Céline Comte

Sélection de présentations de recherche

• Adaptive Load Balancing under Assignment Constraints
  Présentation invitée à la 2ème conférence NETWORKS. En ligne. Juin 2021.
  Résumé Slides

  The design and analysis of load-balancing algorithms has been a popular research topic over the past three decades. The basic problem consists of assigning a stream of incoming jobs to servers, so as to equalize the server loads as far as possible. The fundamental challenge consists of taking the best assignment decision upon the arrival of each job despite uncertainty on the length of ongoing and future jobs. However, the growing use of cloud-computing platforms for data-intensive applications has added further constraints on load balancing. For instance, the large scale of the system calls for decentralized algorithms that perform well in the presence of many dispatchers, and data locality may restrict the set of servers to which each job can be assigned.

  In this presentation, we consider a blocking variant of a well-known load-balancing algorithm, called join-idle-queue, whereby incoming jobs are rejected if no server is available upon their arrival. It was previously observed that, if jobs arrive according to a Poisson process, this blocking variant can be described by a Markov process with a product-form stationary distribution; furthermore, the long-run performance is insensitive to the job size distribution beyond its mean. We first introduce several extensions to this blocking variant that preserve these two properties while also accounting for practical constraints of cloud-computing platforms. We also show how these extensions relate to existing insensitive load-balancing algorithms, and use this observation to compute long-run performance metrics. These results give insights into the performance of the original join-idle-queue algorithm under the practical constraints of cloud-computing platforms.

• Gestion des Ressources dans les Grappes d’Ordinateurs: Conception d’Algorithmes et Analyse de Performance
  Prix de thèse de Télécom Paris pour l'année 2020. En ligne. Juillet 2020.
  Résumé Slides Vidéo

  La demande croissante pour les services de cloud computing encourage les opérateurs à optimiser l'utilisation des ressources dans les grappes d'ordinateurs. Cela motive le développement de nouvelles technologies qui rendent plus flexible la gestion des ressources. Cependant, exploiter cette flexibilité pour réduire le nombre d'ordinateurs nécessite aussi des algorithmes de gestion des ressources efficaces et dont la performance est prédictible sous une demande stochastique. Dans cette thèse, nous concevons et analysons de tels algorithmes en utilisant le formalisme de la théorie des files d'attente.

  Notre abstraction du problème est une file multi-serveur avec plusieurs classes de clients. Les capacités des serveurs sont hétérogènes et les clients de chaque classe entrent dans la file selon un processus de Poisson indépendant. Chaque client peut être traité en parallèle par plusieurs serveurs, selon des contraintes de compatibilité décrites par un graphe biparti entre les classes et les serveurs, et chaque serveur applique la politique premier arrivé, premier servi aux clients qui lui sont affectés. Nous prouvons que, si la demande de service de chaque client suit une loi exponentielle indépendante de moyenne unitaire, alors la performance moyenne sous cette politique simple est la même que sous l'équité équilibrée, une extension de processor-sharing connue pour son insensibilité à la loi de la demande de service. Une forme plus générale de ce résultat, reliant les files order-independent aux réseaux de Whittle, est aussi prouvée. Enfin, nous développons de nouvelles formules pour calculer des métriques de performance.

  Ces résultats théoriques sont ensuite mis en pratique. Nous commençons par proposer un algorithme d’ordonnancement qui étend le principe de round-robin à une grappe où chaque requête est affectée à un groupe d'ordinateurs par lesquels elle peut ensuite être traitée en parallèle. Notre seconde proposition est un algorithme de répartition de charge à base de jetons pour des grappes où les requêtes ont des contraintes d'affectation. Ces deux algorithmes sont approximativement insensibles à la loi de la taille des requêtes et s'adaptent dynamiquement à la demande. Leur performance peut être prédite en appliquant les formules obtenues pour la file multi-serveur.

• Resource Management in Computer Clusters: Algorithm Design and Performance Analysis
  Soutenance de thèse. LINCS, Paris, France. Septembre 2019.
  Résumé Slides Vidéo Manuscrit

  The growing demand for cloud-based services encourages operators to maximize resource efficiency within computer clusters. This motivates the development of new technologies that make resource management more flexible. However, exploiting this flexibility to reduce the number of computers also requires efficient resource-management algorithms that have a predictable performance under stochastic demand. In this thesis, we design and analyze such algorithms using the framework of queueing theory.

