7.1.1 ENOS

• Name:
  Experimental eNvironment for OpenStack
• Keywords:
  OpenStack, Experimentation, Reproducibility
• Functional Description:

  Enos workflow :

  A typical experiment using Enos is the sequence of several phases: - enos up : Enos will read the configuration file, get machines from the resource provider and will prepare the next phase - enos os : Enos will deploy OpenStack on the machines. This phase rely highly on Kolla deployment. - enos init-os : Enos will bootstrap the OpenStack installation (default quotas, security rules, ...) - enos bench : Enos will run a list of benchmarks. Enos support Rally and Shaker benchmarks. - enos backup : Enos will backup metrics gathered, logs and configuration files from the experiment.

• URL:
  https://­github.­com/­BeyondTheClouds/­enos
• Publication:
  hal-01664515
• Contact:
  Adrien Lebre
• Participants:
  Mathieu Simonin, Marie Delavergne, Adrien Lebre, Baptiste Jonglez
• Partner:
  Orange Labs

7.1.2 EnOSlib

• Name:
  EnOSlib is a library to help you with your experiments
• Keywords:
  Distributed Applications, Distributed systems, Evaluation, Grid Computing, Cloud computing, Experimentation, Reproducibility, Linux, Virtualization
• Functional Description:

  EnOSlib is a library to help you with your distributed application experiments. The main parts of your experiment logic is made reusable by the following EnOSlib building blocks:

  - Reusable infrastructure configuration: The provider abstraction allows you to run your experiment on different environments (locally with Vagrant, Grid’5000, Chameleon and more) - Reusable software provisioning: In order to configure your nodes, EnOSlib exposes different APIs with different level of expressivity - Reusable experiment facilities: Tasks help you to organize your experimentation workflow.

  EnOSlib is designed for experimentation purpose: benchmark in a controlled environment, academic validation …

• URL:
  https://­discovery.­gitlabpages.­inria.­fr/­enoslib/
• Publications:
  hal-01664515, hal-01689726
• Contact:
  Mathieu Simonin
• Participants:
  Mathieu Simonin, Baptiste Jonglez, Marie Delavergne, Alexis Bitaillou

7.1.3 Concerto

• Name:
  Concerto
• Keywords:
  Reconfiguration, Distributed Software, Component models, Dynamic software architecture
• Functional Description:
  Concerto is an implementation of the formal model Concerto written in Python. Concerto allows to : 1. describe the life-cycle and the dependencies of software components, 2. describe a components assembly that forms the overall life-cycle of a distributed software, 3. automatically reconfigure a Concerto assembly of components by using a set of reconfiguration instructions as well as a formal operational semantics.
• URL:
  https://­gitlab.­inria.­fr/­VeRDi-project/­concerto
• Publications:
  hal-03103714, hal-02535077, hal-01897803
• Contact:
  Hélène Coullon
• Participants:
  Christian Perez, Hélène Coullon, Maverick Chardet, Simon Robillard
• Partners:
  IMT Atlantique, LS2N, LIP

7.1.4 6TiSCH Simulator

• Name:
  High-level simulator of a 6TiSCH network
• Keywords:
  Network simulator, 6TiSCH
• Functional Description:
  The simulator is written in Python. While it doesn't provide a cycle-accurate emulation, it does implement the functional behavior of a node running the full 6TiSCH protocol stack. This includes RPL, 6LoWPAN, CoAP and 6P. The implementation work tracks the progress of the standardization process at the IETF.
• Publication:
  hal-03919553
• Contact:
  Malisa Vucinic
• Participant:
  Remous Koutsiamanis

7.2.1 OpenStack

Participants: Geo Johns Antony, Alexis Bitaillou, Marie Delavergne, Baptiste Jonglez, Adrien Lebre.

OpenStack is the de facto open-source management system to operate and use Cloud Computing infrastructure. Started in 2012, the OpenStack foundation gathers 500 organizations including groups such as Intel, AT&T, RedHat, etc. The software platform relies on tens of services with a 6-month development cycle. It is composed of more than 2 millions of lines of code, mainly in Python, just for the core services. While these aspects make the whole ecosystem quite swift, they are also good signs of maturity for this community.

We created and animated between 2016 and 2018 the Fog/Edge/Massively Distributed (FEMDC) Special Interest Group and have been contributing to the Performance working group since 2015. The former investigates how OpenStack can address Fog/Edge Computing use cases whereas the latter addresses scalability, reactivity and high-availability challenges. In addition to releasing white papers and guidelines 58, the major result from the academic view point is the aforementioned EnOS solution, a holistic framework to conduct performance evaluations of OpenStack (control and data plane). In May 2018, the FEMDC SiG turned into a larger group under the control of the OpenStack foundation. This group gathers large companies such as Verizon, ATT, etc. Although our involvment has been less important since 2020, our participation is still signficant. For instance, we co-signed the second white paper delivered by the edge working group in 2020  59 and are still taking part to the working group, investigating new use-cases as well as resulting challenges.

7.2.2 Grid'5000

Participants: Remous Aris koutsiamanis, Baptiste Jonglez, Adrien Lebre, Jean Marc Menaud.

7.2.3 SeDuCe

Participants: Remous Aris koutsiamanis, Baptiste Jonglez, Jean Marc Menaud.

