Adwait Bauskar

Anderson Da Silva

Adrien Lebre

Clément Mommessin

Pierre Neyron

Yanik Ngoko

Yoann Ricordel

Denis Trystram

Alexandre Van Kempen

Investigating Placement Challenges in Edge Infrastructures through a Common Simulator

Keywords: Edge, Simulation, Scheduling algorithms, Data movements

Scheduling computational jobs with data-sets dependencies is an important challenge of edge computing infrastructures. Although several strategies have been proposed, they have been evaluated through ad-hoc simulator extensions that are, when available, usually not maintained. This is a critical problem because it prevents researchers to -easily-perform fair comparisons between different proposals. In this research report, we propose to address this limitation by presenting a simulation engine dedicated to the evaluation and comparison of scheduling and data movement policies for edge computing use-cases. Built upon the Batsim/SimGrid toolkit, our tool includes an injector that allows the simulator to replay a series of events captured in real infrastructures. It also includes a controller that supervises storage entities and data transfers during the simulation, and a plug-in system that allows researchers to add new models to cope with the diversity of edge computing devices. We demonstrate the relevance of such a simulation toolkit by studying two scheduling strategies with four data movement policies on top of a simulated version of the Qarnot Computing platform, a production edge infrastructure based on smart heaters. We chose this use-case as it illustrates the heterogeneity as well as the uncertainties of edge infrastructures. Our ultimate goal is to gather industry and academics around a common simulator so that efforts made by one group can be factorised by others.

Investigations sur les challenges de placement dans une infrastructure Edge à travers un simulateur commun

Résumé : Ordonnancer efficacement des travaux de calcul avec des dépendances de données est un des plus importants challenges des infrastructures de calcul edge. Bien que plusieurs stratégies ont été proposées, elles ont toutes été évaluées avec des extensions de simulateur ad-hoc qui sont, s'ils sont rendues disponibles, habituellement pas maintenus. C'est un problème critique parce que cela empêche les chercheurs de -facilement -conduire des évaluations équitables entre différentes stratégies proposées. Dans ce rapport de recherche, nous proposons d'adresser cette limitation en présentant un simulateur dédié à l'évaluation et la comparaison de politiques d'ordonnancement et de mouvement de données pour le cas d'usage du calcul edge. Construit au dessus de Batsim/SimGrid, notre outil inclut un injecteur qui permet au simulateur de rejouer une série d'évènements capturés au sein d'une infrastructure réelle. Ce outil inclut aussi un contrôleur qui supervise les entités de stockage et les transferts de données durant la simulation, et un système de plug-in qui permet aux chercheurs d'ajouter des nouveaux modèles pour faire face à la diversité des appareils de calcul edge.

Nous démontrons la pertinence d'un tel outil de simulation en étudiant deux stratégies d'ordonnancement avec quatre politiques de mouvements de données sur une version simulée de la plateforme de Qarnot Computing, une infrastructure de production edge basée sur des radiateurs intelligents. Nous avons choisi ce cas d'usage car il illustre l'hétérogénéité et l'incertitude d'une infrastructure de l'edge.

Notre but ultime est de rassemble les industriels et les académiques autour d'un simulateur commun afin que les efforts faits par un groupe puissent être factorisés par d'autres.

Mots-clés : Edge, Simulation, Algorithmes d'ordonnancement, Mouvements de données

Introduction

The proliferation of Internet of Things (IoT) applications [START_REF] Atzori | The internet of things: A survey[END_REF], as well as the advent of new technologies such as Mobile Edge computing [START_REF] Ahmed | A survey on mobile edge computing[END_REF], and Network Function Virtualisation [START_REF] Mijumbi | Network function virtualization: State-of-the-art and research challenges[END_REF] (NFV) have been accelerating the deployment of Cloud Computing-like capabilities at the edge of the Internet. Referred to as the Edge Computing [START_REF] Shi | Edge computing: Vision and challenges[END_REF] paradigm, the main objective is to perform on demand computations close to the place where the data are produced and analysed to mitigate data exchanges and to avoid too high latency penalties [START_REF] Zhang | The Cloud is Not Enough: Saving IoT from the Cloud[END_REF]. Among the open questions our community should address to favour the adoption of such infrastructures is the computation/data placement problem, i.e., where to transfer data-sets according to their sources and schedule computations to satisfy specific criteria. Although several works have been dealing with this question [START_REF] Salaht | Service Placement in Fog Computing Using Constraint Programming[END_REF][START_REF] Brogi | QoS-Aware Deployment of IoT Applications Through the Fog[END_REF][START_REF] Donassolo | Fog Based Framework for IoT Service Provisioning[END_REF][START_REF] Naas | iFogStor: An IoT Data Placement Strategy for Fog Infrastructure[END_REF][START_REF] Skarlat | Optimized IoT Service Placement in the Fog[END_REF][START_REF] Xia | Combining Hardware Nodes and Software Components Ordering-based Heuristics for Optimizing the Placement of Distributed IoT Applications in the Fog[END_REF][START_REF] Yousefpour | QoS-aware Dynamic Fog Service Provisioning[END_REF], it is difficult to understand how each proposal behaves in a different context and with respect to different objectives (scalability, reactivity, etc.). In addition to having been designed for specific use-cases, available solutions have been evaluated either using ad hoc simulators or through limited in-vivo (i.e., real-world) experiments. These methods are not accurate and not representative enough to, first, ensure their correctness on real platforms and, second, perform fair comparisons between them.

Similarly to what has been proposed for the Cloud Computing paradigm [START_REF] Lebre | Putting the next 500 vm placement algorithms to the acid test: The infrastructure provider viewpoint[END_REF], we claim that a dedicated simulator toolkit to help researchers investigate Edge scheduling strategies should be released soon. Indeed, we claim that using placement simulators for Cloud Computing is not appropriate to study Edge challenges. Besides resource heterogeneity, network specifics (latency, throughput), and workloads, Edge Computing infrastructures differ from Cloud Computing platforms because of the uncertainties: connectivity between resources is intermittent, storage/computation resources are more heterogeneous and can join or leave the infrastructure at any time, for an unpredictable duration. In other words, a part of the infrastructure can be isolated or unavailable for minutes/hours preventing accessing some data-sets or assigning new computations.

In this article, we present several extensions we implemented on top of the Batsim/SimGrid toolkit [START_REF] Casanova | Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms[END_REF][START_REF] Dutot | Batsim: a Realistic Language-Independent Resources and Jobs Management Systems Simulator[END_REF] to favour fair evaluations and comparisons between various scheduling and data placement strategies for Edge infrastructures. In particular, we developed an external module to allow injecting in the simulation any type of unforeseen events that could occur (e.g., a machine became unavailable at time t). We also implemented a Storage Controller to supervise all transfers of data-sets within the simulated platform. We chose to rely on Batsim/SimGrid instead of any other available Edge simulators [START_REF] Sonmez | Edgecloudsim: An environment for performance evaluation of edge computing systems[END_REF][START_REF] Zeng | IOTSim: a Cloud based Simulator for Analysing IoT Applications[END_REF] for the following reasons:

• Batsim has been especially designed to test and compare resource management policies in distributed infrastructures. In other words, the design of Batsim enforces researchers to use the same abstractions and, thus, favours straightforward comparisons of different strategies, even if they have been implemented by different research groups;

• Batsim promotes separation of concerns and enables the decoupling between the core simulator and the scheduler. Moreover, Batsim provides APIs in different languages (including Python, C++ and Rust) that makes the development of a scheduling strategy accessible for a large number of researchers;

• The accuracy of the internal models (computation and network) of SimGrid has been already validated [START_REF] Degomme | Simulating MPI applications: the SMPI approach[END_REF][START_REF] Velho | On the Validity of Flow-level TCP Network Models for Grid and Cloud Simulations[END_REF] and extensively used [2];

• SimGrid provides a plug-in mechanism, which is of particular interest to deal with the diversity of Edge devices: it lets researchers add new models of specific Edge facilities without requiring intrusive modifications into the simulation engine.

By extending Batsim to the Edge paradigm, we target a tool that will enable researchers/engineers to re-evaluate major state-of-the-art load balancing strategies. In particular, we think about scheduling strategies that have been proposed in desktop computing platforms, volunteer computing and computational grids [START_REF] Allcock | Data management and transfer in high-performance computational grid environments[END_REF][START_REF] Anderson | Boinc: A system for public-resource computing and storage[END_REF] as these infrastructures have several characteristics in common with Edge platforms.

To demonstrate the relevance of our proposal, we discuss several simulations we performed for the Qarnot Computing [START_REF]Qarnot computing[END_REF] use-case. The infrastructure of Qarnot Computing is composed of 3,000 disk-less machines (smart heaters) distributed across several sites in France. Each computing resource can be used remotely as traditional Cloud Computing capabilities or locally to satisfy data processing requirements of IoT devices deployed in the vicinity of the computing resource. As such, the Qarnot platform is a good example of Edge infrastructure, with computing units and mixed local/global jobs with data-sets dependencies.

The strategies presented in this article are simple. They aim to illustrate what can be done without important efforts. More advanced strategies can be analysed in the same manner. We are, for instance, investigating more advanced strategies that consider pulling data-sets from other Edge resources rather than from the centralised storage system of the Qarnot Computing infrastructure.

The rest of the report is structured as follows. Section 2 gives an overview of the Batsim/SimGrid toolkit and the extensions we implemented. Section 3 presents the Qarnot Computing use-case and Section 4 describes how we simulated this case study. Section 5 discusses a first analysis of different scheduling strategies for the Qarnot platform. Section 6 presents the related work. Conclusion and future works are given in Section 7.