  Our abstraction of the problem is a multi-server queue with several customer classes. Servers have heterogeneous capacities and the customers of each class enter the queue according to an independent Poisson process. Each customer can be processed in parallel by several servers, depending on compatibility constraints described by a bipartite graph between classes and servers, and each server applies first-come-first-served policy to its compatible customers. We first prove that, if the service requirements are independent and exponentially distributed with unit mean, this simple policy yields the same average performance as balanced fairness, an extension to processor-sharing known to be insensitive to the distribution of the service requirements. A more general form of this result, relating order-independent queues to Whittle networks, is also proved. Lastly, we derive new formulas to compute performance metrics.

  These theoretical results are then put into practice. We first propose a scheduling algorithm that extends the principle of round-robin to a cluster where each incoming job is assigned to a pool of computers by which it can subsequently be processed in parallel. Our second proposal is a load-balancing algorithm based on tokens for clusters where jobs have assignment constraints. Both algorithms are approximately insensitive to the job size distribution and adapt dynamically to demand. Their performance can be predicted by applying the formulas derived for the multi-server queue.

• Dynamic Load Balancing with Tokens
  Workshop Young European Queueing Theorists (YEQT) XII. ENSEEIHT, Toulouse, France. Decembre 2018.
  Séminaire de l'équipe POLARIS. INRIA Grenoble, France. Novembre 2018.
  Résumé Slides Papier

  Efficiently exploiting the resources of data centers is a complex task that requires efficient and reliable load balancing and resource allocation algorithms. The former are in charge of assigning jobs to servers upon their arrival in the system, while the latter are responsible for sharing the server resources between their assigned jobs. These algorithms should adapt to various constraints, such as data locality, that restrict the feasible job assignments. In this présentation, we propose a token-based algorithm that efficiently balances the load between the servers without requiring any knowledge on the job arrival rates and server capacities. Assuming a balanced fair sharing of the server resources, we show that the resulting dynamic load balancing is insensitive to the job size distribution. Its performance is compared to that obtained under the best static load balancing and in an ideal system that would constantly optimize the resource utilization. We also make the connection with other token-based algorithms such as Join-Idle-Queue.

• Performance of Balanced Fairness in Resource Pools: A Recursive Approach
  Eindhoven Stochastics Seminar. Université Technologique d'Eindhoven, Pays-Bas. Mars 2018.
  Séminaire Science Park Informal Probability Meetings. Université d'Amsterdam (UvA), Pays-Bas. Mars 2018.
  Séminaire du LINCS. LINCS, Paris, France. Janvier 2018.
  Résumé Slides Vidéo Papier

  Understanding the performance of a pool of servers is crucial for proper dimensioning. One of the main challenges is to take into account the complex interactions between servers that are pooled to process jobs. In particular, a job can generally not be processed by any server of the cluster due to various constraints like data locality. In this talk, we will represent these constraints by some assignment graph between jobs and servers. We will present a recursive approach to computing performance metrics like mean response times when the server capacities are shared according to balanced fairness. While the computational cost of these formulas can be exponential in the number of servers in the worst case, we will illustrate their practical interest by introducing broad classes of pool structures that can be exactly analyzed in polynomial time. This extends considerably the class of models for which explicit performance metrics are accessible.

• Networks of Multi-Server Queues with Parallel Processing
  Workshop Young European Queueing Theorists (YEQT) X. Eurandom, Eindhoven, Pays-Bas. Novembre 2016.
  Résumé Slides Papier

  We consider a network of multi-class multi-server queues. Each job can be processed in parallel by any subset of servers within a pre-defined set that depends on its class. Each server is allocated in FCFS order at each queue. Jobs arrive according to Poisson processes, have independent exponential service requirements and are routed independently at random. We prove that, when stable, the network has a product-form stationary distribution. From a practical perspective, we propose an algorithm on this basis to allocate the resources of a computer cluster according to balanced fairness. We finally examine further developments of this model to allow for dynamic adaptation to the system occupancy.

