Alignment with Inria's scientific strategy

Data-intensive applications exhibit several common requirements with respect to the need for data storage and I/O processing. We focus on some core challenges related to data management, resulted from these requirements. Our choice is perfectly in line with Inria's strategic objectives  30, which acknowledges HPC-Big Data convergence as one of the Top 3 priorities of our institute.

In the area of cloud data processing, a significant milestone is the emergence of the Map-Reduce  24 parallel programming paradigm. It is currently used on most cloud platforms, following the trend set up by Amazon  18. At the core of Map-Reduce frameworks lies the storage system, a key component which must meet a series of specific requirements that are not fully met yet by existing solutions: the ability to provide efficient fine-grain access to the files, while sustaining a high throughput in spite of heavy access concurrency; the need to provide a high resilience to failures; the need to take energy-efficiency issues into account.

More recently, it becomes clear that data-intensive processing needs to go beyond the frontiers of a single type of infrastructure. Cloud workflows evolve from single-datacenter deployment towards multiple-datacenter deployments, and further from cloud deployments towards distributed, edge-based infrastructures. In this perspective, extra challenges arise, related to the efficiency of metadata management and data processing.

Challenges and goals related to data-intensive HPC applications

Key research fields such as climate modeling, solid Earth sciences or astrophysics rely on very large-scale simulations running on post-Petascale supercomputers. Such applications exhibit requirements clearly identified by international panels of experts like IESP  31, EESI  28, ETP4HPC  34. A jump of one order of magnitude in the size of numerical simulations is required to address some of the fundamental questions in several communities in this context. In particular, the lack of data-intensive infrastructures and methodologies to analyze the huge results of such simulations is a major limiting factor.

The challenge we have been addressing is to find new ways to store, visualize and analyze massive outputs of data during and after the simulations. Our main initial goal was to do it without impacting the overall performance, avoiding the jitter generated by I/O interference as much as possible. During the recent years we focused specifically on in situ processing approaches and we explored approaches to model and predict I/O phase occurrences and to reduce intra-application and cross-application I/O interference. More recently, we started to investigate approaches exploring how to process data for complex workflows where simulations run on HPC systems are coupled with data analytics codes typically running on clouds. A particularly challenging case is that of such applications running on hybrid HPC/cloud/edge infrastructures.

Platforms and Methodology

The highly experimental nature of our research validation methodology should be emphasized. To validate our proposed algorithms and architectures, we build software prototypes, then validate them at a large scale on real testbeds and experimental platforms.

We strongly rely on the Grid'5000 platform. Moreover, thanks to our projects and partnerships, we have access to reference software and physical infrastructures. In the cloud area, we use the Microsoft Azure, Amazon cloud platforms and the Chameleon  22 experimental cloud testbed. In the post-Petascale HPC area, we are running our experiments on systems including some top-ranked supercomputers, such as Titan, Jaguar, Kraken or Aurora. This provides us with excellent opportunities to validate our results on advanced realistic platforms.

Collaboration strategy

Our collaboration portfolio includes international teams that are active in the areas of data management for edge, clouds and HPC systems, both in Academia and Industry.

Our academic collaborating partners include Argonne National Lab, University of Illinois at Urbana-Champaign, Universidad Politécnica de Madrid, Barcelona Supercomputing Center. In industry, through bilateral or multilateral projects, we have been collaborating with Microsoft, IBM, Total, Huawei, ATOS/BULL.

Moreover, the consortiums of our collaborative projects include application partners in multiple application domains from the areas of area of climate modeling, precision agriculture, earth sciences, precision agriculture, smart cities, botanical science. This multidisciplinary approach is an additional asset, which enables us to take into account application requirements in the early design phase of our proposed approaches to data storage and processing, and to validate those solutions with real applications and real users.

3.1.1 Towards common storage abstractions for Extreme Computing and Big Data analytics

Storage is a plausible pathway to convergence. In this context, we plan to focus on the needs of concurrent Big Data applications that require high-performance storage, as well as transaction support. Although blobs (binary large objects) are an increasingly popular storage model for such applications, state-of-the-art blob storage systems offer no transaction semantics. This demands users to coordinate data access carefully in order to avoid race conditions, inconsistent writes, overwrites and other problems that cause erratic behavior.