The SeDuCe Project aims to deliver a research testbed dedicated to holistic research studies on energetical aspects of datacenters. Part of the Grid'5000 Nantes' site, this infrastructure is composed of probes that measure the power consumption of each server, each switch and each cooling system, and also measure the temperature at the front and the back of each servers. These sensors enable reasearch to cover a full spectrum of the energetical aspect of datacenters, such as cooling and power consumption depending of experimental conditions.

The testbed is connected to renewable energy sources (solar panels). This “green” datacenter enables researchers to perform real experiment-driven studies on fields such as temperature based scheduling or “green” aware software (i.e., software that take into account renewable energies and weather conditions).

7.2.4 PiSeDuCe

Participants: Remous Aris koutsiamanis, Baptiste Jonglez, Jean Marc Menaud.

In 2021, we consolidated the development of PiSeDuCe, a deployment and reservation system for Edge Computing infrastructures composed of multiple Raspberry Pi Cluster started in 2020. Typically, a cluster of 8 Raspberry Pi costs less than 900 euros and only needs an electrical outlet and a wifi connection for its installation and configuration. Funded by the CNRS through the Kabuto project, and in connection with the SLICES-FR initiative, we have extended PiSeduce to propose a device to cloud deployment system (from devices on Fit IoTLab to servers in Grid’5000). PiSeDuCe and SeDuce led us to submit the Samurai CPER proposal.

7.2.5 SAMURAI

Participants: Remous Aris koutsiamanis, Baptiste Jonglez, Jean Marc Menaud.

In 2022 the SAMURAI (Sustainable And autonoMoUs gReen computing for AI) project was accepted, as a part of the energy and digital transition theme. The project aims at reinforcing an innovative hardware infrastructure for the scientific study of the intersecting problems of computing infrastructure that supports artificial intelligence and its energy autonomy. SAMURAI will extend SeDuCe into energy autonomy by adding a smart and clean energy storage system. Additionally, it will extend the capabilities of the platform by adding AI computing nodes (GPUs) for the scientific study of AI tools. Finally, it will also add new sensor nodes within the Nantes connected object platform (Nantes nodes of the national FIT IoT-Lab platform) to support future work on embedded AI.

7.2.6 SLICES-FR/SLICES

Participants: Remous Aris koutsiamanis, Baptiste Jonglez, Adrien Lebre, Jean Marc Menaud.

Replica indexation:

 The first contribution we have made is related to how peers in a geo-distributed infrastructure could maintain an index of relevant replicas 26. Although the holy grail of how to store and manipulate data in Edge infrastructures is yet to be found, state-of-the-art approaches demonstrated the relevance of replication strategies that bring content closer to consumers: The latter enjoy better response time while the volume of data passing through the network decreases overall. Unfortunately, locating the closest replica of a specific content requires indexing every live replica along with its location. Relying on remote services enters in contradiction with the properties of Edge infrastructures as locating replicas may effectively take more time than actually downloading the content. At the opposite, maintaining such an index at every node would prove overly costly in terms of memory and traffic, especially since nodes can create and destroy replicas at any time. In this paper, we abstract content indexing as distributed partitioning: every node only indexes its closest replica, and connected nodes with a similar index compose a partition. Our decentralized implementation AS-cast is (i) efficient, for it uses partitions to lock down the traffic generated by its operations to relevant nodes, yet it (ii) guarantees that every node eventually acknowledges its partition despite concurrent operations. Our complexity analysis supported by simulations shows that AS-cast scales well in terms of generated traffic and termination time. As such, AS-cast can constitute a new building block for geo-distributed services.

Network resource management:

 As aforementioned, the team has initiated new activities covering the Cloud–IoT continuum. Two recent topics in this context have been investigated resulting in two survey papers. First, in 15, we have surveyed the state of the art on intelligent management in next-generation networks, which can be applied to Cloud–IoT continuum. We started by presenting a comprehensive background beginning from conventional machine learning (ML) algorithms and deep learning (DL) and follow this with a focus on different dimensionality reduction techniques, which is a crucial topic for scalability. Afterward, we presented the study of ML/DL applications in a sofwarized environment and highlighted the issues and challenges that should be considered. Second, as the Internet of Things (IoT) has remarkably evolved over the last few years, incentive techniques such as the blockchain, game theory, and Artificial Intelligence (AI) are highly desirable to build a sustainable IoT ecosystem. For these reasons, we have presented in 20 a systematic literature review of the incentive techniques for IoT, aiming to provide an overview of incentive-enabled IoT from background, motivations, and enabling techniques. Moreover, in the context of radio resource management in IoT networks, in 17, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags, a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications are scheduled globally (using UWB-TSCH), allowing deterministic channel access and low power consumption. We designed a centralized scheduler which organizes nodes into cells, allowing simultaneous localizations and data transmissions, making indoor positioning system (IPS) more scalable and reducing deployment costs. We show through simulations that high positioning rates can be achieved, even in large (400-cell/400-tag) networks, with low schedule calulation times (less than 11s).