Groupes de lecture

• Exponential families
  Groupe de lecture Network Theory du LINCS. En ligne. Avril 2021.
  Résumé Slides Vidéo

  Exponential families are parametric sets of probability distributions that arise in many applications. These include well-known univariate distributions (such as the binomial, Poisson, geometric, exponential, and normal distributions), but also multi-variate distributions like probabilistic graphical models and stationary distributions of several queueing models. In this presentation, we will first recall the definition of exponential families and motivate their study. In a second time, we will present a generic method for approximating the normalization constant of these distributions, as the exact calculation of this constant is practically infeasible in high dimension.

  Basé sur les références suivantes :

  • Graphical Models, Exponential Families, and Variational Inference. M. J. Wainwright and M. I. Jordan (2008). Lien vers le livre.
  • Convex Optimization. S. Boyd and L. Vandenberghe (2004). Lien vers le livre.
  • Pages Wikipédia Exponential family, Maximum-entropy probability distribution, Principle of maximum entropy, Lagrange multiplier, and Convex conjugate.
• Manipulating and Analyzing Data with Pandas
  Groupe de travail Python Workshop du LINCS. Paris, France. Mai 2019.
  Résumé Slides Notebook Jupyter

  The Python Data Analysis Library (pandas) provides data structures and tools for manipulating and analyzing data. It is built on top of NumPy, the core package for numerical computations in the Scientific Computing Tools for Python (SciPy) ecosystem. The objective of this presentation is threefold: explain when one can benefit from using pandas, describe its fundamental data structures, and give an overview of the data analysis tools it provides.

• Belief Propagation in Bayesian Networks
  Groupe de lecture Network Theory du LINCS. Paris, France. Novembre 2018.
  Résumé Slides

  Basé sur les références suivantes :

  • "Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach". J. Pearl. Proceedings of AAAI’82 (1982).
  • "Fusion, propagation, and structuring in belief networks". J. Pearl. Artificial Intelligence, Elsevier (1986).
• Analyzing the M/G/1 Queue with a Branching Process
  Gropue de lecture Network Theory du LINCS. Paris, France. Décembre 2017.
  Résumé Slides

  Basé sur les références suivantes :

  • D.G. Kendall, "Some Problems in the Theory of Queues". Journal of the Royal Statistical Society. (1951).
  • D.G. Kendall, "Stochastic processes occurring in the theory of queues and their analysis by the method of the embedded Markov chain". The Annals of Mathematical Statistics (1953).

Revues et manifestations d'audience internationale avec comité de sélection

• Performance Evaluation of Stochastic Bipartite Matching Models. C. Comte and J.L. Dorsman
  ASMTA 2021 / Lecture Notes in Computer Science, Springer (2021)
  Résumé Papier

  We consider a stochastic bipartite matching model consisting of multi-class customers and multi-class servers. Compatibility constraints between the customer and server classes are described by a bipartite graph. Each time slot, exactly one customer and one server arrive. The incoming customer (resp. server) is matched with the earliest arrived server (resp. customer) with a class that is compatible with its own class, if there is any, in which case the matched customer-server couple immediately leaves the system; otherwise, the incoming customer (resp. server) waits in the system until it is matched. Contrary to classical queueing models, both customers and servers may have to wait, so that their roles are interchangeable. While (the process underlying) this model was already known to have a product-form stationary distribution, this paper derives a new compact and manageable expression for the normalization constant of this distribution, as well as for the waiting probability and mean waiting time of customers and servers. We also provide a numerical example and make some important observations.

• Stochastic Non-Bipartite Matching Models and Order-Independent Loss Queues. C. Comte
  Stochastic Models, Taylor & Francis (2021)
  Résumé Papier

  The need for matching items with one another while meeting assignment constraints or preferences has given rise to several well-known problems like the stable marriage and roommate problems. While most of the literature on matching problems focuses on a static setting with a fixed number of items, several recent works incorporated time by considering stochastic models, in which items of different classes arrive according to independent Poisson processes and assignment constraints are described by an undirected non-bipartite graph on the classes. In this paper, we prove that the continuous-time Markov chain associated with this model has the same transition diagram as in a product-form queueing model called an order-independent loss queue. This allows us to adapt existing results on order-independent (loss) queues to stochastic matching models, and in particular to derive closed-form expressions for several performance metrics, like the waiting probability or the mean matching time, that can be implemented using dynamic programming. Both these formulas and the numerical results that they allow us to derive are used to gain insight into the impact of parameters on performance. In particular, we characterize performance in a so-called heavy-traffic regime in which the number of items of a subset of the classes goes to infinity while items of other classes become scarce.