There is a gap between existing storage solutions and application requirements, which limits the design of transaction-oriented applications. In this context, one idea on which we focus our efforts is exploring how blob storage systems could provide built-in, multiblob transactions, while retaining sequential consistency and high throughput under heavy access concurrency. From a more general perspective, we investigate how object-based storage could serve as a common storage abstraction for both HPC simulations (traditionally running on supercomputers) and for Big Data analytics codes (traditionally running on clouds). HPC/cloud storage convergence can be seen as as a means to make a step forward towards the general HPC-Big Data convergence.

The early principles of this research direction have already raised interest from our partners at ANL (Rob Ross) and UPM (María Pérez) for potential collaborations. Our paper on the Týr transactional blob storage system, selected as a Best Student Paper Award Finalist at the SC16 conference 9, illustrates this direction.

3.1.2 Towards unified data processing techniques for Extreme Computing and Big Data applications

In the high-performance computing area (HPC), the need to get fast and relevant insights from massive amounts of data generated by extreme-scale computations led to the emergence of in situ processing. It allows data to be visualized and processed in real-time on the supercomputer generating them, in an interactive way, as they are produced, as opposed to the traditional approach consisting of transferring data off-site after the end of the computation, for offline analysis. As such processing runs on the same resources executing the simulation, there is a risk to "disturb" the simulation if it consumes too many resources.

Consequently, an alternative approach was proposed (in transit processing), as a means to reduce this impact: data are transferred to some temporary processing resources (with high memory and processing capacities). After this real-time processing, they are moved to persistent storage.

In the Big Data area, the search for real-time, fast analysis was materialized through a different approach: stream-based processing. Such an approach is based on a different abstraction for data, that are seen as a dynamic flow of items to be processed. Stream-based processing and in situ/in transit processing have been developed separately and implemented in different tools in the BDA (Big Data Applications) and HPC (High-Performance Computing) areas respectively.

A major challenge from the perspective of the HPC-BDA convergence is their joint use in a unified data processing architecture. This is one of the future research challenges that we plan to address in the near future, by combining ongoing approaches currently active in our team: Damaris and KerA. We started preliminary work within the "Frameworks" work package of the HPC-Big Data IPL. Further exploring this convergence is a core direction of our involvement in collaborative European projects: PRACE 6IP (2019-2021); and ACROSS, to start in 2021.

3.2.1 Stream-oriented, Big Data processing on clouds

The state-of-the-art Hadoop Map-Reduce framework cannot deal with stream data applications, as it requires the data to be initially stored in a distributed file system in order to process them. To better cope with the above-mentioned requirements, several systems have been introduced for stream data processing such as Flink  19, Spark  20, Storm  33, and Google MillWheel  17. These systems keep active data in memory to decrease latency, and preserve scalability by using partitioning data over the various distributed nodes or dividing the streams into a set of deterministic batch computations to be distributedly processed.

However, they are designed to work in dedicated environments and they do not consider the performance variability (i.e., network, I/O, etc.) caused by resource contention in the cloud. This variability may in turn cause high and unpredictable latency when output streams are transmitted to further analysis. Moreover, they overlook the dynamic nature of data streams and the volatility in their computation requirements. Finally, they still address failures in a best-effort manner. Our objective is to investigate new approaches for reliable, stream Big Data processing on clouds.

3.2.2 Efficient edge, cloud and hybrid edge/cloud data processing

Today, we are approaching an important technological milestone: applications are generating huge amounts of data and are demanding low-latency responses to their requests. Mobile computing and Internet of Things (IoT) applications are good illustrations of such scenarios. Using only cloud computing for such scenarios is challenging. Firstly, cloud resources are most of the time accessed through Internet, hence, data are sent across high-latency wide area networks, which may degrade the performance of applications. Secondly, it may be impossible to send data to the cloud due to data regulations, national security laws or simply because an Internet connection is not available. Finally, data transmission costs (e.g., cloud provider fees, carrier costs) could make a business solution impractical.