A Dedicated Simulator for Edge Platforms

Our proposal relies on extensions developed in the Batsim/SimGrid toolkit [START_REF] Dutot | Batsim: a Realistic Language-Independent Resources and Jobs Management Systems Simulator[END_REF]. Released in 2016, Batsim delivers a high-level API on top of SimGrid [START_REF] Casanova | Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms[END_REF] to ease the development and simulation of resource management algorithms. Thus, our proposal relies on tools already validated by our community.

Operational Components

We discuss in this section the role of the different components, namely SimGrid, Batsim, the decision process, and their interactions.

SimGrid

SimGrid [START_REF] Casanova | Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms[END_REF] is a state-of-the-art simulation toolkit that enables the simulation of distributed systems. SimGrid's relevance in terms of performance and validity has been backed-up by many publications [2]. In addition to providing the program to be evaluated, performing simulations with SimGrid requires writing a platform specification and interfacing the program to simulate. SimGrid enables the description of complex platforms, such as hierarchical infrastructures composed of many interconnected devices with possibly highly heterogeneous profiles, such as the edge ones.

Batsim and the decision process

Batsim [START_REF] Dutot | Batsim: a Realistic Language-Independent Resources and Jobs Management Systems Simulator[END_REF] is a simulator engine built on top of SimGrid. It proposes a specialised API to help researchers design and analyse jobs and I/O scheduling systems. Such systems are for Inria instance Batch Schedulers a.k.a., Resource and Jobs Management Systems, in charge of managing resources in large-scale computing centres. Batsim allows researchers to simulate the behaviour of a computational platform in which workloads are executed according to the rules of a decision process. It uses a simple event-based communication interface: as soon as an event occurs, Batsim stops the simulation and reports what happened to the decision process.

The decision process embeds the actual scheduling code to be evaluated. In other words, to simulate a given scheduling algorithm, an experimenter has to implement this decision process. Comparing different algorithms consists in switching between different decision processes, which is easy in Batsim. Internally, the decision process (i) reacts to the simulation events received from Batsim, (ii) takes decisions according to the given scheduling algorithm, and (iii) drives the simulated platform by sending back its decisions to Batsim. Batsim and the decision process communicate via a language-agnostic synchronous protocol. In this work, we used Batsim's Python API to implement the decision process. For more details on Batsim and SimGrid mechanisms, we invite the reader to refer to Chapter 4 of Poquet's manuscript [START_REF] Poquet | Simulation approach for resource management[END_REF].

Extensions

To ease the study of scheduling and data placement strategies for Edge platforms, we have been working on a couple of extensions for Batsim. We present in this section those already available, namely the external events injector and the storage controller. Modifications made in Batsim and its Python API for this work are integrated in the main branch of the repositories1 . Besides, we present the plug-in mechanism of SimGrid that researchers can leverage to provide models of particular Edge devices.

External events injector

To simulate the execution of an Edge infrastructure, which is by essence subject to very frequent unexpected or unpredictable changes, our simulator offers the opportunity to inject external events on demand. Those events impact the behaviour of the platform during the execution and thus the choices of the scheduling strategy. For example, one would be interested in studying the behaviour and resilience of a scheduling policy when a range of machines becomes unexpectedly unavailable for a period of time, due to a failure or action (e.g., from a local user) occurring at the edge.

An external event is represented as a JSON object composed of two mandatory fields: a timestamp that indicates when the event occurs, and the type of the event. Depending on the type of event, other fields can complement the event description, such as for instance the name of the unavailable resource, the new value of an environment parameter such as the network bandwidth, or anything of interest to the decision process.

Similarly to the workload submissions, external events are replayed thanks to the injector process of Batsim. For each external event file given as input to Batsim, with one aforementioned JSON object per line in the file, an external events submitter is created during the initialisation of Batsim. Each submitter parses the list of external events from the input file and iterates over the list to submit the external events to the main process of Batsim at the right simulation times. Then, the external event is processed by Batsim, the state of the platform is updated and the occurring external event is forwarded to the decision process.

This event injection mechanism is generic by concept: users can define their own types of event and associated fields, which will simply be forwarded to the decision process without requiring any modification in the code of Batsim.

Storage Controller

The Storage Controller is a module included in Batsim's Python API to ease the management of storage entities and data-sets, and supervise data transfers during the simulation.

At the beginning of the simulation, the Storage Controller retrieves the list of storage resources of the platform and initialises one storage object per resource. These created storages are empty by default, but they can be filled on demand by the decision process by providing a single or a list of data-sets to be added to a storage. A data-set is represented by two fields, id and size, denoting the unique identifier of the data-set and its size in bytes. The Storage Controller exposes to the decision process an API to add data-sets to storages during the initialisation of the simulation. It also exposes functions to ask, for example, for the copy of a data-set from one storage to another, or to retrieve the list of all storages holding a copy of a given data-set during the simulation.

When a data-set should be copied from one storage to another, the Storage Controller creates a specific Batsim job for data transfers describing that a given amount of bytes should be transferred from the source to the destination storage resource. Once Batsim notifies that this job completed, the Storage Controller notifies back the decision process that the requested data transfer has completed.

A timestamp is saved for each data movement. In other words, there is a timestamp associated to each data-set in each storage. This timestamp corresponds to the last time the data-set has been requested on this storage.

When adding a new data-set to a storage, the Storage Controller makes sure that there is enough available space in the destination storage before starting the data transfer. In the case there is not enough space, an eviction policy is used to determine which data-sets should be removed to free space for the new data-set. The default policy in use is LRU (Least Recently Used ), which removes the data-set with the smallest timestamp in the storage. However, this eviction policy can be easily overridden by end-users of our simulator without diving into the main code. When implementing their decision process, end-users should simply create a call that inherits from the Storage Controller and override the eviction method. This enables the evaluation of more advanced eviction policies that can impact the overall scheduling decisions.

Finally, the presence of a particular data-set on a storage can be enforced through the Storage Controller API by assigning meta-information on a data-set. This information can then be used by the eviction policy to prevent for instance the deletion of the data-set while being used by running jobs.

SimGrid plug-ins

When designing an Edge simulator, it is a nonsense to foresee all the models and devices that may compose the platform. There are just too many. However, we claim that leveraging generic models is not the right solution either and so a trade-off should be found. We propose to leverage the SimGrid plug-ins capability that facilitates the implementation of new models without requiring intrusive changes in the simulation engine. We underline that, unfortunately, there is no generic manner of exposing information that can be captured by new plug-ins to the scheduler. Hence, some modifications might be required to extend the communication protocol of Batsim and exchange information between a particular plug-in and the decision process. Examples of such modifications are explained for the case of the Qarnot platform in Section 4. This is the trade-off to be able to cope with the high heterogeneity of edge infrastructures while targeting accuracy of sub-models.

Inria 3 Case Study: the Qarnot Computing Platform

We present in this section the Qarnot Computing infrastructure we use to demonstrate the relevance of our simulation tool.

Infrastructure Overview

Qarnot Computing has been incorporated in 2010 to develop "a disruptive solution able to turn IT waste heat into a viable heating solution for buildings". The infrastructure is distributed in housing buildings, offices and warehouses across several geographical sites in France. As of writing this report, the whole platform is composed of about 1,000 computing devices hosting about 3,000 disk-less machines, and is growing quickly. On each of the 20 geographical sites, there is a NFS-based storage with a few TB of capacity that enables disk-less machines to manipulate data. In a typical configuration a computing machine has a 1 Gbps uplink to a common switch, which then has up to 40 Gbps uplink to the NFS server. The latency between a computing machine and the NFS server is of the order of 1 ms. The various deployment sites are connected to the Internet using either a public or enterprise ISP, with characteristics varying from 100 Mbps to 1 Gbps symmetric bandwidth to the Internet, and about 10 ms latency to French data centres used by Qarnot to host control and monitoring services, the central storage system, and gateways to its distributed infrastructure.

On a daily basis, Qarnot computing solution processes from a few hundred to several thousands of batch jobs and thousands of cores are provisioned for dedicated corporate customers, and up to tens of GB of data are replicated from central storage to Edge Computing sites.

Platform Organisation and Terminology

The jobs and resources manager of the Qarnot platform is based on a hierarchy of 3 levels, as shown in Figure 1: the Qnode-, the QBox -and the QRad -level. The QNode is a root node, a "global" server that takes placement decisions for the whole platform. It can be viewed as a load balancer for the platform. On the second level are the QBoxes, "local" servers in smart buildings that take scheduling decisions locally on their own computing nodes. Each QBox is in charge of a set of computing nodes, the QRads, which are composed of one or several disk-less computing units denoted by QMobos.

Moreover, a centralised storage server is present at the QNode-level, while each QBox has its own local storage disk. From a physical point of view, the QNode and the storage server are in the Cloud while QBoxes are distributed over smart buildings of several cities, while QRads among a building are distributed in different rooms.

The Qarnot platform receives two types of user requests: requests for computing and requests for heating. The computing requests describe the workload to be executed on the platform. They are made by users that first upload input data needed to execute their jobs (named QTasks) to the centralised server and upload a Docker image either to the centralised server or the Docker Hub. Then, they submit the QTasks to the QNode. A QTask can be decomposed as a bag of several instances that share the same Docker image and data dependencies, but with different command arguments. This can be used for example to process each frame of a given movie, with one frame or a range of frames per instance.