• Load Balancing in Heterogeneous Server Clusters: Insights From a Product-Form Queueing Model. M. van der Boor and C. Comte
  IWQoS 2021 / Proceedings of the 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), IEEE (2021)
  Résumé Papier

  Efficiently exploiting servers in data centers requires performance analysis methods that account not only for the stochastic nature of demand but also for server heterogeneity. Although several recent works proved optimality results for heterogeneity-aware variants of classical load-balancing algorithms in the many-server regime, we still lack a fundamental understanding of the impact of heterogeneity on performance in finite-size systems. In this paper, we consider a load-balancing algorithm that leads to a product-form queueing model and can therefore be analyzed exactly even when the number of servers is finite. We develop new analytical methods that exploit its product-form stationary distribution to understand the joint impact of the speeds and buffer lengths of servers on performance. These analytical results are supported and complemented by numerical evaluations that cover a large variety of scenarios.

• Pass-and-Swap Queues. C. Comte et J.L. Dorsman.
  Queueing Systems: Theory and Applications, Springer (2021)
  Résumé Papier

  Order-independent (OI) queues, introduced by Berezner et al. (Queueing Syst 19(4):345–359, 1995), expanded the family of multi-class queues that are known to have a product-form stationary distribution by allowing for intricate class-dependent service rates. This paper further broadens this family by introducing pass-and-swap (P&S) queues, an extension of OI queues where, upon a service completion, the customer that completes service is not necessarily the one that leaves the system. More precisely, we supplement the OI queue model with an undirected graph on the customer classes, which we call a swapping graph, such that there is an edge between two classes if customers of these classes can be swapped with one another. When a customer completes service, it passes over customers in the remainder of the queue until it finds a customer it can swap positions with, that is, a customer whose class is a neighbor in the graph. In its turn, the customer that is ejected from its position takes the position of the next customer it can be swapped with, and so on. This is repeated until a customer can no longer find another customer to be swapped with; this customer is the one that leaves the queue. After proving that P&S queues have a product-form stationary distribution, we derive a necessary and sufficient stability condition for (open networks of) P&S queues that also applies to OI queues. We then study irreducibility properties of closed networks of P&S queues and derive the corresponding product-form stationary distribution. Lastly, we demonstrate that closed networks of P&S queues can be applied to describe the dynamics of new and existing load-distribution and scheduling protocols in clusters of machines in which jobs have assignment constraints.

• Kleinberg's Grid Unchained. C. Comte et F. Mathieu
  Theoretical Computer Science, Elsevier (2020)
  Résumé Papier Résultats numériques

  One of the key features of small-world networks is the ability to route messages in a few hops, using a decentralized algorithm in which each node has a limited knowledge of the topology. In 2000, Kleinberg proposed a model based on an augmented grid that asymptotically exhibits such a property.

  In this paper, we revisit the original model with the help of numerical simulations. Our approach is fueled by a new algorithm that can sample augmenting links in an almost constant time. The speed gain allows us to perform detailed numerical evaluations. We first observe that, in practice, the augmented scheme proposed by Kleinberg is more robust than what is predicted by the asymptotic behavior, even in very large finite grids. We also propose tighter bounds on the asymptotic performance of Kleinberg's greedy routing algorithm. We finally show that, if the model is fed with realistic parameters, the results are in line with real-life experiments.

• Dynamic Load Balancing with Tokens (Version étendue). C. Comte
  Computer Communications, Elsevier (2019)
  Résumé Papier

  Efficiently exploiting the resources of data centers is a complex task that requires efficient and reliable load balancing and resource allocation algorithms. The former are in charge of assigning jobs to servers upon their arrival in the system, while the latter are responsible for sharing the server resources between their assigned jobs. These algorithms should adapt to various constraints, such as data locality, that restrict the feasible job assignments. In this paper, we propose a token-based algorithm that efficiently balances the load between the servers without requiring any knowledge on the job arrival rates and the server capacities. Assuming a balanced fair sharing of the server resources, we show that the resulting dynamic load balancing is insensitive to the job size distribution. Its performance is compared to that obtained under the best static load balancing and in an ideal system that would constantly optimize the resource utilization. We also make the connection with other token-based algorithms such as Join-Idle-Queue.