Edge computing is a new paradigm which aims to address some of these issues. The key idea is to leverage computing and storage resources at the "edge" of the network, i.e., on processing units located close to the data sources. This allows applications to outsource task execution from the main (cloud) processing data centers to the edge. The development of edge computing was accelerated by the recent emergence of stream processing, a new model for handling continuous flows of data in real-time, as opposed to batch processing, which typically processes bounded datasets offline.

However, edge computing is not a silver bullet: issues like node volatility, limited processing power, high latency between nodes, fault tolerance and data degradation may impact applications depending on the characteristics of the infrastructure.

Some relevant research questions are: How much can one improve (or degrade) the performance of an application by performing data processing closer to the data sources rather than performing it in the cloud? How to progress towards a seamless scheduling and execution of a data analytics workflow and break the limitations of the current dual approaches? Those dual approached used to be applied in preliminary efforts in this area, which rely on manual and empirical deployment of the corresponding dataflow operator graphs, using separate analytics engines for centralized clouds and for edge systems respectively.

Our objective is to try to precisely answer such questions. We are interested in understanding the conditions that enable the usage of edge or cloud computing to reduce the time to results and the associated costs. While some state-of-the-art approaches advocate either "100 % cloud" or "100 % edge" solutions, the relative efficiency of a method over the other may vary. Intuitively, it depends on many parameters, including network technology, hardware characteristics, volume of data, amount of computing power, processing framework configuration and application requirements, to cite a few. We plan to study their impact on the overall application performance.

Social impact.

One of our target applications is Early Earthquake Warning. We propose a solution that enables earthquakes classification with an outstandingly perfect accuracy. By enabling accurate identification of strong earthquakes, it becomes possible to trigger adequate measures and save lifes. For this reason, our work was distinguished with an Outstanding Paper Award — Special Track for Social Impact at AAAI-20, an A* conference in the area of Artificial Intelligence. This result has been highlighted by the Le Monde journal in its edition of December 28, 2020, in a section entitled: Ces découvertes scientifiques que le Covid-19 a masquées en 2020.

Environmental impact.

As presented in Section 4, we have started a collaboration with CybeleTech that we plan to materialize in the framework of the EUPEX EuroHPC project. CybeleTech is a French company specialized in precision agriculture. Within the framework of our collaboration, we propose to focus our efforts on a scale-oriented data management mechanism targeting two CybeleTech use-cases. They address irrigation scheduling for orchards and optimal harvest date for corn, and their models require the acquisition of large volumes of remote data. The results of our collaboration will have concrete applications as they will improve the accuracy of plant growth models and improve decision making for precision agriculture, which directly aims to contribute to sustainable development.

7.1.1 Damaris

• Keywords: Visualization, I/O, HPC, Exascale, High performance computing
• Scientific Description:

  Damaris is a middleware for I/O and data management targeting large-scale, MPI-based HPC simulations. It initially proposed to dedicate cores for asynchronous I/O in multicore nodes of recent HPC platforms, with an emphasis on ease of integration in existing simulations, efficient resource usage (with the use of shared memory) and simplicity of extension through plug-ins. Over the years, Damaris has evolved into a more elaborate system, providing the possibility to use dedicated cores or dedicated nodes to in situ data processing and visualization. It proposes a seamless connection to the VisIt visualization framework to enable in situ visualization with minimum impact on run time. Damaris provides an extremely simple API and can be easily integrated into the existing large-scale simulations.

  Damaris was at the core of the PhD thesis of Matthieu Dorier, who received an Accessit to the Gilles Kahn Ph.D. Thesis Award of the SIF and the Academy of Science in 2015. Developed in the framework of our collaboration with the JLESC – Joint Laboratory for Extreme-Scale Computing, Damaris was the first software resulted from this joint lab validated in 2011 for integration to the Blue Waters supercomputer project. It scaled up to 16,000 cores on Oak Ridge’s leadership supercomputer Titan (first in the Top500 supercomputer list in 2013) before being validated on other top supercomputers. Active development is currently continuing within the KerData team at Inria, where it is at the center of several collaborations with industry as well as with national and international academic partners.