The heating requests are made by inhabitants that can turn on and off the smart heaters in their homes, or set a target temperature for rooms to be reached as soon as possible. Since the computing units in a smart heater are unavailable when cooling is necessary, and are available otherwise, such changes increases the heterogeneity challenges of an Edge infrastructure: the computation capacity does not simply appear or disappear but also vary according to the heating needs.

Principle of the Actual Scheduling Policy

QTasks submitted to the platform are scheduled onto QMobos through two steps. The first step takes place at the QNode-level. The QNode greedily dispatches as much instances of the QTasks as possible on QBoxes, depending on the amount of QMobos available for computation on each QBox. The second step takes place at the QBox-level. Upon receiving instances of a QTask, the QBox will select and reserve a QMobo for each instance and fetch from the storage server each missing data dependency before starting the instances.

Notice that, at all times, a Frequency Regulator runs on each QRad to ensure that the ambient air is close to the target temperature set by the inhabitant, by regulating the frequencies of the QMobos and completely turning off a QRad if it is too warm. Moreover, whenever there is no computation performed on the QMobos while heating is required, "dummy" compute-intensive programs are executed to keep the QRad warm.

Modelling such an infrastructure to identify improvement opportunities and analyse new scheduling strategies is something possible with our Batsim extensions.

Simulated Platform

We detail in the following how we modelled and instantiated the Qarnot platform with our simulation toolkit, also depicted in Figure 2.

The schedulers of the QNode-and QBox-level were implemented using Batsim's Python API. The decision process is a Python process holding one instance of the QNode scheduler and the Storage Controller, and one instance of the QBox scheduler for each QBox of the simulated platform. Upon receiving a message from Batsim, the simulation events are directly forwarded to the correct destination scheduler or the Storage Controller that should handle it.

In the following we provide details about the descriptions of the platform, the workload and data-sets, the temperature modeling, and how we generated the input files for the simulation.

Platform Description

All details about the platform description are modelled by the XML platform file given to SimGrid at the beginning of the simulation. Each QMobo is simulated as a single-core SimGrid host (representing a machine) as they are the only computing units of the platform. A host holds information such as its unique identifier, the list of speeds and corresponding power usage of its processor, and additional values such as the thermal coefficients required by the temperature plug-in. We keep the same hierarchical structure of the Qarnot platform: QMobos belonging to the same QRad are aggregated in the same SimGrid zone (representing a network). Similarly, all QRads of a same QBox are aggregated in the same zone, as well as all QBoxes of the QNode. The simulation of storage spaces is done by adding special hosts which carry the Batsim storage role. Thus, in each QBox zone, there is one additional storage host for the QBox disk. Similarly, there is one storage host in the QNode zone to represent the storage server.

Workload Description

The workload to simulate is represented by a JSON file fed to Batsim that contains a list of job and profile descriptions.

Job descriptions are defined by the user requests and contain: the id, the submission time, and the job profile to use. Profile descriptions represent how a job should be simulated, plus other specific information, and contain: the type of the job to simulate, the number of flops to compute, the job priority and the list of data-sets required as inputs.

Each instance of a given QTask in Qarnot can run independently from the others, so we transcribed each instance as one Batsim job and profile. Instances belonging to the same QTask have the same data-set dependencies and submission times.

at the QNode-level, denoted by LocalityBased. Upon dispatching instances, LocalityBased gives priority to the QBoxes already having the data-set dependencies of the QTask on their storage disk. This variant aims at taking benefit from the data locality and reducing the data transfers.

To evaluate the impact of data placement on the scheduling decisions, we also implemented three variants of replication policies upon the submission of QTasks. The question we want to answer with these variants is whether replicating data-sets can achieve significant improvements, and at which cost? The first two variants, denoted by Replicate3 and Replicate10, respectively replicates the data-dependencies of a submitted QTask on the 3 and 10 least loaded QBox disks among the 20 QBoxes in the platform, before applying the LocalityBased scheduling algorithm. These two variants aim at reducing the waiting time of the instances by providing more QBox candidates for the LocalityBased dispatcher. The last variant, denoted by DataOnPlace, instantaneously copies all data-set dependencies on all QBox disks upon the submission of a QTask. Even if it is unrealistic, this variant aims at visualising the behaviours of the standard scheduling policy without having any impact caused by the data transfers.

Simulated Workloads

We extracted 4 different simulation inputs corresponding to logs of the Qarnot platform for a 1-week period each. Since the simulation and the scheduling algorithms are deterministic, we ran one simulation with each combination of scheduler and workload. Each simulation took less than 20 minutes to run, with about 60% of the time spent in the decision process.

The considered workloads contained between 5,000 and 9,000 instances and between 40 and 60 different data-sets. In each workload, there was at least one data-set used by 50% of the instances, and up to 9 data-sets were used by 700 instances in workload 2 (as depicted in Figure 3). This information shows that using replication for data-sets should improve the quality of the schedules compared to standard scheduling decisions.

In our simulations, we compared the quality of the produced schedules using the waiting time of the instances, the total number of transfers that occurred, and the total data transferred in GB. For one instance, the waiting time denotes the difference between its starting and submission times.

Simulation Results

Figures 4 to 7 shows, respectively for each worklaod, the waiting time distribution for each scheduling strategy separated in 3 intervals for better clarity. Figure 8 shows the amount of manipulated data we observed through simulations for the four workloads. Note that, in the following, the analysis of the results is focused on workload 3.

Impact of data locality

As depicted on Figures 4 to 7, for each scheduler more than 60% of the instances waited less than one second before starting their execution. The last column shows that a few instances waited a long time before starting their execution (around 1,455 or 2,910 seconds). This is due to the long transfer time of one of their data dependencies that was as large as 36 GB, while other data-sets were smaller than 5 GB. Comparing the behaviour of Standard and LocalityBased, we do not observe a big difference in the distribution of the waiting times, except for the amount of instances that waited for 1,455 seconds. This is confirmed by the average value over all instances of 39 seconds for Standard and 34.6 for LocalityBased.

Regarding the amount of data manipulated Figure 8 shows, the results from the Locali-tyBased scheduler is as expected: dispatching instances on QBoxes already having the data-set dependencies on their disk permits to reduce the number of transfers by about 44%, and between 30 and 65% the total data transferred, compared to Standard. To conclude, considering data locality decreases the amount of data transfer as expected but does not seem to be satisfactory enough to significantly improve waiting times.

Transferring data has a cost

Replicating data-sets permits to reduce the mean waiting times of the instances but at a cost of more data transfers, as depicted in Figure 8. More precisely for the 3rd workload, the mean waiting time of the instances decreases from 34.6 to 32.6, 28.6 and 22.2 seconds respectively for LocalityBased, Replicate3, Replicate10 and the unrealistic DataOnPlace strategies. While these results look encouraging, it is important to take into account the associated overhead in terms of data transfer: from 90 GB to 384 GB for Replicate3, 599 GB for Replicate10 and 1,056 GB for DataOnPlace. This respectively corresponds to an overhead in terms of data transfer of 4.3x, 6.7x and 11.7x.

Consequently, it is not clear whether replicating data-sets at a high ratio is a valid approach. On the first hand, taking into account only the data locality is not sufficient to have good waiting time performance (LocalityBased). On the second hand, it is crucial to control data-set exchanges as they have an impact on the overall performance. For instance, it may make sense to have a replication ratio that is dynamic according to the popularity of the data-set and the status of the platform. In other words, it is crucial to also consider the time spent in data transfer before taking scheduling decisions. This is critical as the size of data-sets should be increased with respect to IoT-based scenarios envisioned by Qarnot Computing. In this regard, we plan to extend the Storage Controller to estimate the transfer time of a data-set to a given storage entity at a certain time. This information is valuable for the schedulers to decide when triggering data transfers and on which QBoxes. Besides, we plan to leverage our proposal to evaluate whether exchanging data-sets directly between QBoxes can help us reduce the data transfer time.

Finally, we recall that our goal through this study was not to find the best scheduling algorithm but to illustrate the use of our simulation toolkit on a concrete scenario, and to demonstrate how such a simulator would help to drive the design of scheduling and data placement strategies. Capturing the aforementioned observations in the Qarnot Computing production platform would have been impossible.

Related Simulation Tools

We described in this report a novel simulation tool for easily designing and testing scheduling and data placement strategies on Edge Computing platforms. We motivated the effort of building a new simulator using adequate tools for modelling the processing and storage units and the network topology. We discuss briefly below the main competitors and argument for our simulator.

Some simulators have constraints that would prevent us to correctly simulate a platform such as the Qarnot one. For example, EmuFog [START_REF] Mayer | Emufog: Extensible and scalable emulation of large-scale fog computing infrastructures[END_REF] does not support hierarchical fog infrastructures, whereas Qarnot infrastructure is inherently hierarchical.

Other simulators such as iFogSim [START_REF] Gupta | ifogsim: A toolkit for modeling and simulation of resource management techniques in internet of things, edge and fog computing environments[END_REF], EdgeCloudSim [START_REF] Sonmez | Edgecloudsim: An environment for performance evaluation of edge computing systems[END_REF] and IOTSim [START_REF] Zeng | IOTSim: a Cloud based Simulator for Analysing IoT Applications[END_REF], are simulation frameworks that enable to simulate Fog or Edge Computing infrastructures and execute simulated applications on top of it. These solutions are close to our work. However they have been built on top of the CloudSim toolkit [START_REF] Calheiros | Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[END_REF]. Although widely used to study algorithms and applications, CloudSim is based on a top-down approach of cloud environments. This is efficient to deliver the right abstractions to the end-users but unfortunately lacks of validations of the low-level models. We believe it is an important issue as it may return invalid observations. Besides, the Inria Batsim/SimGrid toolkit is the only one that has been designed to study and compare scheduling challenges in an easy manner. In other simulators, including CloudSim, researchers have to implement a lot of business logic that is redundant each time they want to investigate a scheduling policy. Batsim/SimGrid delivers all this logic in a generic manner, making it more versatile and user-friendly for researchers/engineers.