• Dynamic Load Balancing with Tokens. C. Comte
  IFIP NETWORKING 2018 / Proceedings of the 17th International IFIP TC6 Networking Conference, IFIP Open Digital Library (2018)
  Résumé Papier Rapport Slides

  Efficiently exploiting the resources of data centers is a complex task that requires efficient and reliable load balancing and resource allocation algorithms. The former are in charge of assigning jobs to servers upon their arrival in the system, while the latter are responsible for sharing server resources between their assigned jobs. These algorithms should take account of various constraints, such as data locality, that restrict the feasible job assignments. In this paper, we propose a token-based mechanism that efficiently balances load between servers without requiring any knowledge on job arrival rates and server capacities. Assuming a balanced fair sharing of the server resources, we show that the resulting dynamic load balancing is insensitive to the job size distribution. Its performance is compared to that obtained under the best static load balancing and in an ideal system that would constantly optimize the resource utilization.

• Of Kernels and Queues: When Network Calculus Meets Analytic Combinatorics. A. Bouillard, C. Comte, E. de Panafieu et F. Mathieu
  NetCal 2018 / Proceedings of the 2018 30th International Teletraffic Congress (2018)
  Résumé Papier Résultats numériques

  Stochastic network calculus is a tool for computing error bounds on the performance of queueing systems. However, deriving accurate bounds for networks consisting of several queues or subject to non-independent traffic inputs is challenging. In this paper, we investigate the relevance of the tools from analytic combinatorics, especially the kernel method, to tackle this problem. Applying the kernel method allows us to compute the generating functions of the queue state distributions in the stationary regime of the network. As a consequence, error bounds with an arbitrary precision can be computed. In this preliminary work, we focus on simple examples which are representative of the difficulties that the kernel method allows us to overcome.

• Performance of Balanced Fairness in Resource Pools: A Recursive Approach. T. Bonald, C. Comte et F. Mathieu
  ACM SIGMETRICS 2018 / Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS), ACM (2017)
  Résumé Papier Slides Vidéo

  Understanding the performance of a pool of servers is crucial for proper dimensioning. One of the main challenges is to take into account the complex interactions between servers that are pooled to process jobs. In particular, a job can generally not be processed by any server of the cluster due to various constraints like data locality. In this paper, we represent these constraints by some assignment graph between jobs and servers. We present a recursive approach to computing performance metrics like mean response times when the server capacities are shared according to balanced fairness. While the computational cost of these formulas can be exponential in the number of servers in the worst case, we illustrate their practical interest by introducing broad classes of pool structures that can be exactly analyzed in polynomial time. This extends considerably the class of models for which explicit performance metrics are accessible.

• Balanced Fair Resource Sharing in Computer Clusters. T. Bonald et C. Comte
  Performance Evaluation, Elsevier (2017)
  Résumé Papier Slides

  We represent a computer cluster as a multi-server queue with some arbitrary graph of compatibilities between jobs and servers. Each server processes its jobs sequentially in FCFS order. The service rate of a job at any given time is the sum of the service rates of all servers processing this job. We show that the corresponding queue is quasi-reversible and use this property to design a scheduling algorithm achieving balanced fair sharing of the computing resources.

• Poly-Symmetry in Processor-Sharing Systems. T. Bonald, C. Comte, V. Shah et G. de Veciana
  ECQT 2016 / Queueing Systems: Theory and Applications, Springer Verlag (2017)
  Résumé Papier Slides

  We consider a system of processor-sharing queues with state-dependent service rates. These are allocated according to balanced fairness within a polymatroid capacity set. Balanced fairness is known to be both insensitive and Pareto-efficient in such systems, which ensures that the performance metrics, when computable, will provide robust insights into the real performance of the system considered. We first show that these performance metrics can be evaluated with a complexity that is polynomial in the system size if the system is partitioned into a finite number of parts, so that queues are exchangeable within each part and asymmetric across different parts. This in turn allows us to derive stochastic bounds for a larger class of systems which satisfy less restrictive symmetry assumptions. These results are applied to practical examples of tree data networks, such as backhaul networks of Internet service providers, and computer clusters.