• Functional Description: Damaris is a middleware for data management and in-situ visualization targeting large-scale HPC simulations: - In situ data analysis by some dedicated cores/nodes of the simulation platform - Asynchronous and fast data transfer from HPC simulations to Damaris - Semantic-aware dataset processing through Damaris plug-ins - Writing aggregated data (by hdf5 format) or visualizating them either by VisIt or ParaView
• URL: https://project.inria.fr/damaris/
• Authors: Matthieu Dorier, Gabriel Antoniu, Orçun Yildiz, Luc Bougé, Hadi Salimi, Joshua Charles Bowden
• Contact: Gabriel Antoniu
• Participants: Gabriel Antoniu, Lokman Rahmani, Luc Bougé, Matthieu Dorier, Orçun Yildiz, Hadi Salimi, Joshua Charles Bowden
• Partner: ENS Rennes

7.1.2 KerA

• Name: KerAnalytics
• Keywords: Big data, Distributed Storage Systems, Streaming, Real time
• Scientific Description:

  Current state-of-the-art Big Data analytics architectures are built on top of a three layer stack: data streams are first acquired by the ingestion layer (e.g., Kafka) and then they flow through the processing layer (e.g., Flink) which relies on the storage layer (e.g., HDFS) for storing aggregated data or for archiving streams for later processing. Unfortunately, in spite of potential benefits brought by specialized layers (e.g., simplified implementation), moving large quantities of data through specialized layers is not efficient: instead, data should be acquired, processed and stored while minimizing the number of copies.

  KerA was developed on top of RAMCloud, a low-latency key-value distributed system.

• Functional Description:

  KerA is a platform for continuous data ingestion and storage. This data can be of various types (videos, images, texts, structured data, etc.), and can be generated by multiple sources: sensors, websites, complex software (producers). The data thus acquired are stored permanently and made accessible to processing software (consumers), useful for extracting useful information or calculating statistics.

  KerA focuses on reducing the latency between reading and publishing the data: the results are close to being obtained in real time. The platform is distributed: the available hardware resources are not on the same machine. This feature ensures scalability when the number of producers/consumers is increased and facilitates the replication of data on several servers in order to tolerate possible failures of some of them.

• URL: https://kerdata.gitlabpages.inria.fr/Kerdata-Codes/kera-website/
• Publications: hal-01773799, tel-02127065, hal-01532070
• Authors: Ovidiu-Cristian Marcu, Gabriel Antoniu, Alexandru Costan, Thomas Bouvier
• Contacts: Thomas Bouvier, Gabriel Antoniu, Alexandru Costan

7.1.3 Pufferscale

• Keywords: Distributed Storage Systems, Elasticity
• Scientific Description: Pufferscale is a rescaling service developed in the context of the Mochi project. It is a library designed to manage data units (buckets) of behalf of microservices during rescaling operations (adding or removing nodes). It tracks the position of each bucket in the system, and during rescaling operations Pufferscale determines the new position of each bucket (moved out of removed nodes, added to newly added nodes to balance the load). It gives the transfer instructions to the microservices.
• Functional Description: Pufferscale is a rescaling service designed to manage data units (buckets) of behalf of microservices during rescaling operations (adding or removing nodes). It was design in the context of the Mochi project.
• Release Contributions: First release of Pufferscale.
• Contacts: Nathanaël Cheriere, Gabriel Antoniu

7.1.4 E2Clab

• Name: Edge-to-Cloud lab
• Keywords: Distributed Applications, Distributed systems, Computing Continuum, Large scale, Experimentation, Evaluation, Reproducibility
• Functional Description:

  E2Clab allows researchers to reproduce in a representative way the application behavior in a controlled environment for extensive experiments and therefore to understand end-to-end performance of applications by correlating results to the parameter settings. E2Clab provides a rigorous approach to answering questions like: How to identify infrastructure bottlenecks? Which system parameters and infrastructure configurations impact on performance and how?

  High-level features provided by E2Clab: - Reproducible Experiments: Supports repeatability, replicability and reproducibility. - Mapping: Application parts (Edge, Fog and Cloud/HPC) and physical testbed. - Variation & Scaling: Experiment variation and transparent scaling of scenarios. - Network Emulation: Edge-to-Cloud communication constraints. - Experiment Management: Deployment, execution and monitoring (e.g. on Grid’5000).