Concluding Remarks and Future Steps

We presented in this report extensions we made to the Batsim/SimGrid framework to evaluate scheduling and data placement policies in Edge Computing infrastructures. Its integration into a simulator leads to a complete management system for Edge Computing platforms that focuses on the evaluation of scheduling strategies, taking into account both jobs and data.

While more extensions are still under development, the presented toolkit already enables researchers/engineers to easily evaluate existing load balancing and placement strategies. It may also serve at developing and testing new strategies thanks to its modular and clear interface.

To assess the interest of such simulator, we instantiated the toolkit to simulate the whole Edge platform of the Qarnot Computing company based on smart heaters. As a first use case, we investigated four scheduling strategies and compared them to the actual policy implemented in the Qarnot platform. We showed that replication of data-sets is an interesting approach to reduce job waiting times but requires additional investigations to determine how the replication ratio can be computed according to several metrics, such as data-set popularity, size, etc. To help researchers move forward on this question, we are currently extending the Storage Controller to monitor additional information such as the number of on-going data transfers. We are also discussing with the SimGrid team to see how we can leverage information about the current load of the links between resources to have estimations of bandwidth, latency or the time to transfer a particular data-set from a source to a destination.

Besides, we envision to design an automatic and probabilistic injector of machine and network failures based on statistical studies of the platform logs and learning techniques. Being able to model the dynamic of Edge infrastructures would be also an important added-value for our framework to capture side effects of such events on scheduling strategies.

Figure 1 :

 1 Figure 1: Scheme of the Qarnot platform.

Figure 2 :

 2 Figure 2: Scheme of the simulated Qarnot platform.

Figure 3 :Figure 4 :Figure 5 :Figure 6 :

 3456 Figure 3: Number of instances using each dataset for the second workload.

Figure 7 :Figure 8 :

 78 Figure 7: Waiting time distribution (in seconds) of all instances of the fourth workload.

RR n°9282

https://gitlab.inria.fr/batsim/batsim and https://gitlab.inria.fr/batsim/pybatsim RR n°9282

https://gitlab.inria.fr/batsim/batsim/tree/temperature-sbac-2020

https://github.com/Mommessc/simgrid/tree/temperature-sbac-2020

https://gitlab.inria.fr/batsim/pybatsim/tree/temperature-sbac-2020 Inria

Acknowledgements.

This work was supported by the ANR Greco project 16-CE25-0016-01 and by AUSPIN with the International Student Exchange Program from the University of São Paulo.

Data-sets Description

The list of data-sets is described as a list of JSON objects (one per line). Each data-set is represented by the unique identifier of the data-set and its size in bytes. This file is read by the decision process and fed to the Storage Controller at the beginning of the simulation to initialise the state of the storage server.

Temperature Modeling

As temperature plays an important role in the platform and the scheduling decisions, we leveraged the plug-in mechanism of SimGrid to implement our own model. Built on top of the existing energy plug-in [START_REF] Heinrich | Predicting the Energy Consumption of MPI Applications at Scale Using a Single Node[END_REF], our plug-in computes the temperature of a QRad and its ambient air from the energy consumption of the QMobos and other physical parameters, such as the thermal conductivity and mass of the QRad. For the simulation of heating requests, each change of a QRad target temperature is simulated as an external event injected in the simulation, with additional fields for the name of the QRad and the new target temperature. Besides, we take into account the outside temperature of the cities where the QRads are deployed. This value is measured on a one-hour basis and injected as another type of external event.

We modified Batsim to relay these external events to the SimGrid temperature plug-in, and we modified the communication protocol to periodically forward the ambient air temperature of each QRad to the scheduler.

The modifications that are specific to the Qarnot use-case, such as the handling of temperature events, are available in a separate branch of the repository 2 . The temperature plug-in is also available in a separate SimGrid repository of the authors 3 .

Input Files Generation

A log extractor was built to generate all the input JSON files from real logs of the Qarnot platform, for a given time period (including the list of jobs to execute, the list of data-sets in the storage server and the list of external events). It is noteworthy that due to users' privacy reasons, we cannot provide access to the log extractor and the Qarnot logs used for the experiments. Since we want to simulate an exact time period, we added a special external event that enforces the simulation to stop at a particular time.

Simulations

Two kind of experiments have been performed to investigate the Qarnot Computing use case. The first aimed to compare the standard scheduling policy used in the real Qarnot platform with a policy based on locality of the data-sets. The second experiment enabled us to study the impact of replication policies for the data-sets that are uploaded on the platform (i.e., how they affect the scheduling decisions). The code of all evaluated schedulers is available in a dedicated branch of Batsim's Python API repository 4 .

Data/Job Scheduling Policies

Along with the real Standard Qarnot scheduler that serves as a baseline for our experiments (see Section 3.3), we implemented a variant using the data-locality to take scheduling decisions


Zoé Colombet
email: zoe.colombet@gmail.com

Benjamin Allès

Wendy Si Hassen

Aurélie Lampuré

Emmanuelle Kesse-Guyot

Sandrine Péneau

Serge Hercberg

Caroline Méjean

Colombet

Si Allès

Hassen

Péneau Kesse-Guyot Lampuré

Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort

Keywords: 95% CI: 95% confidence interval Basal Metabolic Rate CARDIA: Coronary Artery Risk Development in Young Adults Plant-based foods, plant proteins, dietary change, sustainable diet, longitudinal analysis, individual characteristics

Purpose: Rebalance the contribution of animal-and plant-based foods is needed to achieve sustainable diet. However, little is known concerning individual characteristics that may influence intake of plant-based foods and their changes over time. We aimed to assess changes in the contribution of plant-based foods to dietary intake over time, and their association with individual characteristics.

Methods

: The contribution of plant-based foods was assessed by percent energy intake provided by plant proteins in diet (PEIPP) and a score of adherence to a pro-vegetarian diet, using repeated 24hours records in 15,615 French adults participating in the NutriNet-Santé cohort study. Associations between baseline individual characteristics and changes in the two indicators over a 4 to 6 year followup were assessed using a linear mixed model. Results: At baseline, PEIPP and pro-vegetarian score were positively associated with age (β65+=0.80, 95%CI=[0.71, 0.88], β65+=3.30, 95%CI=[2.97, 3.64], respectively) and education (βpostgraduate=0.23, 95%CI=[0.12, 0.34], βpostgraduate= 1.19, 95%CI=[0.75, 1.62]), while they were inversely associated with BMI class (βobesity=-0.48, 95%CI=[ 0.56, 0.41], βobesity=-2.31, 95%CI=[-2.63, -1.98]). Men had higher PEIPP than women (β=0.06, 95%CI=[0.01, 0.11]). Pro-vegetarian score significantly increased over time (β=0.23, 95%CI=[0.08, 0.37]). The older the individual at baseline, the greater the decrease in the two indicators during follow-up. Pro-vegetarian score increased during follow-up for obese participants at baseline.

Conclusions:

The contribution of plant-based foods was associated with several socio-demographic and economic characteristics at baseline, whereas change over time was related to age and weight status. Further analysis of individual obstacles and lever to consume plant-based foods is needed.

Introduction

Consumption of animal-based foods is a major environmental, public health and economic issue [START_REF] Burlingame | Sustainable diets and biodiversity -Directions and solutions for policy research and action[END_REF].

Previous studies have highlighted that, compared to production of plant-based foods, production of animal-based foods mobilizes more resources (space, energy, water, etc.) and seems to have a deleterious impact on the environment [START_REF] Carlsson-Kanyama | Potential contributions of food consumption patterns to climate change[END_REF][START_REF] Mcmichael | Food, livestock production, energy, climate change, and health[END_REF]. However, the health impact of plant-based diet compared with animal-based diet remains an ongoing discussion. Available studies on the health impact of red and processed meat have mostly highlighted that a high consumption of red and processed meat is a notable risk factor for major nutrition-related chronic diseases and for early mortality, compared with low intake or consumption of other meat [START_REF]Food, nutrition, physical activity and the prevention of cancer: a global perspective[END_REF][START_REF] Micha | Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and metaanalysis[END_REF][START_REF] Sinha | Meat intake and mortality: a prospective study of over half a million people[END_REF][7][START_REF] Nagao | Meat consumption in relation to mortality from cardiovascular disease among Japanese men and women[END_REF]. In contrast, an increasingly number of studies have shown that plant-based diets and diets with lower intake of red and processed meat and higher intake of fruits and vegetables seems to be associated with longer life expectancy and lower risk of chronic disease [9][START_REF] Aston | Impact of a reduced red and processed meat dietary pattern on disease risks and greenhouse gas emissions in the UK: a modelling study[END_REF][START_REF] Scarborough | Modelling the health impact of environmentally sustainable dietary scenarios in the UK[END_REF][START_REF] Key | Cancer in British vegetarians: updated analyses of 4998 incident cancers in a cohort of 32,491 meat eaters, 8612 fish eaters, 18,298 vegetarians, and 2246 vegans[END_REF][START_REF] Chang-Claude | Lifestyle determinants and mortality in German vegetarians and health-conscious persons: results of a 21-year follow-up[END_REF][START_REF] Key | Mortality in British vegetarians: results from the European Prospective Investigation into Cancer and Nutrition (EPIC-Oxford)[END_REF]. To improve overall food and nutrition security, and to limit the environmental impact of diet, rebalancing of dietary intake toward plant-based foods in western societies may therefore represent sustainable answers [START_REF] Burlingame | Sustainable diets and biodiversity -Directions and solutions for policy research and action[END_REF][START_REF] Pimentel | Sustainability of meat-based and plant-based diets and the environment[END_REF].