• The Multi-Source Model for Dimensioning Data Networks. T. Bonald et C. Comte
  Computer Networks, Elsevier (2016)
  Résumé Papier

  Traffic modeling is key to the dimensioning of data networks. Usual models rely on the implicit assumption that each user generates data flows in series, one after the other, the ongoing flows sharing equitably the considered network link. We relax this assumption and consider the more realistic case where users may generate several data flows in parallel, these flows having to share the user's access line as well. We qualify this model as multi-source since each user now behaves as an independent traffic source. Usual performance metrics like mean throughput and congestion rate must now be defined at user level rather than at flow level. We derive explicit expressions for these performance metrics under the assumption that flows share bandwidth according to balanced fairness. These results are compared with those obtained by simulation when max-min fairness is imposed, either at flow level or at user level.

Revues et manifestations d'audience nationale avec comité de sélection

• Modèle de couplage stochastique non-biparti. C. Comte
  AlgoTel 2021 – 23èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications
  Résumé Papier Slides

  Alors que la plus grande partie de la littérature sur les problèmes de couplage considère des modèles statiques où le nombre d'éléments à coupler est donné, plusieurs travaux récents se sont intéressés à une variante stochastique où des éléments de différentes classes arrivent selon des processus de Poisson indépendants, les contraintes de couplage entre ces éléments étant décrites par un graphe non-biparti entre leurs classes. Ces éléments peuvent notamment représenter des pièces à assembler dans une chaîne de production ou des couples donneur-receveur sur une liste de don d'organes. Dans ce papier, nous développons de nouvelles formules pour calculer des métriques de performance comme le temps moyen d'attente. Ces formules, ainsi que les résultats numériques qu'elles nous permettent de tracer, sont utilisées pour mieux comprendre l'impact des paramètres sur la performance. Nous caractérisions en particulier un régime limite où les éléments de certaines classes s'accumulent alors que les éléments des autres classes deviennent rares.

• Rien ne sert de prédire ; il faut servir ancien. C. Comte
  AlgoTel 2019 – 21èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications
  Résumé Papier Slides

  Optimiser l’utilisation des centres de données requiert des algorithmes efficaces et fiables pour répartir la charge entre les machines en s’adaptant à des contraintes variées, telles que la localité des données, qui restreignent les affectations possibles. Dans ce papier, nous proposons un algorithme simple qui réalise cette tâche sans avoir besoin de connaître le taux d’arrivée des requêtes ni la capacité des machines. Sa propriété essentielle, vérifiée à l’aide d’un modèle de files d’attente, est son insensibilité à la loi de la taille des requêtes : si chaque machine applique la discipline de service processor sharing et si les requêtes arrivent selon un processus de Poisson, alors la performance ne dépend de la loi de la taille des requêtes que par l’intermédiaire de sa moyenne. De premiers résultats numériques évaluant les taux de rejet des requêtes et d’occupation des machines indiquent de plus que cet algorithme surpasse le meilleur algorithme statique et est proche d’un algorithme idéal qui optimiserait constamment l’utilisation des ressources.

• 0 = 0, c'est le truc du noyau ! Application aux files d'attente. A. Bouillard, C. Comte, E. de Panafieu et F. Mathieu
  AlgoTel 2019 – 21èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications
  Résumé Papier

  Le calcul réseau stochastique est un outil de calcul de bornes d’erreur sur la performance des réseaux de files d’attente. Obtenir des bornes précises pour des réseaux constitués de plusieurs files ou soumis à des arrivées non-indépendantes est un exercice délicat. Dans ce papier, nous évaluons la pertinence des outils de combinatoire analytique pour aborder ce problème. Nous appliquons en particulier la méthode du noyau pour exprimer les fonctions génératrices des distributions des états des files, ce qui nous permet de calculer des bornes d’erreur d’une précision arbitraire sur le comportement à l’état stationnaire. Dans ce travail préliminaire, nous nous focalisons sur des exemples simples mais représentatifs des difficultés que la méthode du noyau permet de surmonter. Ces résultats sont validés par des simulations.