• URL: https://kerdata.gitlabpages.inria.fr/Kerdata-Codes/e2clab/
• Contacts: Daniel Rosendo, Gabriel Antoniu, Alexandru Costan, Matthieu Simonin

8.1.1 Unifying HPC and Big Data stacks: Towards application-defined blobs at the storage layer

Participants: Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Pierre Matri, formerly a PhD intern in the team, and now at Argonne National Laboratory, USA.

HPC and Big Data stacks evolved separately. The storage layer offers opportunities for convergence, as the challenges associated with HPC and Big Data storage are similar: trading versatility for performance. This motivates a global move towards dropping file-based, POSIX-IO compliance systems.

However, on HPC platforms this is made difficult by the centralized storage architecture using file-based storage. In 13 we advocate that the growing trend of equipping HPC compute nodes with local storage redistributes the cards by enabling object storage to be deployed alongside the application on the compute nodes. Such integration of application and storage not only allows fine-grained configuration of the storage system, but also improves application portability across platforms. In addition, the single-user nature of such application-specific storage obviates the need for resource-consuming storage features like permissions or file hierarchies offered by traditional file systems.

We propose and evaluate Blobs (Binary Large Objects) as an alternative to distributed file systems. We factually demonstrate that it offers drop-in compatibility with a variety of existing applications while improving storage throughput by up to 28%.

8.1.2 Memory and data-awareness in hybrid workflows

Participant: François Tessier.

Among the broad variety of challenges that arise from HPC and HPDA workloads, data movement is of paramount importance, especially on coming Exascale systems featuring multiple tiers of memory and storage. While the focus has, for years, been primarily on optimizing floating point operations, the importance of improving data handling on such architectures is now well understood. As optimization techniques can be applied at different stages (operating system, runtime system, programming environment, and so on), a middleware providing a uniform and consistent data-awareness becomes necessary.

We introduce a novel memory- and data-aware middleware  29 called Maestro, designed for data orchestration. In particular, we put the emphasis on the data abstraction layer and the API we have devised. We evaluate our approach with benchmarks and we discuss how Maestro can be of benefit for real-life use-cases such as numerical weather forecasting.

8.2.1 Exploring the Computing Continuum through repeatable, replicable and reproducible Edge-to-Cloud experiments

Participants: Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Pedro De Souza Bento Da Silva, formerly a post-doc student in the team, and now at Hasso Plattner Institute, Berlin, Germany.

Distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge devices to the HPC Cloud (aka the Computing Continuum, the Digital Continuum, or the Transcontinuum). Understanding end-to-end performance in such a complex continuum is challenging. This breaks down to reconciling many, typically contradicting application requirements and constraints with low-level infrastructure design choices. One important challenge is to accurately reproduce relevant behaviors of a given application workflow and representative settings of the physical infrastructure underlying this complex continuum.

In this work we introduce a rigorous methodology for such a process and validate it through E2Clab 16. It is the first platform to support the complete analysis cycle of an application on the Computing Continuum: (i) the configuration of the experimental environment, libraries and frameworks; (ii) the mapping between the application parts and machines on the Edge, Fog and Cloud; (iii) the deployment of the application on the infrastructure; (iv) the automated execution; and (v) the gathering of experiment metrics. We illustrate its usage with a real-life application deployed on the Grid'5000 testbed, showing that our framework allows one to understand and improve performance, by correlating it to the parameter settings, the resource usage and the specifics of the underlying infrastructure.

8.2.2 Analytical models for performance evaluation of stream processing

Participants: Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with José Aguilar Canepa, formerly a PhD intern in the team, and now at Instituto Politécnico Nacionál, Mexico.

A major challenge in deploying stream processing on Cloud and Edge infrastructure is finding the appropriate mapping between the application graph and the network graph. Operators need to be placed on machines in a way to enhance performance, i.e., reduce the total execution time. We are studying this challenge in the context of the SmartFastData associate team.