To rebalance the contribution of plant vs. animal foods in the diet, knowledge of the association between consumption of plant-and animal-based foods and individual characteristics is needed.

Available studies focused on socio-demographic and economic characteristics associated with intake of specific plant-based foods and, in particular, fruits and vegetables [START_REF] Darmon | Does social class predict diet quality?[END_REF][START_REF] Micha | Global, regional and national consumption of major food groups in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys worldwide[END_REF][START_REF] Drewnowski | Impact of aging on eating behaviors, food choices, nutrition, and health status[END_REF][START_REF] Laforge | Psychosocial factors influencing low fruit and vegetable consumption[END_REF][START_REF] Devine | Life-course events and experiences: association with fruit and vegetable consumption in 3 ethnic groups[END_REF]. To the best of our knowledge, no study has yet assessed individual determinants of the contribution of plant-and animalbased foods to diet. Understanding changes in dietary intake in recent years based on individual characteristics is essential so as to efficiently promote consumption of plant-based foods [START_REF] Lea | Consumers' readiness to eat a plant-based diet[END_REF]. Few available studies have assessed the association between individual characteristics and change in diet over time [START_REF] Sijtsma | Longitudinal trends in diet and effects of sex, race, and education on dietary quality score change: the Coronary Artery Risk Development in Young Adults study[END_REF][START_REF] Arabshahi | Longitudinal change in diet quality in Australian adults varies by demographic, socio-economic, and lifestyle characteristics[END_REF][START_REF] Roos | Trends of socioeconomic differences in daily vegetable consumption, 1979-2002[END_REF][START_REF] Harrington | Capturing changes in dietary patterns among older adults: a latent class analysis of an ageing Irish cohort[END_REF][START_REF] Eng | Effects of marital transitions on changes in dietary and other health behaviours in US male health professionals[END_REF][START_REF] Lee | Effects of marital transitions on changes in dietary and other health behaviours in US women[END_REF][START_REF] Vinther | Marital transitions and associated changes in fruit and vegetable intake: Findings from the population-based prospective EPIC-Norfolk cohort, UK[END_REF]. In the Coronary Artery Risk Development in Young Adults (CARDIA) study conducted among US adults, dietary quality increased over time with age [START_REF] Sijtsma | Longitudinal trends in diet and effects of sex, race, and education on dietary quality score change: the Coronary Artery Risk Development in Young Adults study[END_REF]. In contrast, an Australian study showed that younger age at baseline was independently associated with improvement in dietary quality over time [START_REF] Arabshahi | Longitudinal change in diet quality in Australian adults varies by demographic, socio-economic, and lifestyle characteristics[END_REF]. Change in diet quality and fresh vegetable consumption over time was related to gender in Australian and Finnish adults [START_REF] Arabshahi | Longitudinal change in diet quality in Australian adults varies by demographic, socio-economic, and lifestyle characteristics[END_REF][START_REF] Roos | Trends of socioeconomic differences in daily vegetable consumption, 1979-2002[END_REF]. Regarding socio-economic factors, dietary quality [START_REF] Sijtsma | Longitudinal trends in diet and effects of sex, race, and education on dietary quality score change: the Coronary Artery Risk Development in Young Adults study[END_REF][START_REF] Arabshahi | Longitudinal change in diet quality in Australian adults varies by demographic, socio-economic, and lifestyle characteristics[END_REF][START_REF] Harrington | Capturing changes in dietary patterns among older adults: a latent class analysis of an ageing Irish cohort[END_REF] and daily fresh vegetable consumption [START_REF] Roos | Trends of socioeconomic differences in daily vegetable consumption, 1979-2002[END_REF] increased over time in better-educated individuals, subjects belonging to higher occupational and income classes. Regarding Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 [START_REF] Micha | Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and metaanalysis[END_REF], 1991-2002. , DOI : 10.1007/s00394-018-1752-8

5 lifetime events, a decrease in fruit and vegetable intake at the end of a marriage (divorce, widowhood) [START_REF] Eng | Effects of marital transitions on changes in dietary and other health behaviours in US male health professionals[END_REF][START_REF] Lee | Effects of marital transitions on changes in dietary and other health behaviours in US women[END_REF][START_REF] Vinther | Marital transitions and associated changes in fruit and vegetable intake: Findings from the population-based prospective EPIC-Norfolk cohort, UK[END_REF] and an increase upon remarrying were observed [START_REF] Eng | Effects of marital transitions on changes in dietary and other health behaviours in US male health professionals[END_REF][START_REF] Lee | Effects of marital transitions on changes in dietary and other health behaviours in US women[END_REF]. However, no prospective cohort had previously assessed the association between individual characteristics and change in consumption of plant-and animal-based foods in recent years, even though readjustment of such intake has become of greater concern to consumers [29].

The aim of the present study was to assess the association between individual characteristics and the contribution of plant-based foods to diet both at baseline and over time, using two complementary indicators. The first indicator that assesses the percent energy intake from plant proteins in diet represents the proportion of plant consumption in the diet from a nutritional point of view while the provegetarian score aims to evaluate the adherence to a vegetarian diet and therefore provides a behavioural point of view of dietary habits [30]. In addition, the first indicator had been previously used in epidemiological studies [31][32][33][34], enabling comparison of our results with data from the literature.

Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse Electronic informed consent was obtained from all participants.

Data collection

Dietary assessment

At baseline and each year thereafter, participants were invited to complete three non-consecutive validated web-based 24 hours (24-h) dietary records, randomly assigned over a 2-week period (2

products) was computed by adjusting for total energy intake using the residual method [42] separately for men and women. The energy-adjusted estimates were ranked according to their sex-specific quintiles. For this score, plant-derived foods were positively weighted, assigning a value of 1 for the first quintile, 2 for the second quintile, and successively, up to a value of 5 for the fifth quintile. Animalderived foods were negatively weighted, assigning a value of 5 for the first quintile to 1 for the fifth quintile. The scores were then summed up to obtain a global score ranging from 12 (lowest adherence) to 60 (highest adherence).

Assessment of socio-economic and demographic characteristics

At baseline, self-administered questionnaires were used to collect data on socio-economic and demographic characteristics, including sex, age, size of the urban unit of residence, educational level and household composition. To assess educational level, participants were asked their highest attained diploma. Educational level was recoded into five categories: none or primary education, secondary education, high school graduate or equivalent, undergraduate (corresponding to up to 3 years following high school graduation), and post-graduate (more than 3 years after high school graduation). Household composition was classified into three categories: living alone without a child, living with at least one adult and no child, or living with at least one child. Size of the urban unit of residence was categorized into rural, fewer than 20,000 inhabitants, 20,000 to 200,000 inhabitants, more than 200,000 inhabitants and Paris. Height and weight were also self-reported at baseline; body mass index (BMI) was calculated and categorized according to the World Health Organization (WHO) classification for adults [START_REF]Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee[END_REF]: underweight, normal weight, overweight and obesity. Validity of selfreported height and weight has been evaluated by a previous study conducted in the NutriNet-Santé cohort [START_REF] Lassale | Validity of web-based self-reported weight and height: results of the Nutrinet-Santé study[END_REF]. Self-reported and measured height and weight were compared and showed a sensitivity of 88% and a specificity of 99%.

Statistical analysis

The present analysis focused on participants in the NutriNet-Santé study included between May 2009 and April 2010 who had at least 2 sets of 24-h dietary records: at baseline and after 4 years of followup. A set was composed of two or three 24-h dietary records. Participants were invited to complete a set every year during the 6 years of follow-up, leading to a maximum of 6 sets of 24-h dietary records 9 per subject (range: 2 to 6 collected sets). Other selection criteria to be included in the analysis sample were: residency in mainland France, not being underreporters of energy intake, and no missing data for individual characteristics at baseline. Self-reported vegetarians and vegans were excluded from this analysis. They are overrepresented in the NutriNet-Santé study cohort, and their inclusion in the analysis sample might have artificially increased the mean scores of vegetable protein consumption.

Energy-underreporting participants were identified by the method proposed by Black [START_REF] Black | Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations[END_REF] and excluded. Briefly, basal metabolic rate (BMR) was estimated by Schofield equations [START_REF] Schofield | Predicting basal metabolic rate, new standards and review of previous work[END_REF] according to sex, age, weight and height collected at enrolment in the study. BMR was compared to energy intake, taking into account the physical activity level (a physical activity level of 1.55 was used to identify underreporting subjects) [START_REF] Black | Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations[END_REF].

For each sex, weighting was calculated using the iterative proportional fitting procedure according to the 2009 French national census reports on age, occupational categories, educational level, marital status and geographical area of residence [47]. Weighting was accounted for all analyses.

Mean percent energy intake provided by plant proteins and mean pro-vegetarian score at baseline were compared by sex, age group, educational level, household composition, size of the urban unit of residence and BMI category, using Student's t-test or analysis of variance (ANOVA) as appropriate, and were presented with their standard error (SE).