Finally, considering heterogeneity in emerging network traffic, traffic classification can enable a number of practical applications ranging from network monitoring to resource management. Numerous ML models have been applied to identify network applications. However, among the applied models so far, no model outperforms all the others. To solve these issues, we have proposed in 14 a novel DL-based approach that incorporates multiple Decision Tree based models. This approach employs a non-linear blending ensemble method by combining tree-based classifiers through DL in order to maximize generalization accuracy. This ensemble consists of two levels called base classifiers and meta-classifiers. In the first level, Decision Tree-based models are used as the base classifiers while in the second level, DL is used as a meta-model to combine the outputs of the base classifiers.

Resource management for scientific workflows:

The goal of a workflow engine is to facilitate the writing, the deploying, and the execution of a scientific workflow (i.e., graph of coarse-grain and heterogeneous tasks) on distributed infrastructure. With the democratization of the Cloud paradigm, many workflow engines of the state-of-the-art offer a way to execute workflows on distant data centers by using the Infrastructure-as-a-Service (IaaS) or the Function-asa- Service (FaaS) services of Cloud providers. Hence, workflow engines can take advantage of the (presumably) infinite resources and the economical model of the Cloud. However, two important limitations lie in this vision of Cloud-oriented workflow engines. First, by using existing services of Cloud providers, and by managing the workflows at the user side, the Cloud providers are unaware of both the workflows and their user needs, and cannot apply specific resource optimizations to their infrastructure. Second, for the same reasons, handling the heterogeneity of tasks (different operating systems) in workflows necessarily degrades either the transparency for the users (who must provision different types of resources), or the completion time performance of the workflows, because of the stacking of virtualization layers. In 24, we tackle these two limitations by presenting a new Cloud service dedicated to scientific workflows. Unlike existing workflow engines, this service is deployed and managed by the Cloud providers, and enables specific resource optimizations and offers a better control of the heterogeneity of the workflows. We evaluate our new service in comparison to Argo, a well-known workflow engine of the literature based on FaaS services. This evaluation was made on a real distributed experimental platform with a realistic and complex scenario.

Fog Modeling:

In 11, we propose a detailed overview of the current state-of-the-art in terms of Fog modeling languages. We relied on our long-term experience in Cloud Computing and Cloud Modeling to contribute a feature model describing what we believe to be the most important characteristics of Fog modeling languages. We also performed a systematic scientific literature search and selection process to obtain a list of already existing Fog modeling languages. Then, we evaluated and compared these Fog modeling languages according to the characteristics expressed in our feature model. As a result, we discuss in this paper the main capabilities of these Fog modeling languages and propose a corresponding set of open research challenges in this area.

Model-Driven Engineering (MDE):

 MDE is a software programming approach that raises the level of abstraction in traditional programming languages by using models with recurring design patterns. MDE simplifies the collaboration between development teams and more broadly promotes compatibility between systems. MDE is increasingly used to help in the development of distributed systems, microservices architectures, and IoT systems but is also leveraged, for instance, in lowcode platforms such as Node-RED to write IoT applications.

In the thesis of Jolan Philippe (defended in December 2022) two important drawbacks of MDE have been studied. First, as most of the high-abstraction level programming models, when dealing with very big models most MDE frameworks become poorly efficient and slow, thus losing their initial advantage of speeding up the development process. Second, because of the underlying complexity of MDE approaches, the level of confidence in MDE frameworks is usually low. In the thesis the formal transformation engine (transformation being an important part of MDE approaches) CoqTL has been extended with a more scalable implementation that is proven equivalent to the initial one, and that is automatically dumped to a Apache Spark code. A prototype has been implemented on top of Spark, and has been evaluated. In addition to this, the thesis also explores how to automatically tune our Spark-based transformation engine according to the input models (size, nature etc.). This contribution notably presents a feature model of different configurations of the transformation engine: a feature model that could be used in future work to automatically configure and reconfigure the engine, thus opening a door to the second part of this section.

In 22, we address the issue of formally modeling the architecture of the IoT in order to be able to provide security guarantees and/or to respect real-time properties. In this work we proposed a novel correct-by-construction formal approach based on an Event-B method to describe the physical architecture of IoT layers. This formal approach inspects four layers: the physical layer, the gateway layer, the middleware layer, and the application layer. An Electrocardiogram (ECG) IoT system is applied in our model as a case study. Finally, we proved and validated the correctness of our formal model by using proof obligations and the model checking tool called Rodin.

Software deployment/reconfiguration:

 If speeding up and simplifying the development process of distributed systems is of prior importance because of the increasing complexity of geographically distributed infrastructures, speeding up and simplifying the deployment of these systems, and enabling and speeding up their dynamic evolution through time is another key to making these infrastructures usable. Indeed, because of the scale of both geo-distributed infrastructure and distributed systems (e.g., microservices architectures) manually handling deployments and reconfiguration procedures is an error-prone, tedious and complex task, probably impossible in practice. Hence, these procedures need to be automated, or even better they need to become completely autonomous.

In 27, we tackle the planning problem for the reconfiguration of distributed systems in the component-based reconfiguration model Concerto. Specifically, given some tasks to execute and a desired final state of the system, we show how to compute a reconfiguration plan that guarantees satisfaction of inter-component dependencies and which is also optimized for parallel execution. Our technique relies on an SMT solver to compute the required dependencies between components and ultimately schedule the reconfiguration. We illustrate the use of this technique on a variety of synthetic examples as well as a real use case in the context of an OpenStack system.