We have used a dynamic programming (DP) approach to address this mapping algorithm. The critical aspect of DP is to divide the problem into smaller sub-problems as independent as possible, that can be solved more easily than the original one, so that the sub-solutions can be combined to solve the complete problem. With this principle in mind, we designed an algorithm to solve the Graph Coloring Problem, a classical combinatorial optimization problem. Given a graph, the goal is to assign each of its vertices a label (color) so that no adjacent vertices share the same label. In our case, colors correspond to different machines (associated with one operator).

Our proposal uses random, efficient algorithms to partition the graph into smaller sub-graphs, color them using a classical genetic algorithm, and glue together to solve the complete graph. Experimental tests on benchmark graphs show promising results (a paper is currently underway to showcase these initial results).

We are now interested in testing its performance on large graphs derived from real-life scenarios. A case study is a network of thousands of servers used to distribute contents on the Internet. This scenario is a typical scheduling application of a graph coloring problem.

8.2.3 Modeling smart cities applications

Participants: Alexandru Costan, Gabriel Antoniu.

• Collaboration.
  This work has been carried out in close co-operation with Edgar Romo Montiel, formerly a PhD intern in the team, and now at Instituto Politécnico Nacionál, Mexico.

Some important components of smart cities are the connected vehicles. We have focused on modeling data flow from Vehicular Networks through phase-type distributions and Artificial Neural Networks to obtain a robust system to reserve and allocate resources needed to process the data collected from those vehicles. The ultimate goal is to distribute the data flow’s prediction in multiple antennas to estimate load work in a specific point based on the demand for resources in the neighborhood.

To this end, we have leveraged the SUMO simulator that generates vehicles’ traffic flow to feed the Neural Network and the Phase-type model. The first approach considered a single antenna, where the mathematical model was computing an upper-bound of the real traffic and the Neural Network was learning the actual traffic history. This single antenna model only considers its own data. Multiple antennas’ model considers the data from neighbor antennas to feed the Neural Network to predict the vehicles’ flow.

The precision of Neural Networks is beneficial for the prediction. However, the complexity of such a model increases so that updates or computing may require large Cloud capabilities. We leveraged Grid’5000 to compare performance between Cloud capabilities and Edge capabilities and estimate multiple antennas’ data flow.

The resulting flow prediction models are currently in submission to a journal in the field of Vehicular/Edge Networks. This collaboration of Inria and Instituto Politécnico Nacional, Mexico (IPN) is the starting point of a future papers’ collection for Edge/Cloud computing architecture in the area of Vehicular Networks.

8.2.4 Efficient InfiniBand access for stream ingestion systems

Participants: Thomas Bouvier, Alexandru Costan, Gabriel Antoniu.

Large-scale applications like Big Data analytics need efficient communication for their processing tasks. Fast networks like InfiniBand offer bandwidths of up to 200 Gb/s with latencies of less than two microseconds. While they are mainly used in high performance computing, some applications in the field of Big Data analytics are now starting to use them. In addition, some cloud providers are offering instances equipped with InfiniBand hardware. Yet, the support for such fast networks is still limited in current stream processing engines.

To this end we propose to enhance KerA, our flagship stream ingestion system, with support for InfiniBand access. The idea is to allow the distribution and the access to the stream partitions and their corresponding metadata through InfiniBand. We leveraged the Neutrino network library developed by our partners from the University of Dusseldorf in the context of the PHC PROCOPE FlexStream project.

This library provides comfortable and efficient access to InfiniBand hardware in Java as well as a poll-based multithreaded connection management. Neutrino supports InfiniBand message passing as well as remote direct memory access, is implemented using the Java Native Interface, and can be used with any Java Virtual Machine. It also provides access to native C structures via a specific proxy system, which in turn enables the developer to leverage the full functionality of InfiniBand hardware.

Our initial experiments showed that efficient access to InfiniBand hardware is possible while fully utilizing the available bandwidth and thus speeding-up the stream ingestion.

9.1.1 Inria International Labs

9.1.2 Inria associate team not involved in an IIL

9.1.3 Participation in other international programs

9.2.1 Visits to international teams

9.3.1 FP7 & H2020 Projects

9.3.2 Collaborations in European programs, except FP7 and H2020

9.3.3 Collaborations with major European organizations

Participants: Gabriel Antoniu, Alexandru Costan.