The database was constituted of repeated yearly measurements of the two dietary indicators for each participant. To assess the evolution of the two indicators over time and their interaction with individual characteristics, linear mixed models were used. They take into account inter-individual variability induced by the change in consumption for each subject, and the intra-subject correlation resulting from repeated measurements of indicators in the same subject [START_REF] Laird | Random-effects models for longitudinal data[END_REF]. Multivariate linear mixed models, including sex, age, educational level, household composition, size of the urban unit of residence and BMI categories at baseline, were established. Models were also adjusted for household composition at the last follow-up. To select individual characteristics, univariable analysis was conducted and all analyses were significant. A P-value <0.05 was considered statistically significant. 11

Comment

Results

Among 33,212 subjects included between May 2009 and April 2010, with at least 2 sets of 24-h dietary records and who were not energy-underreporting subjects, we excluded 499 persons not living in the French metropolitan area, 492 who self-reported being vegetarian and vegan, 268 participants with missing data for variables at baseline (BMI, educational level and size of the UU) and 657 participants with missing data for household composition at the last follow-up (Figure 1). We also excluded 15,681 participants who were lost to follow-up after four years, leaving 15,615 participants in the final sample.

All subjects had been followed for 4 years, and 51% had been followed for up to 6 years. The mean follow-up was 4.6 years (SD: 0.5 years). In this sample, 29% of the participants had completed all their dietary sets of records every year for 6 years, 36% had 5 sets of records, 21% had 4 sets of records, 10% had 3 sets of records and 4% had only 2 sets of records.

Comparisons between participants and excluded subjects (17,598 subjects) presented in Supplementary Table 1 showed that included subjects were older (mean 48.4 years (SD: 14.1) vs.

41.7 years (SD: 14.1)); the percentage of those not living with children and the prevalence of overweight were higher, while the percentage of those living with children and the obesity prevalence were lower. In addition, the mean percentage of energy intake from plant proteins and the mean provegetarian score were equivalent in both included and excluded subjects (means 5.6% (SD: 1.3) vs.

5.5% (SD: 1.4) and 36.1 (SD: 5.1) vs. 35.9 (SD: 5.3), respectively) (Supplementary Table 1).

According to raw data, the size of the urban unit of residence of our sample has similar percentage that those reported by the national census while men, young adults, subjects with a low level of education and individuals living with a child were poorly represented in our sample (Table 1).

Sociodemographic characteristics were modified by the weighting procedure. Demographic and socioeconomic characteristics were mostly comparable between the weighted sample and the French general population, except for education (Table 1). After weighted, about half of participants in the analysis sample were women (Table 1). Mean age at baseline was 47.8 years (SE: 0.1). In addition, 63% of participants had an educational level higher than or equal to high school graduation. More than half of the subjects were living without a child but with at least one adult but only 5% were living alone, 2).

Comment
The mean PEIPP was higher in older subjects, individuals who lived alone and underweight or normalweight subjects, compared to young adults, those who lived with at least one child and obese persons respectively (Table 2). The mean pro-vegetarian score was positively associated with age (Table 2).

The score was higher in individuals who lived alone or with another adult and in underweight or normal weight subjects compared to those who lived with at least one child, and compared to obese persons.

During follow-up, PEIPP decreased slightly (-0.1% (SE: 0.01), p<0.01), while the pro-vegetarian score showed a slight increase (+0.1 (SE: 0.04), p=0.02) over time (Supplementary Table 2). In addition, energy intake without alcohol decreased (-156.2 kJ/day (SE: 16.8), p<0.01) as the mean protein intake (-1.9 g/day (SE: 0.2), p<0.01) and the percent of plant protein in the protein intake (-0.5% (SE: 0.1), p<0.01). Men had slightly higher intakes of plant proteins than women whereas previous studies showing lower consumption of plant foods such as fruits and vegetables in men than in women [START_REF] Micha | Global, regional and national consumption of major food groups in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys worldwide[END_REF][START_REF] Roos | Trends of socioeconomic differences in daily vegetable consumption, 1979-2002[END_REF][START_REF] Jensen | Preferences, quantities and concerns: socio-cultural perspectives on the gendered consumption of foods[END_REF]. Further analysis showed that the slightly higher contribution of plant proteins in men in our study may be explained by their equivalent cereals, potatoes and tubers consumption, an important source of plant proteins (Supplementary Table 2).

Results of the associations

At baseline, the contribution of plant proteins to diet was positively associated with age, in agreement with the literature [START_REF] Micha | Global, regional and national consumption of major food groups in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys worldwide[END_REF][START_REF] Drewnowski | Impact of aging on eating behaviors, food choices, nutrition, and health status[END_REF]. This suggests that dietary habits of older adults are more in line with nutritional recommendations than in young adults, including higher consumption of fruits and vegetable, possibly due to a generational effect on dietary patterns and food supply practices. Despite higher intake of plant-based foods, the older the participant at baseline, the more the two indicators decreased over time compared to younger participants at baseline. Food and nutrient intake, including plant-based foods, declined with age [START_REF] Drewnowski | Impact of aging on eating behaviors, food choices, nutrition, and health status[END_REF], possibly due to physiological changes associated with aging such as altered taste and smell, altered digestive capacity and altered dentition and chewing ability, limiting intake of certain foods and reducing dietary diversity [START_REF] Drewnowski | Impact of aging on eating behaviors, food choices, nutrition, and health status[END_REF][START_REF] Kimura | Evaluation of chewing ability and its relationship with activities of daily living, depression, cognitive status and food intake in the communitydwelling elderly[END_REF]. 15

Comment
In agreement with a multicenter study on protein intake conducted in 10 European countries including France [34], our study showed that education was positively associated with both pro-vegetarian score and PEIPP. Indeed, education is associated with better understanding of the importance of nutritional information messages and the ability to appropriate these in order to generate eating behaviour in line with nutritional recommendations, such as higher consumption of fruits and vegetables and lower intake of animal-based foods [START_REF] Darmon | Does social class predict diet quality?[END_REF][START_REF] Galobardes | Indicators of socioeconomic position (part 1)[END_REF][START_REF] Méjean | Socio-economic indicators are independently associated with intake of animal foods in French adults[END_REF].

Previous studies on the association between the presence of children in the household and intake of fruits and vegetables showed contradictory results [START_REF] Laforge | Psychosocial factors influencing low fruit and vegetable consumption[END_REF][START_REF] Devine | Life-course events and experiences: association with fruit and vegetable consumption in 3 ethnic groups[END_REF]. In the present study, at baseline, no significant association was found with the pro-vegetarian score, but individuals who lived alone had higher PEIPP than those living with children. In available studies, for individuals living with children compared to those living alone, overall consumption of meat, seafood and eggs was higher [START_REF] Touvier | Variations in compliance with recommendations and types of meat/seafood/eggs according to sociodemographic and socioeconomic categories[END_REF][START_REF] Hercberg | The French National Nutrition and Health Program: 2001-2006-2010[END_REF],

which may explain their lower ratio of plant proteins vs. animal proteins. Results showing the decrease in the pro-vegetarian score over time in persons living with at least one adult but without a child at baseline may be due to additional intrahousehold factors not taken into account in our study, such as psychosocial influences [START_REF] Devine | Life-course events and experiences: association with fruit and vegetable consumption in 3 ethnic groups[END_REF][START_REF] Schäfer | Life events as turning points for sustainable nutrition[END_REF].

The pro-vegetarian score and the contribution of plant proteins to the diet were lower in subjects who presented overweight and obesity. Our finding is consistent with a multicenter study showing an inverse association between BMI and intake of plant proteins in French women, while a positive association between BMI and intake of animal proteins was found [34]. Our results is also in line with the findings of the prospective cohort Chicago Western Electric Study showing a significant inverse association between higher vegetable protein intake and obesity and a positive association between animal protein intake and obesity in employed men aged 40-55 years [58]. Our result is mainly due to higher consumption of animal foods such as red and processed meats, eggs and dairy products and, to a lesser extent, to lower intake of fruits, cereals and nuts in obese and overweight participants (data not shown). The pro-vegetarian score was higher at baseline and over time for participants who presented obesity compared to underweight or normal-weight participants. Obese subjects increased their intake of plant-based foods such as fruits, legumes and nuts during follow-up, and decreased their intake of animal-based foods (Supplementary as the protein intake but the percent of plant protein in the protein intake increased (Supplementary Table 2). Obese participants at baseline may have changed their dietary intake over time, possibly increasing plant-based food intake.

For this analysis, two indicators were chosen among several potential indicators to assess the role of plant foods in the diet. We have chosen the PEIPP rather than the ratio of plant proteins to animal proteins also used in the literature, since our preliminary analyses showed that this indicator reflected the percent energy intake provided by animal proteins. Indeed, when the PEIPP increased, the percent of energy intake provided by animal proteins decreased. Also, an increase in PEIPP was associated with a decrease of the intake of protein: 90.3 g in the first quartile of PEIPP and 83.1 g in the last quartile (p<0.01, Supplementary Table 3). Thereby, an increase in PEIPP not reflects a diet with more protein but it represents an increase of the proportion of plant consumption in the diet from a nutritional point of view. As expected, an increase in the pro-vegetarian score was associated with an increase of plant product intakes but a decrease of the animal product intakes (Supplementary Table 4).