Reifying geo-distribution at the software level:

One question to answer in the shift from the Cloud to the Edge computing paradigm is: how distributed applications developed for Cloud platforms can benefit from the opportunities of the Edge while dealing with inherent constraints of wide-area network links? Our solution to this question is to give the illusion of “single service images” spreading over the Edge infrastructure. Thanks to the modularity of micro-service based applications, one can deploy multiple instances of the same service (one per edge site) and deliver collaborations between them according to each request. This non-invasive approach is made possible by (i) a DSL that extends the application API and allows DevOps to program where/how the execution of each request should be executed, (ii) and its runtime, Cheops, a service that interprets and orchestrates each request in order to satisfy the geo-distribution parameters, allowing collaborations in a transparent manner for the underlying application. We demonstrate the relevance of our proposal by illustrating how Cheops can successfully geo-distribute the Kubernetes vanilla code 32, 25.

Secure and privacy-preserving distributed genetic analyses:

The amount of biological data collected and stored has grown significantly. By the amount of data collected, traditional scenarios are not feasible due to technical and legal restrictions moving data, especially on data of medical origin. This has motivated the scientific community to create global collaborations to analyze biomedical and share scientific findings responsibly. However, current tools do not support safely and efficiently these collaborations. In this paper, we review the state of the art identifying the needs to support these collaborations, which we characterize as Fully Distributed Collaborations (FDCs). We also investigate the technical and legal restrictions of sharing biomedical data. Additionally, we present a taxonomy of current tools to analyze biomedical data in distributed settings. The taxonomy considers three architectural key features to support FDC scenarios: data and computation placement, Privacy and Security, and, Performance and Scalability. The review reveals opportunities for multi-site analyses that encourage scientific collaborations while mitigating technical and legal constraints. Finally, we present these opportunities that serve as future work in this direction to promote collaborations around the analysis of biomedical data. 19

The Allele Frequency Net Database (AFND) is a repository that allows users to research and analyze immune gene frequencies in different populations around the world. This database contains allele, haplotype and genotype format information. They are collected in a common database based on the results and the work of users. With the massive increase in medical data and the strengthening of data governance laws, the proposal for a new distributed and secure model for the historically centralized method in AFND has become important. In 21, we have developed Distributed AFND, an alternative distributed version of AFND that allows users to perform their research and analysis in cooperation with other remote sites without sharing their original data and monitoring data access.

In the context of Sirine Sayadi's PhD thesis 31, we have considered the development of distributed biomedical data analyses as well as their deployment in IT infrastructure and their integration into real medical tools. As part of a kidney transplant application, we have provided a new distributed contextualization solution that helps clinicians assess patients’ kidney problems. For the same application, we have also proposed a new distributed version of Factor Analysis of Mixed Data (FAMD) for dimension reduction. In the context of analyses based on HLA genetic data, we have proposed a new distributed and secure model for the estimation of allele frequencies, haplotype frequencies and individual genotype frequencies. We also propose a new algorithm for the distributed estimation of HLA haplotype frequency using the expectation-maximization EM algorithm.

Despite the profitable usage of AI-based algorithms in the medical domain, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). In 16, we focused on the emerging deployment of FL, provide a broad overview of current approaches and existing challenges, and outline several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.

Network security and privacy:

The rapid development of the Internet and smart devices trigger surges in network traffic making its infrastructure more complex and heterogeneous. The predominant usage of mobile phones, wearable devices and autonomous vehicles are examples of distributed networks, which generate a huge amount of data each and every day. The computational power of these devices have also seen steady progression which has created the need to transmit information, store data locally and drive network computations towards edge devices. Intrusion detection systems play a significant role in ensuring security and privacy of such devices. In this domain, Machine Learning and Deep Learning with Intrusion Detection Systems have gained great momentum due to their achievement of high classification accuracy.

However, the privacy and security aspects potentially get jeopardized due to the need of storing and communicating data to a centralized server. On the contrary, federated learning (FL) fits in appropriately as a privacy-preserving decentralized learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. In 9, we aimed to present an extensive and exhaustive review on the use of FL in intrusion detection system. In order to establish the need for FL, various types of IDS, relevant ML approaches and its associated issues are discussed. The paper presents detailed overview of the implementation of FL in various aspects of anomaly detection. The allied challenges of FL implementations are also identified which provides an idea on the scope of future direction of research. The paper finally presents the plausible solutions associated with the identified challenges in FL based intrusion detection system implementation acting as a baseline for prospective research.

Security has also become a critical issue for Industry 4.0 due to different emerging cyber-security threats. Despite the huge amount of data generated by the Internet of Things (IoT) devices in Industry 4.0, it is difficult to get labeled data, because data labeling is costly and time-consuming. This poses many challenges for several DL approaches, which require labeled data. To handle these issues, we proposed in 13 a novel federated semi-supervised learning scheme, that takes advantage of both unlabeled and labeled data in a federated way. First, an AutoEncoder (AE) is trained on each device (using unlabeled local/private data) to learn the representative and low-dimensional features. Then, a cloud server aggregates these models into a global AE using Federated Learning (FL). Finally, the cloud server composes a supervised neural network, by adding fully connected layers (FCN) to the global encoder (the first part of the global AE) and trains the resulting model using publicly available labeled data.