9.4.1 ANR

9.4.2 Other National Projects

10.1.1 Scientific events: organization

10.1.2 Scientific events: selection

10.1.3 Journal

10.1.4 Invited talks

• Gabriel Antoniu:
  • LIRIMA seminar, in May 2020. The Edge, the Cloud and the Supercomputer: Welcome to the Age of the Digital Continuum!. URL: https://videos-rennes.inria.fr/video/rJgGW8ATU.
  • BDVA Activity Group, November 2020. TCI: The Transcontinuum Initiative. URL: https://www.bdva.eu/node/1680.

10.1.5 Leadership within the scientific community

• Gabriel Antoniu:
  • TCI: Since 2020, co-leader of the Use-Case Analysis Working Group. TCI (The Transcontinuum Initiative) emerged as a collaborative initiative of ETP4HPC, BDVA, CLAIRE and other peer organizations, aiming to identify joint research challenges for leveraging the HPC-Cloud-Edge computing continuum and make recommendations to the European Commission about topics to be funded in upcoming calls for projects.
  • ETP4HPC: Since 2019, co-leader of the working group on Programming Environments and co-lead of two research clusters, contributing to the next Strategic Research Agenda of ETP4HPC (published in March 2020).
  • International lab management: Vice Executive Director of JLESC for Inria. JLESC is the Joint Inria-Illinois-ANL-BSC-JSC-RIKEN/AICS Laboratory for Extreme-Scale Computing. Within JLESC, he also serves as a Topic Leader for Data storage, I/O and in situ processing for Inria.
  • Team management: Head of the KerData Project-Team (INRIA-ENS Rennes-INSA Rennes).
  • International Associate Team management: Leader of the UNIFY Associate Team with Argonne National Lab (2019–2021).
  • Technology development project management: Coordinator of the Damaris 2 ADT project (2019–2022).
• Luc Bougé:
  • SIF: Co-Vice-President of the French Society for Informatics (Société informatique de France, SIF), in charge of the Teaching Department.
  • CoSSAF: Member of the Foundation Committee for the College of the French Scientific Societies (CoSSAF, Collège des sociétés savantes académiques de France) gathering more than 40 French societies from all domains.
• Alexandru Costan:
  • International Associate Team management: Leader of the SmartFastData Associate Team with Instituto Politécnico Nacional, Mexico City (2019–2021).
• François Tessier:
  • JLESC: Member of the organizing committee of the bi-annual workshops of the Joint Laboratory for Extreme Scale Computing.

10.1.6 Teaching

• Alexandru Costan
  • Bachelor: Software Engineering and Java Programming, 28 hours (lab sessions), L3, INSA Rennes.
  • Bachelor: Databases, 68 hours (lectures and lab sessions), L2, INSA Rennes, France.
  • Bachelor: Practical case studies, 24 hours (project), L3, INSA Rennes.
  • Master: Big Data Storage and Processing, 28h hours (lectures, lab sessions), M1, INSA Rennes.
  • Master: Algorithms for Big Data, 28 hours (lectures, lab sessions), M2, INSA Rennes.
  • Master: Big Data Project, 28 hours (project), M2, INSA Rennes.
• Gabriel Antoniu
  • Master (Engineering Degree, 5th year): Big Data, 24 hours (lectures), M2 level, ENSAI (École nationale supérieure de la statistique et de l'analyse de l'information), Bruz, France.
  • Master: Scalable Distributed Systems, 10 hours (lectures), M1 level, SDS Module, EIT ICT Labs Master School, France.
  • Master: Infrastructures for Big Data, 10 hours (lectures), M2 level, IBD Module, SIF Master Program, University of Rennes, France.
  • Master: Cloud Computing and Big Data, 14 hours (lectures), M2 level, Cloud Module, MIAGE Master Program, University of Rennes, France.
• Daniel Rosendo
  • Master: Miage BDDA, 24 hours (lab sessions), M2, ISTIC Rennes.
  • Bachelor: Algorithms for Big Data, 7.5 hours (lectures, lab sessions), INSA Rennes.