Interpretation of present results should take into account several limitations. Since the cohort is not random, women and individuals belonging to highly educated groups may be overrepresented. . Difference regarding men in our sample may be due to a bias induced by volunteering: male participants may be more aware and may attach greater importance to nutrition issues and so may make dietary choices more in line with nutritional recommendations than those of the general population. Caution is therefore needed when interpreting and generalizing our results. Differences in dietary intake between gender and education categories are probably wider in the general population, which may explain why there was no significant association with indicators over time. The large size of our sample may have also been a constraint, since significant results were found even when the difference in intake according to categories was slight. However, the large sample size was also a strength, as it enabled a wide diversity of individual characteristics. Validity of self-reported height and weight can be questioned therefore it has been evaluated by a previous study conducted in the NutriNet-Santé cohort, showing that they can be considered as valid enough to be used [START_REF] Lassale | Validity of web-based self-reported weight and height: results of the Nutrinet-Santé study[END_REF]. The question of accuracy of web-based self-reported data also arises for repeated 24-h dietary records compared to interviews by trained dietitians. However, the validity of our web-based self-reported dietary record tool was tested against 24-h urinary and plasma biomarkers. It showed that the web-based dietary record tool used in the NutriNet-Santé study performs well at estimating protein (correlations with urinary, 0.61 in men, 0.64 in women) and potassium (correlations with urinary, 0.78 in men, 0.42 in women) intakes, and fairly well at estimating fruits and vegetables (correlation with plasma beta-carotene, 0.35 in men and 0.41 in women), fish (correlation with plasma docosahexaenoic acid and eicosapentaenoic acid, 0.51 in men and 0.54 in women), beta-carotene (correlations with plasma, 0.37 in men, 0.38 in women), vitamin C (correlations with plasma, 0.58 in men, 0.32 in women), sodium (correlations with urinary, 0.47 in men, 0.37 in women), and n-3 fatty acids intakes (correlations with plasma, 0.36 in men, 0.38 in women) [37,38]. In addition, a pilot study comparing our web-based 24-h record tool with dietitian interviews showed strong agreement between the two methods, particularly for plant-based vs. animal-based food intakes [36]. Some participants may belong to the same household and this may modify the results but this information has not been collected. Finally, we did not take into consideration life-events even though previous studies shown that life-events can influence dietary intake

8 10

 8 citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-Data management and statistical analyses were performed using SAS (version 9.4; SAS Institute, Inc., Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8

14 Discussion

 14 between baseline individual characteristics and change over time of PEIPP and the pro-vegetarian score after weighting are presented in Table3. The pro-vegetarian score significantly increased over time (β=0.23,95% CI = [0.08, 0.37]), while the change in PEIPP was not significant (β=0.005, 95% CI = [-0.03, 0.04]). The size of the urban unit of residence was not significantly associated with either of the indicators, either at baseline or during follow-up. Being a man was positively associated with PEIPP, whereas the association between sex and the pro-vegetarian score was not significant. In addition, sex was not significantly associated with change over time of either indicator. PEIPP and the pro-vegetarian score were positively associated with age at baseline. Both indicators declined over time in subjects aged 55 to 64 and over 65. Educational level higher than or equal to an undergraduate degree was positively associated with PEIPP, while an educational level higher than or equal to high school graduation was positively associated with the pro-vegetarian score at baseline. Education was not significantly associated with a change over time of either indicator. Household composition was not significantly associated with the pro-vegetarian score at baseline, but individuals living alone had higher PEIPP than those living with a child. The provegetarian score decreased over time in persons living with at least one adult but without a child Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 13 compared to those living with a child. Compared to underweight or normal-weight participants, overweight subjects and those with obesity had lower PEIPP and pro-vegetarian score at baseline. In subjects who presented obesity at baseline, pro-vegetarian score significantly increased over time. Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8To the best of our knowledge, this is the first prospective study on consumption of plant-based foods and its associated characteristics. Also, our study uses of two complementary indicators assessing the contribution of plants to diet from a nutritional and a behavioural point of view, thereby rendering our results more robust.Our findings indicate that the percent energy intake provided by plant proteins, and the pro-vegetarian score, were positively associated with age and education at baseline, while they were inversely associated with weight status. During follow-up, the contribution of plant foods to overall diet decreased in older adults whereas pro-vegetarian score increased only in participants who presented obesity. Thus, very few individual characteristics influenced changes in plant-based food intake over time. This may be due to the slight variation in the contribution of plant-based foods to diet over time across population subgroups. A longer follow-up period might reveal a greater change in food consumption according to individual factors. Another explanation is that, in the context of the recent economic crisis, environmental factors such as food price may more strongly influence consumption of plant-based foods than individual characteristics[START_REF] Franceagrimer | Crise économique et comportements de consommation alimentaire des Français. Les cahiers de FranceAgriMer Les études 50[END_REF] 50].

  citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8

18 Conclusion 8 20

 188 [61]. Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 Our findings provide useful information on individual characteristics associated with the contribution of plant-based foods to the overall diet, and changes over time, associated with age, education and BMI. Further studies targeting specific subgroups known to have changed their intake of these foods are needed to understand their motivation to change and identify levers affecting the rebalance of the contribution of plant vs. animal foods in the diet. Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8

Figure 1 .

 1 Figure 1. Selection of the analysis sample from the NutriNet-Santé study cohort.

  The dietary record is completed via an interactive interface and is designed for self-administration on the dedicated website (www.etude-nutrinet-sante.fr)[36]. The accuracy of this web-based 24-h dietary record was assessed by comparing the record to interviews by trained dietitians [36] and to 24-h urinary biomarkers[37, 38]. The web-based dietary assessment method relied on a meal-based approach, recording all foods and beverages (type and quantity) consumed at breakfast, lunch, dinner and all other eating occasions. First, the participant fills in the names of all food items eaten. Next, he/she estimates portion sizes for each reported food and beverage according to standard measurements (e.g., home containers, grams indicated on the package) or using validated photographs available via the interactive interface. These photographs, taken from a validated illustrated booklet[39], represent more than 250 foods (corresponding to 1,000

	First, a nutritional indicator, the percentage of energy intake without alcohol, provided by plant proteins
	was computed as:
	Plant protein, % of energy =	plant protein (g) × 17 kJ energy intake without alcohol	× 100
	This indicator had been previously used in epidemiological studies [31-34], enabling comparison of
	our results with data from the literature. Plant protein intake was positively associated with overall
	nutrient adequacy, making a robust marker of health awareness, better compliance to dietary
	guidelines and quality of the diet [32, 41]. In addition, the structure of this indicator included the effect
	of energy intake, and thus did not require adjustment for this variable.
	The second indicator, a behavioural indicator, was a score of adherence to a "pro-vegetarian" food
	pattern elaborated by Martínez-González et al. [30]. Briefly, consumption (in g/day) of seven plant food
	groups (vegetables, fruits, legumes, cereals, potatoes, nuts, olive oil) and five animal food groups
	(meat and meat products, animal fat for cooking or as a spread, eggs, fish and seafood, dairy
	Comment citer ce document :
	Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot,

Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 7 weekdays and 1 weekend day) [35].

generic foods) served in seven different portion sizes. One dish could pertain to several groups if it was composed of several components. For each food group, components of composite dishes were proportionally accounted for, using French recipes validated by food and nutrition professionals.

Values for energy, macronutrients and micronutrients were estimated using published nutrient databases

[40]

.

In the present study, two indicators were used to assess the contribution of plant foods to the diet. E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort.

European Journal of Nutrition, 58

[START_REF] Micha | Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and metaanalysis[END_REF]

, 1991-2002. , DOI : 10.1007/s00394-018-1752-8

  22% lived in rural areas. Finally, 28% of individuals presented overweight and 10% presented obesity. At baseline, the mean percent energy intake provided by plant proteins (PEIPP) was 5.4% (SD: 0.1, range: 0.9 to 20.1) and the mean pro-vegetarian score was 35.4 (SD: 0.2, range: 16 to 55)

citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 12 and (Table

Table 2

 2

) as a large majority of obese participants in our sample (86%) reported dieting during follow-up. The energy intake decreased during the follow-up Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 16

  21 29. Vainio A, Niva M, Jallinoja P, Latvala T (2016) From beef to beans: Eating motives and the replacement of animal proteins with plant proteins among Finnish consumers. Appetite 106:92-100 . doi: 10.1016/j.appet.2016.03.002 30. Martínez-González MA, Sánchez-Tainta A, Corella D, et al (2014) A provegetarian food pattern and reduction in total mortality in the Prevención con Dieta Mediterránea (PREDIMED) study. Bolca S, Vandevijvere S, et al (2011) Dietary sources of animal and plant protein intake among Flemish preschool children and the association with socio-economic and lifestylerelated factors. Nutr J 10:97 . doi: 10.1186/1475-2891-10-97 34. Halkjaer J, Olsen A, Bjerregaard LJ, et al (2009) Intake of total, animal and plant proteins, and their food sources in 10 countries in the European Prospective Investigation into Cancer and Nutrition. Eur J Clin Nutr 63 Suppl 4:S16-36 . Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 23 58. Bujnowski D, Xun P, Daviglus ML, et al (2011) Longitudinal association between animal and vegetable protein intake and obesity among adult males in the United States: the Chicago Western Electric Study. J Am Diet Assoc 111:1150-1155.e1 . doi: 10.1016/j.jada.2011.05.002 59. Andreeva VA, Salanave B, Castetbon K, et al (2015) Comparison of the sociodemographic characteristics of the large NutriNet-Santé e-cohort with French Census data: the issue of Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort.