Moreover, with the increasing development of beyond 5G and network softwarization, there is more more flexibility and agility in the network than ever. This can be exploited to integrate intelligence into the network, for example, for conceiving intelligent Intrusion detection systems (IDS). However, traditional approaches in this domain require all data (and their associated labels) to be centralized. Such approaches lead to: (i) a large bandwidth overhead, as raw data needs to be transmitted to the server, (ii) low incentives for devices to send their private data, and (iii) large computing and storage resources needed on the server side to label and treat all this data. To cope with the above limitations, we have proposed in 23, a semi-supervised federated learning model for IDS. Moreover, we use network softwarisation for automation and deployment. Our model combines Federated Learning and Semi-Supervised Learning where the clients train unsupervised models (using unlabeled data) to learn the representative and low-dimensional features and the server conducts a supervised model (using labeled data).

Finally, in 28 we have addressed the challenge of the regulation of traffic violations in intelligent transportation system networks. We have proposed using a blockchain smart contract-based method to perform vehicle speed detection and vehicle information collection through vehicle re-identification using smart contracts. The smart contract is a conditioned filter that follows regulatory rules from reporting violation points and fines for each violation to penalties. We have implemented the proposed algorithm and we have presented an evaluation of the proposed method leading to satisfactory results in comparison to other known methods.

Kelio (formely Bodet Software)

Participants: Thomas Ledoux.

In 2022, several activities were conducted, such as a hackathon with 70 participants, an event promoting the integration of international students at IMT Atlantique and preliminary discussions on the topic of a joint PhD thesis with the Stack team.

Alterway/Smile

Participants: Thomas Ledoux, Hiba Awad.

In 2020, during the preparation of the ANR SeMaFoR project, we started a cooperation with Alterway/Smile, an SME specialized in Cloud and DevOps technologies. This cooperation resulted in a joint PhD thesis (called Cifre) entitled "A model-based approach for dynamic, multi-scale distributed systems" started in Nov. 2021.

OVHcloud

Participants: Thomas Ledoux, Pierre Jacquet.

Pierre Jacquet started his PhD in October 2021, under a co-supervision with the Spirals team with the subject "Fostering the Frugal Design of Cloud Native Applications". Pierre presented a poster, ”Sampling Resource Requirements to Optimize Virtual Machines Sizing”, at the EuroSys conference in April 2022.

Orange

Participants: Paul Bori, Divi de Lacour, Adrien Lebre, Thomas Ledoux, Dan Freeman Mahoro, Jean-Marc Menaud, Duc-Thinh Ngo, Kandaraj Piamrat, Mario Südholt.

In 2022, Orange Labs and the Stack team launched several PhD grants.

Dan Freeman Mahoro initiated a a new collaboration on the topic of the “digital twin”, a dynamic virtual copy of a physical asset, process, system or environment that looks and behaves identically to its real-world counterpart. This cooperation has resulted in a joint doctoral dissertation entitled "Digital twins of complex cyber-physical systems," which began in April 2022, under a co-supervision with Orange team in Rennes.

Paul Bori started his PhD in January 2023, with the subject "Container application security: a programmable OS-level approach to monitoring network flows and process executions".

Duc-Thinh Ngo started his PhD in December 2022, on the subject "Dynamic graph learning algorithms for the digital twin in edge-cloud continuum", under a co-supervision with Orange team in Rennes.

Divi de Lacour started his PhD in January 2022, on the subject "Architecture et services pour la protection des données poursy stèmes coopératifs autonomes", under a co-supervision with the Orange team in Chatillon (Paris south region).

Ericsson

Participants: Samia Boutalbi, Mario Südholt, Remous-Aris Koutsiamanis.

Samia Boutalbi started her PhD in January 2022, on the subject "Secure deployment of micro-services in a shared Cloud RAN/MEC environment", under a co-supervision with the Ericsson team in Paris.

10.1.1 Visits to international teams

10.2.1 H2020 projects

10.3.1 ANR

10.3.2 PIA 4

10.3.3 CPER

10.3.4 Local and regional projects

10.3.5 Inria Challenges

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Adrien Lebre : keynote speaker at CIOT 2022 and SSS 2022, invited speaker at "journée Infrastructures pour la souverainté numérique" (CNAM Paris).

11.1.5 Scientific expertise

• Adrien Lebre : Member of the steering committee of the PEPR Cloud proposal (IMT representative).
• Kandaraj Piamrat : Reviewer for Information and Communication Technology Call 2022 at the Vienna Science and Technology Fund (WWTF)

11.1.6 Research administration

• Adrien Lebre : Member of the executive committee of the Grid’5000 GIS (Groupement d’intérêt scientifique), Co-director of the <I/O> Lab, a joint lab between Inria and Orange Labs.
• Jean-Marc Menaud : Deputy Director of the Laboratory of Digital Sciences of Nantes (LS2N) UMR CNRS 6004.

11.2.1 Teaching

As a team mainly composed of Associate Prof. and Prof., the amount of teaching activities is significant (around 150hours per person). We present here only the management activities.