10.1.7 Supervision

• PhD in progress : Daniel Rosendo Enabling HPC-Big Data Convergence for Intelligent Extreme-Scale Analytics, thesis started in October 2019, co-advised by Gabriel Antoniu, Alexandru Costan and Patrick Valduriez (Inria).

10.1.8 Juries

• Luc Bougé: Member of the jury the CAPES of mathématiques, Informatics track. This national committee selects more than 1000 mathematics teachers per year for French secondary schools and high-schools.
• Gabriel Antoniu: Referee for 3 PhD juries for PhD defenses at Barcelona Supercomputing Center, École Normale Supérieure de Lyon, University of Montpellier. Member of a PhD jury at the University of Bordeaux.

10.2.1 Internal or external Inria responsibilities

• Gabriel Antoniu:
  • ETP4HPC and BDVA: Inria representative in the working groups of BDVA and ETP4HPC dedicated to HPC-Big Data convergence.
• Luc Bougé:
  • ANR: Scientific officer at the National Research Agency (ANR, Agence nationale de la recherche) in charge of data management.
  • PNRIA: Porgramme National de Recherche en Intelligence Artificielle (National Research Program for Artificial Intelligence). Member of the management team for the IA for Humanity governmental program launched in March 2018. Bertrand Braunschweig, Inria, and now Isabelle Herlin, are the scientific directors of the program.
• Alexandru Costan:
  • In charge of internships at the Computer Science Department of INSA Rennes.
  • In charge of the organization of the IRISA D1 Department Seminars.
  • In charge of the management of the KerData team access to Grid'5000.

10.2.2 Interventions

• Luc Bougé:
  • IH2EF: Participation to a series of trainings at the Institut des hautes études de l'éducation et de la formation (IH2EF, Institute for Higher Studies in Education and Training) about Artificial Intelligence. It targeted the persons in charge of pedagogy high-schools all over France.
  • ANR: Organization of various seminars and training about Large-Scale Data Management at National Research Agency (ANR, Agence nationale de la recherche). It focused on using Web API to access databases, for instance HAL and scanR, the search engine for French research and innovation.

International journals

• 12 articleNathanaelN. Cheriere, MatthieuM. Dorier and GabrielG. Antoniu. How fast can one resize a distributed file system?Journal of Parallel and Distributed Computing140June 2020, 80-98
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• 13 articlePierreP. Matri, YevhenY. Alforov, AlvaroA. Brandon, MaríaM. Pérez, AlexandruA. Costan, GabrielG. Antoniu, MichaelM. Kuhn, PhilipP. Carns and ThomasT. Ludwig. Mission Possible: Unify HPC and Big Data Stacks Towards Application-Defined Blobs at the Storage LayerFuture Generation Computer Systems109August 2020, 668-677
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International peer-reviewed conferences

• 14 inproceedingsNathanaelN. Cheriere, MatthieuM. Dorier, GabrielG. Antoniu, Stefan M.S. Wild, SvenS. Leyffer and RobertR. Ross. Pufferscale: Rescaling HPC Data Services for High Energy Physics ApplicationsCCGRID -2020 - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)Melbourne, AustraliaMay 2020, 182-191
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• 15 inproceedingsKevinK. Fauvel, DanielD. Balouek-Thomert, DiegoD. Melgar, PedroP. Silva, AnthonyA. Simonet, GabrielG. Antoniu, AlexandruA. Costan, VéroniqueV. Masson, ManishM. Parashar, IvanI. Rodero and AlexandreA. Termier. A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early WarningIn Proceedings of the 34th AAAI Conference on Artificial IntelligenceNew York, United StatesFebruary 2020, 403-411
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• 16 inproceedingsDanielD. Rosendo, PedroP. Silva, MatthieuM. Simonin, AlexandruA. Costan and GabrielG. Antoniu. E2Clab: Exploring the Computing Continuum through Repeatable, Replicable and Reproducible Edge-to-Cloud ExperimentsCluster 2020 - IEEE International Conference on Cluster ComputingKobe, JapanSeptember 2020, 1-11
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