposed model performance.

Within real-world social networks people are linked with multiple types of relationships, which brings new challenges in community detection for multilayer social network where each layer represents one type of relationships. However, most of existing approaches consist on transforming the problem into a classical problem of community detection in monoplex network. In this work, we propose a new hybrid community detection approach in multilayer social networks. This approach considers simultaneously the network structure (different social connections) and the homophily of participants (similarities between users). To do so we propose a new multiplex information graph model to represent multilayer social network. Then, we adapt a combined community detection algorithm to the multiplex case. Furthermore, an example in the field of scientific collaboration recommendation is given to illustrate the practical usefulness of the proposed approach. Finally, a comparison with other community detection approaches evaluates its performance.

There was a growing interest in the development of CSCL (Computer Supported Collaborative Learning) environments over the last decade. Despite the plethora of tools available, they ignore the cultural differences between learners. In fact, especially, in the case of distant learning, these environments may bring together learners from different socio-cultural backgrounds that have acquired different socio-cultural values and behaviors and developed different needs, points of view and learning styles. Thus, CSCL environments need to be socio-culturally adapted to each learner’s culture. The main goal of this paper is to emphasize the need of socio-cultural aware collaborative learning, to present our proposed ontology-driven adaptation approach to meet this need, its operationalization and its experimental validation. Results and limits are discussed at the end of the paper.

Software-as-a-Service (SaaS) providers offer on-demand, highly scalable applications to the end users. To maximize their profit, the providers must make profit-aware scheduling decisions about assigning client requests to virtual resources, while respecting the agreed upon Service-Level Agreement (SLA). Given the highly dynamic nature of the cloud environment, unexpected events may affect the initial scheduling plans, which leads to unanticipated SLA violations. Thus, an unaccounted event may create a lose-lose situation between provider and client. If the SLA is violated the provider must pay the potentially high penalty that is negotiated within the original SLA. But from the client's viewpoint, an SLA violation may cause cancellation of a business-critical job, and no ordinary SLA penalty can compensate for the loss of the client's business. The provider's reputation could also suffers as the number of such SLA violations grows, resulting in loss of future clients. On the contrary of most existing work that assume that once established the SLA cannot be modified, we propose to convert the lose-lose situation into a win-win one through an automated renegotiation mechanism. When an event threatens a lose-lose violation of the SLA, the renegotiation mechanism is launched to establish a new SLA that limits the losses on the two sides. Experiments show that this new approach minimizes the loss in profit of the provider and minimizes the number of cancelled jobs experienced by the client, as compared with enforcing the original SLA.

Scalability is a key issue for Multi-Agent Systems (MAS) that aim to model and simulate complex systems. Distributed infrastructures such as clusters, grids and clouds are powerful computational environments that can be effectively used to run large-scale agent-based simulations. To properly distribute an agent-based system and ensure its performance, an appropriate partitioning approach is required. Although multiple partition methods for distributed MAS exist, they remain specific to the individual requirements of a given application domain. There is no generic approach for guiding the designers and developers to select an appropriate approach for partitioning a given agent-based system. Thus a recurrent challenging task, for MAS designers and developers, is how to evaluate, select and then apply the appropriate partitioning mechanism for a given MAS. Therefore, in this paper, we present a generic conceptual framework useful to analyze existing partitioning methods. It can also be used as a basis while designing a distributed architecture of new MAS.

The concept of System-of-Systems (SoS) presents a high level perspective that explains the interactions between independent subsystems. In the context of SoS, the issue of interoperability is crucial especially when integrating multiple heterogeneous and independently governed systems. Any solution is naturally applied to resolve a problem in a precise context, where context specificities are implicitly or explicitly processed. In the scope of this paper, we tackle the problem of SoS interoperability knowledge description taking into account the notion of context. To structure this description, we propose ontology design patterns (ODPs) that describe the notion of “context” in the field of SoS Interoperability. These ODPs was defined based on a Foundational Ontology (FO) for “Context” description. An example in the field of Traffic management is given to illustrate the practical usefulness of the proposed ontology.

Collaborative learning environments bring together learners from different cultures and social contexts, around a common task. These learners interact both with each other and with computers. Hence, a dual problem arises: how to model and integrate socio-cultural factors that characterize these learners? How to design and develop culture-aware collaborative learning environments? This paper addresses both issues by describing an ontology based socio-cultural adaptation approach and its operationalization leading to the implementation of a culture-aware-web-based collaborative system. The adaptation of the collaborative learning environment is performed according to the socio-cultural profile of each learner.

International audience

This paper presents a new approach for Arabic non-vocalized texts disambiguation based on a possibilistic classifier. A morphological analyzer provides all the possible solutions and the values of the morphological features of words. When texts are vocalized, the number of solutions is reduced and in many cases, we can identify the correct analysis of the input word. The main idea of this paper is to exploit this type of texts in order to learn contextual dependencies between the different values of morphological features modeled as a possibilistic network. This knowledge is used later to disambiguate non-vocalized texts. In order to evaluate our approach, we perform experiments on a corpus of arabic stories. In this paper, we present results concerning the Part-Of-Speech (POS) which is the main morphological feature. Our results are compared to the SVM-based system called MADA.

Understanding software development process has always been a great challenge in the software engineering field. Actual engineering has many aspects and processes that need to be well understood and modeled. We focus on simulating the development process according to two complementary points of view: method and application engineering views. We use a formalism to represent the process model, which is the map. Maps are dynamic: they provide several non-deterministic strategies to achieve given intentions from given products. Navigation in a map is dynamic. We design and develop an agent-based simulator where its main components are environment and actors: the environment which is composed of the map structure being simulated and the product being developed; software engineers are modeled as autonomous agents able to select sections and achieve intentions. By agent cloning, we were able to develop exhaustive and concurrent multi-process and multiproduct building. Our simulation supports engineers in building their maps and validating process models by giving an exhaustive and simultaneous navigation through one map. Maps incoherencies have been detected and flexibility assessed.