• Thomas Ledoux : Head of the apprenticeship program in Software Engineering FIL. This 3-year program leads to the award of a Master degree in Software Engineering from the IMT Atlantique.
• Hélène Coullon : Responsible for the LOGIN training in computer science at IMT Atlantique (last year)
• Thomas Ledoux : Head of the Filière informatique nantaise since Sept. 2020. This entity, created by the University of Nantes, Centrale Nantes and IMT Atlantique, aims to bring together the main players in Computer Science training in Nantes to ensure a coherent and ambitious training offer that meets the present and future challenges of Computer Science. It is organized around a Council made up of representatives from the academic and socio-economic worlds.
• Thomas Ledoux : Member of the board of directors of Talents du numérique.
• Jacques Noyé : Deputy head of the Automation, Production and Computer Sciences Department of IMT Atlantique.
• Mario Südholt : Representative for MSc-level and PhD-level studies of the API department of IMT Atlantique.
• Kandaraj Piamrat : Elected member of scientific council at Faculty of Sciences and Techniques, Nantes University
• Kandaraj Piamrat : Responsible for Licence MIAGE, CS Department, Nantes University

11.2.2 Supervision

• PhD: Marie Delavergne, Cheops, a service-mesh to geo-distribute micro-service applica- tions at the Edge, Sept 2019, March 2023, Director: A. Lebre.
• PhD: Geo Johns Anthony, Resource management in geo-distributed infrastructures: the Kubernetes case, April 2021, Sept 2024, Director: A. Lebre.
• PhD: Jolan Philippe, Contribution to the analysis of the design-space of a distributed transformation engine, Setp 2019, Dec 2022, Director: G. Sunye (NaoMod Nantes), Advisors: H. Coullon, M. Tisi (NaoMod).
• PhD: Wilmer Garzon, Secure distributed workflows for biomedical data analytics, Sept 2019, Jan 2023, Director: M. Südholt, Advisor: D. Benavides.
• PhD: Sirine Sayadi, Distributed architectures and secure software containers for multi-site medical cooperation, Sept. 2019, Dec 2022, Director: M. Südholt.
• PhD: Samia Boutalbi, Secure deployment of microsservices in a shared RAN/MEC Cloud environment, Jan 2022, Jan 2025, Director: M. Südholt, advisor: R.-A. Koutsiamanis.
• PhD: Abdou Seck, Parallel transfer service for the exchange of large volumes of data between Datacenters, June 2022, June 2025, Director: J.-M. Menaud, Advisor: R.-A. Koutsiamanis.
• PhD: Pierre Jacquet, Fostering the Frugal Design of Cloud Native Applications, Oct 2021, Oct 2024, Director R. Rouvoy (SPIRALS Lille), Advisor: T. Ledoux.
• PhD: Hiba Awad, A Model-based Approach for Multi-Scale and Dynamic Distributed Systems, Nov. 2021 - March 2025. Director: T. Ledoux.
• PhD: Ons Aouedi, Machine learning-Enabled Network Traffic Analysis, Sept. 2O19, Nov. 2022, Director: B. Parrein (STR, Nantes) Advisor: K. Piamrat.
• PhD: Antoine Omond, Safe, efficient and low-energy self-adaptation for Cyber Physical Systems - Application to a scientific observatory in the Arctic tundra, Dec 2021, Dec 2024. Director: T. Ledoux, Advisor: H. Coullon.
• PhD: Dan Freeeman Mahoro, Digital twins of complex cyber-physical systems, Apr. 2022 - March 2025, Director: T. Ledoux (since Apr. 2022).
• Post-doc: Abdelghani Alidra, Modeling Fog-based systems, Sept 2021, Oct 2022, Advisor: T. Ledoux.
• Post-doc: Yasmina Bouizem, Measure and model the energy consumption of the procedures of deployment and reconfiguration at OVH, Sept 2022, Sept 2023, Advisor: H. Coullon.
• Post-doc: Eloi Perdereau, Configuration languages, OTPaaS, Sept 2022, March 2024, Advisor: J. Noyé.
• Resarch engineer: Alexis Bitaillou, Development support within the OTPaaS project, Sept 2022, Jan 2023, Advisor: A. Lebre.

11.2.3 Juries

• Hélène Coullon was a member of the selection commitee for two associate professor positions in Rennes.
• Adrien Lebre was a member of the seclection committe for two associate professor positions (Evry, Toulouse) and one professor position (Grenoble).
• Thomas Ledoux was the chairman of the PhD thesis jury of Yuwei Wang, ”Evolution of Microservice-based Applications: Modelling and Safe Dynamic Updating”, Télécom SudParis, Oct. 27, 2022.
• Thomas Ledoux was the chairman of the PhD thesis jury of Jolan Philippe, ”Contribution to the Analysis of the Design-Space of a Distributed Transformation Engine”, IMT Atlantique, Dec. 19, 2022.
• Jean-Marc Menaud was a member of the selection commitee for a professor position in Grenoble.
• Jean-Marc Menaud was an examinator of Safuriyawu Ahmed Thesis - Resilient IoT-based Monitoring System for the Nigerian Oil and Gas Industry. INSA Lyon, 12/22.
• Jean-Marc Menaud was a reviewer of Humberto Alvarez - An energy saving perspective for distributed environments: Deployment, scheduling and simulation with multidimensional entities for Software and Hardware - Université de Pau et Des Pays de l’Adour, 06/22.
• Jean-Marc Menaud was a reviewer of Brice EKANE APAH - Optimisation des entrées-sorties dans les architectures multi-tiers Input-output optimization in multi-tiers architectures Université GRenoble Alpes - 12/22.
• Jean-Marc Menaud was a reviewer of Houssam KANSO - Contributing to the Energy Efficiency of Smart Homes : An Automated Management Framework - Université de Pau et Des Pays de l’Adour, 12/22.
• Remous-Aris Koutsiamanis was an examinator of the thesis of Charlier Maximilien entitled "High density and large scale Ultra Wideband indoor positioning infrastructure", University of Mons (UMons), Belgium, May 3, 2022.
• Remous-Aris Koutsiamanis was an examinator of the thesis of David Hauweele entitled "Optimization of Radio Duty Cycling protocols in Wireless Sensor Networks", University of Mons (UMons), Belgium, December 16, 2022.