	Am J Clin Nutr 100 Suppl 1:320S-8S . doi: 10.3945/ajcn.113.071431 31. Lin Y, Bolca S, Vandevijvere S, et al (2011) Plant and animal protein intake and its association with overweight and obesity among the Belgian population. Br J Nutr 105:1106-1116 . doi: 10.1017/S0007114510004642 32. Camilleri GM, Verger EO, Huneau J-F, et al (2013) Plant and animal protein intakes are differently associated with nutrient adequacy of the diet of French adults. J Nutr 143:1466-1473 . doi: 10.3945/jn.113.177113 33. Lin Y, 2458-10-242 36. Touvier M, Kesse-Guyot E, Méjean C, et al (2011) Comparison between an interactive web-based self-administered 24 h dietary record and an interview by a dietitian for large-scale epidemiological studies. Br J Nutr 105:1055-1064 . doi: 10.1017/S0007114510004617 37. Lassale C, Castetbon K, Laporte F, et al (2015) Validation of a Web-based, self-administered, non-consecutive-day dietary record tool against urinary biomarkers. Br J Nutr 113:953-962 . doi: 10.1017/S0007114515000057 38. Lassale C, Castetbon K, Laporte F, et al (2016) Correlations between Fruit, Vegetables, Fish, Vitamins, and Fatty Acids Estimated by Web-Based Nonconsecutive Dietary Records and Respective Biomarkers of Nutritional Status. J Acad Nutr Diet 116:427-438.e5 . doi: 10.1016/j.jand.2015.09.017 39. Le Moullec N, Deheeger M, Preziosi P, et al (1996) Validation du manuel-photos utilisé pour l'enquête alimentaire de l'étude SU.VI.MAX. Cah Nutr Diététique 31:158-164 40. Arnault N, Caillot L, Castetbon K, et al (2013) Table de Composition des aliments NutriNet-Santé. Edition Économica, Paris, France 41. Bianchi CM, Egnell M, Huneau J-F, Mariotti F (2016) Plant Protein Intake and Dietary Diversity Are Independently Associated with Nutrient Adequacy in French Adults. J Nutr 146:2351-2360 . doi: 10.3945/jn.116.236869 42. Willett W (1998) Nutritional Epidemiology. Oxford University Press, USA, New York Comment citer ce document : 205263 60. Andreeva VA, Deschamps V, Salanave B, et al (2016) Comparison of Dietary Intakes Between a Large Online Cohort Study (Etude NutriNet-Santé) and a Nationally Representative Cross-Sectional Study (Etude Nationale Nutrition Santé) in France: Addressing the Issue of Generalizability in E-Epidemiology. Am J Epidemiol 184:660-669 . doi: 10.1093/aje/kww016 61. Wethington E (2005) An overview of the life course perspective: implications for health and Colombet, Z. (volunteer bias revisited. J Epidemiol Community Health 69:893-898 . doi: 10.1136/jech-2014-nutrition. J Nutr Educ Behav 37:115-120

doi: 10.1038/ejcn.2009.73 35. Hercberg S, Castetbon K, Czernichow S, et al (2010) The Nutrinet-Santé Study: a web-based prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status. BMC Public Health 10:242 . doi: 10.1186/1471-European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 24

Table 1 .

 1 Characteristics of the sample (n = 15,615).

	Raw data		Weighted data*	Census estimates**
	n	%	%	%

*** prevalence from a nationally representative study (Etude Nationale Nutrition Santé (ENNS) 2006-2007) Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 26

Table 2 .

 2 Comparison of the percent energy intake* provided by plant proteins and the pro-vegetarian score according to individual characteristics at baseline in French adults participating in the NutriNet-Santé cohort (n = 15,615)**.Comment * Sex-specific data weighted for age, occupational categories, area of residence, marital status and educational level, using 2009 national census. Student's t-test or analysis of variance (ANOVA) as appropriate. ** Percentage of daily energy intake without alcohol provided by plant proteins. † Score of adherence to a pro-vegetarian food pattern elaborated by Martínez-González et al. Energyadjusted estimates of consumption of seven plant and five animal food groups were ranked according to sex-specific quintiles. The quintiles were scored from 1 to 5 and these scores were summed up to obtain an overall score ranging from 12 (lowest adherence) to 60 (highest adherence).

	Percent energy intake
	provided by plant
	proteins***	Pro-vegetarian score †
	Mean ± SE p-value	Mean ± SE p-value

citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 27 * Energy intake without alcohol. **citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 28

Table 3 .

 3 Multivariate linear mixed analysis* showing associations between baseline individual characteristics, changes in percent energy intake** provided by plant proteins and the pro-vegetarian score up to 6 years in French adults participating in the NutriNet-Santé cohort (n = 15,615)***. ** Sex-specific data weighted for age, occupational categories, area of residence, marital status and educational level, using 2009 national census † Percentage of daily energy intake without alcohol provided by plant proteins. ‡ Score of adherence to a pro-vegetarian food pattern elaborated by Martínez-González et al. Energyadjusted estimates of consumption of seven plant and five animal food groups were ranked according to sex-specific quintiles. The quintiles were scored from 1 to 5 and these scores were summed up to obtain an overall score ranging from 12 (lowest adherence) to 60 (highest adherence).

		Percent energy intake from
		plant proteins †	Pro-vegetarian score ‡
		β (95% CI)	p-value	β (95% CI)	p-value
	Time	0.005 (-0.03, 0.04)	0.78	0.23 (0.08, 0.37)	0.002
	Sex
	Men	0.06 (0.01, 0.11)	0.01	-0.07 (-0.26, 0.12)	0.49
	Men*time	0.001 (-0.01, 0.01)	0.92	-0.04 (-0.10, 0.01)	0.10
	Age class
	18-29 years	Reference		Reference
	30-49 years	0.46 (0.39, 0.53) < 0.0001	1.57 (1.28, 1.86) < 0.0001
	30-49 years*time	-0.01 (-0.03, 0.004)	0.13	-0.04 (-0.13, 0.04)	0.30
	50-64 years	0.68 (0.61, 0.75) < 0.0001	2.66 (2.37, 2.94) < 0.0001
	50-64 years*time	-0.04 (-0.06, -0.02) < 0.0001	-0.09 (-0.17, -0.01)	0.04
	≥ 65 years	0.80 (0.71, 0.88) < 0.0001	3.30 (2.97, 3.64) < 0.0001
	≥ 65 years*time	-0.05 (-0.07, -0.03) < 0.0001	-0.13 (-0.22, -0.04)	0.01
	Educational level
	None or primary	Reference		Reference
	Secondary	-0.001 (-0.11, 0.11)	0.99	-0.01 (-0.44, 0.43)	0.98
	Secondary*time	0.01 (-0.02, 0.04)	0.44	-0.05 (-0.16, 0.07)	0.43
	High School graduate	0.09 (-0.02, 0.19)	0.12	0.47 (0.05, 0.89)	0.03
	High School graduate*time	0.003 (-0.02, 0.03)	0.80	-0.07 (-0.18, 0.03)	0.19
	Undergraduate	0.12 (0.01, 0.23)	0.03	0.65 (0.22, 1.08)	0.003
	Undergraduate*time	0.01 (-0.01, 0.04)	0.38	-0.08 (-0.19, 0.04)	0.18
	Postgraduate	0.23 (0.12, 0.34) < 0.0001	1.19 (0.75, 1.62) < 0.0001
	Postgraduate*time	-0.01 (-0.03, 0.02)	0.64	-0.07 (-0.18, 0.04)	0.22
	Household composition
	Living with a child	Reference		Reference
	Living alone	0.11 (0.02, 0.21)	0.02	0.05 (-0.34, 0.43)	0.82
	Living alone*time	0.0004 (-0.02, 0.02)	0.97	-0.05 (-0.15, 0.05)	0.31
	Living with at least one adult, but
	without a child	0.03 (-0.03, 0.09)	0.31	0.25 (-0.01, 0.51)	0.05
	Living with at least one adult, but
	without a child *time	-0.002 (-0.02, 0.01)	0.84	-0.07 (-0.14, -0.01)	0.03
	Size of urban unit of residence
	20,000 -200,000 inhabitants	Reference		Reference
	Rural	-0.04 (-0.1, 0.03)	0.25	-0.19 (-0.46, 0.08)	0.16
	Rural*time	-0.005 (-0.02, 0.01)	0.60	-0.01 (-0.09, 0.06)	0.79
	< 20,000 inhabitants	-0.02 (-0.09, 0.05)	0.53	-0.15 (-0.44, 0.14)	0.31
	< 20,000 inhabitants*time	-0.001 (-0.02, 0.02)	0.95	0.05 (-0.03, 0.13)	0.19
	> 200,000 inhabitants	-0.03 (-0.09, 0.03)	0.34	0.15 (-0.12, 0.41)	0.27
	> 200,000 inhabitants*time	-0.003 (-0.02, 0.01)	0.69	0.01 (-0.06, 0.08)	0.80

Comment citer ce document : Colombet, Z. (Auteur de correspondance), Allès, B., Si Hassen, W., Lampuré, A., Kesse-Guyot, E., Péneau, S., Hercberg, S., Méjean, C. (2019). Individual characteristics associated with changes in the contribution of plant foods to dietary intake in a French prospective cohort. European Journal of Nutrition, 58 (5), 1991-2002. , DOI : 10.1007/s00394-018-1752-8 ** Energy intake without alcohol. *

Cary, NC, USA).

Conflict of interest:

On behalf of all authors, the corresponding author states that there is no conflict of interest.