11.3.1 Articles and contents

• Thomas Ledoux , "[Tribune] Pour une informatique moins gourmande en énergie, formons les ingénieurs", L'Usine nouvelle, Jan. 2022.
• Jean-Marc Menaud , Énergie. Les data centers consomment-ils plus d'électricité que la SNCF ? Ouest-France Nov. 2022.
• Jean-Marc Menaud , Électricité : le numérique trop gourmand. Ouest-France Oct. 2022.
• Jean-Marc Menaud , La transition énergétique passe aussi par la formation et la recherche, Le Cortex Oct. 2022.

11.3.2 Education

Thomas Ledoux is co-leader of the Ecolog initiative. This national project is the result of the association between the Institut du Numérique Responsable (INR) and the IT sector in Nantes, which includes the University of Nantes, IMT Atlantique and Centrale Nantes. Its ambition is to create a body of training courses available in open source and dedicated to green computing.

11.3.3 Interventions

Thomas Ledoux took part in three round tables related to digital sustainability/green computing: ”Before Nantes Digital Week 2022” organized by Nantes Métropole (Sept. 15, 2022); Technoférence ”Le numérique face à ses responsabilités” organized by Pôle de compétitivité Images & Réseaux (Oct. 06, 2022); conference GreenTech Forum 2022 organized by Planet Tech'Care - Numeum (Dec 1st, 2022).

International journals

• 9 articleS.Shaashwat Agrawal, S.Sagnik Sarkar, O.Ons Aouedi, G.Gokul Yenduri, K.Kandaraj Piamrat, M.Mamoun Alazab, S.Sweta Bhattacharya, P. K.Praveen Kumar Reddy Maddikunta and T. R.Thippa Reddy Gadekallu. Federated Learning for intrusion detection system: Concepts, challenges and future directions.Computer CommunicationsSeptember 2022, 1-19
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• 10 articleE.Ehsan Ahvar, A.-C.Anne-Cécile Orgerie and A.Adrien Lebre. Estimating Energy Consumption of Cloud, Fog and Edge Computing Infrastructures.IEEE Transactions on Sustainable Computing72April 2022, 277-288
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• 11 articleA.Abdelghani Alidra, H.Hugo Bruneliere and T.Thomas Ledoux. A feature-based survey of Fog modeling languages.Future Generation Computer Systems138January 2023, 104-119
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• 12 articleO.Ons Aouedi, K.Kandaraj Piamrat and D.Dhruvjyoti Bagadthey. Handling partially labeled network data: a semi-supervised approach using stacked sparse autoencoder.Computer Networks207April 2022, 1-12
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• 13 articleO.Ons Aouedi, K.Kandaraj Piamrat, G.Guillaume Muller and K.Kamal Singh. Federated Semi-Supervised Learning for Attack Detection in Industrial Internet of Things.IEEE Transactions on Industrial InformaticsMarch 2022, 1-10
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• 14 articleO.Ons Aouedi, K.Kandaraj Piamrat and B.Benoît Parrein. Ensemble-based Deep Learning model for network traffic classification.IEEE Transactions on Network and Service ManagementJuly 2022, 1-12
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• 15 articleO.Ons Aouedi, K.Kandaraj Piamrat and B.Benoît Parrein. Intelligent Traffic Management in Next-Generation Networks.Future internetJanuary 2022, 1-35
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• 16 articleO.Ons Aouedi, A.Alessio Sacco, K.Kandaraj Piamrat and G.Guido Marchetto. Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions.IEEE Journal of Biomedical and Health InformaticsJune 2022, 1-14
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• 17 articleM.Maximilien Charlier, R.-A.Remous-Aris Koutsiamanis and B.Bruno Quoitin. Scheduling UWB Ranging and Backbone Communications in a Pure Wireless Indoor Positioning System.IoT31March 2022, 219 - 258
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• 18 articleR.-A.Ronan-Alexandre Cherrueau, M.Marie Delavergne, A.Alexandre van Kempen, A.Adrien Lebre, D.Dimitri Pertin, J.Javier Rojas Balderrama, A.Anthony Simonet and M.Matthieu Simonin. EnosLib: A Library for Experiment-Driven Research in Distributed Computing.IEEE Transactions on Parallel and Distributed Systems336June 2022, 1464-1477
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• 19 articleW.Wilmer Garzón, L. D.Luis Daniel Benavides Navarro, A.Alban Gaignard, R.Richard Redon and M.Mario Südholt. A taxonomy of tools and approaches for distributed genomic analyses.Informatics in Medicine Unlocked322022, 1-17
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• 20 articleP.Praveen Kumar Reddy Maddikunta, Q.-V.Quoc-Viet Pham, D. C.Dinh Cong Nguyen, T.Thien Huynh-The, O.Ons Aouedi, G.Gokul Yenduri, S.Sweta Bhattacharya and T.Thippa Reddy Gadekallu. Incentive techniques for the Internet of Things: A survey.Journal of Network and Computer Applications (JNCA)July 2022, 1-25
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• 21 articleS.Sirine Sayadi, V.Venceslas Douillard, N.Nicolas Vince, M.Mario Südholt and P.-A.Pierre-Antoine Gourraud. Distributing human leukocyte antigen HLA database in histocompatibility: a shift in HLA data governance.Exploration of Immunology2November 2022, 749–759
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• 22 articleZ. H.Zinah Hussein Toman, L.Lazhar Hamel, S. H.Sarah Hussein Toman and M.Mohamed Graiet. Correct-by-Construction Approach for Formal Verification of IoT Architecture.Procedia Computer Science2072022, 2598-2609
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International peer-reviewed conferences

• 23 inproceedingsO.Ons Aouedi, K.Kandaraj Piamrat, G.Guillaume Muller and K.Kamal Singh. Intrusion detection for Softwarized Networks with Semi-supervised Federated Learning.ICC 2022 - IEEE International Conference on CommunicationsICC 2022 - IEEE International Conference on CommunicationsSeoul, South KoreaIEEEJune 2022
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• 24 inproceedingsE.Emile Cadorel, H.Hélène Coullon and J.-M.Jean-Marc Menaud. Handling heterogeneous workflows in the Cloud while enhancing optimizations and performance.CLOUD 2022 - IEEE 15th International Conference on Cloud ComputingBarcelona, SpainIEEEJuly 2022, 49-58
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• 25 inproceedingsM.Marie Delavergne, G. J.Geo Johns Antony and A.Adrien Lebre. Cheops, a Service to Blow Away Cloud Applications to the Edge.Lecture Notes in Computer ScienceICSOC 2022 - 20th International Conference on Service-Oriented ComputingLNCS-13740Service-Oriented ComputingSevilla, SpainSpringer Nature SwitzerlandNovember 2022, 530-539
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• 26 inproceedingsA.Adrien Lebre, B.Brice Nédelec and A.Alexandre van Kempen. AS-cast: Lock Down the Traffic of Decentralized Content Indexing at the Edge.Meng, W., Lu, R., Min, G., Vaidya, J. (eds) Algorithms and Architectures for Parallel Processing, Lecture Notes in Computer Science book series (LNCS)ICA3PP 2022 - 22nd International Conference on Algorithms and Architectures for Parallel Processing13777Copenhagen, Denmark2022, 433-454
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• 27 inproceedingsS.Simon Robillard and H.Hélène Coullon. SMT-Based Planning Synthesis for Distributed System Reconfigurations.FASE 2022 : 25th International Conference on Fundamental Approaches to Software Engineering13241Lecture Notes in Computer ScienceMunich, Germany2022, 268-287
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• 28 inproceedingsN.Nihed Yousfi, M.Mourad Kmimech, I.Imed Abbassi, H.Hedi Hamdi and M.Mohamed Graiet. ITS Traffic Violation Regulation Based on Blockchain Smart Contracts.Communications in Computer and Information ScienceICCCI 2022 - 14th International Conference on Computational Collective Intelligence1653Advances in Computational Collective IntelligenceHammamet, TunisiaSpringer International PublishingSeptember 2022, 459-471
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Conferences without proceedings

• 29 inproceedingsD.Divi De Lacour, M.Marc Lacoste, M.Mario Südholt and J.Jacques Traoré. Towards Scalable Resilient Federated Learning: A Fully Decentralised Approach.PeRConAI 2023 - 2nd IEEE Workshop on Pervasive and Resource-constrained Artificial IntelligenceAtlanta (GA), United StatesIEEE2023
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Doctoral dissertations and habilitation theses

• 30 thesisO.Ons Aouedi. Machine Learning-Enabled Network Traffic Analysis.Nantes UniversitéDecember 2022
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• 31 thesisS.Sirine Sayadi. Distributed architectures and secure software containers for multi-site medical cooperation.Ecole nationale supérieure Mines-Télécom AtlantiqueDecember 2022
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Reports & preprints

• 32 reportM.Marie Delavergne, G. J.Geo Johns Antony and A.Adrien Lebre. Cheops, a service to blow away Cloud applications to the Edge.RR-9486Inria Rennes - Bretagne AtlantiqueSeptember 2022, 1-16
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Other scientific publications

• 33 inproceedingsO.Ons Aouedi, K.Kandaraj Piamrat, G.Guillaume Muller and K.Kamal Singh. Détection d’intrusions fédérée et semi-supervisée pour l’IoT.Plate-Forme Intelligence Artificielle PFIA 2022Saint-Etienne, FranceJune 2022
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