• Un seul serveur vous manque, et tout est découplé ! T. Bonald, C. Comte et F. Mathieu
  AlgoTel 2018 – 20èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications
  Résumé Papier Slides

  Les infrastructures de cloud computing reposent sur des grappes de serveurs qui se partagent des requêtes. Leur dimensionnement nécessite de prédire leurs performances. L’un des principaux défis consiste à tenir compte des interactions complexes entre serveurs lorsqu’ils sont mis en commun pour traiter des requêtes en parallèle, en intégrant certaines contraintes comme la localité des données qui restreignent les affectations possibles. Pour les analyser, nous représentons ces contraintes par un graphe d’affectation entre les requêtes et les serveurs ; les ressources sont partagées selon l’équité équilibrée, qui a l’avantage de rendre les performances du système insensibles à la distribution de la taille des requêtes. Notre principale contribution est une nouvelle approche récursive pour calculer les métriques de performance, consistant à décomposer le système en éteignant les serveurs les uns après les autres. Même si la complexité des formules obtenues peut être exponentielle en la taille de la grappe dans le pire cas, nous illustrons leur intérêt pratique en identifiant de vastes familles de structures pour lesquelles elle devient polynomiale. Ceci étend considérablement l’ensemble des systèmes pour lesquels des métriques de performance explicites sont accessibles.

• La Grille de Kleinberg, l’univers et le reste. C. Comte et F. Mathieu
  AlgoTel 2017 – 19èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications
  Prix du meilleur papier étudiant à AlgoTel 2017
  Résumé Papier Slides

  Longtemps source d’anecdotes, le routage dans les petits mondes a été étudié en 2000 par Kleinberg grâce à un modèle simple qui capture l’influence de la construction de liens distants, les raccourcis, sur la navigabilité dans un graphe : la grille de Kleinberg. Tandis que le modèle initial a été enrichi par de nombreuses analyses asymptotiques et extensions, les résultats numériques sur des grilles simulées sont limités par la complexité du calcul des raccourcis. La contribution principale de l’article est l’introduction d’une nouvelle méthode de tirage par double rejet dynamique qui permet une analyse numérique détaillée. Il devient possible de choisir comme taille de la grille de Kleinberg l’univers, et le reste de l’article montre quelques applications : mise en évidence d’une certaine robustesse du routage ; proposition de nouvelles bornes asymptotiques ; observation des six degrés de séparation.

• À la racine du parallélisme. T. Bonald, C. Comte et F. Mathieu
  AlgoTel 2017 – 19èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications
  Résumé Papier Rapport Slides

  Nous considérons un cluster de serveurs traitant des requêtes en parallèle. Si les clients ont en général intérêt à ce que leurs requêtes soient traitées par le plus grand nombre de serveurs, l'impact du parallélisme sur les serveurs est moins clair : trop faible, il ne permet pas d'utiliser pleinement les ressources disponibles ; trop fort, il risque d'encombrer inutilement les serveurs de requêtes en attente. Nous étudions ce phénomène à l'aide d'un modèle de files d'attente où les requêtes arrivent selon un processus de Poisson et requièrent des traitements dont le volume suit une loi exponentielle. Chaque nouvelle requête est affectée à un certain nombre de serveurs, choisis de manière aléatoire, uniforme, et indépendante de l'état du système. Chaque serveur traite ses requêtes dans leur ordre d'arrivée. Nous montrons qu'il existe un degré de parallélisme qui minimise le nombre moyen de requêtes présentes dans chaque serveur. Ce degré optimal est de l'ordre de la racine carrée du nombre de serveurs pour une charge faible à modérée, et décroît jusqu'à deux à très forte charge.

Ph.D. thesis

• Resource Management in Computer Clusters: Algorithm Design and Performance Analysis. C. Comte
  Institut Polytechnique de Paris (2019)
  Premier prix au concours de thèse de Télécom Paris pour l'année 2020
  Résumé Manuscrit Slides Vidéo

  The growing demand for cloud-based services encourages operators to maximize resource efficiency within computer clusters. This motivates the development of new technologies that make resource management more flexible. However, exploiting this flexibility to reduce the number of computers also requires efficient resource-management algorithms that have a predictable performance under stochastic demand. In this thesis, we design and analyze such algorithms using the framework of queueing theory.

  Our abstraction of the problem is a multi-server queue with several customer classes. Servers have heterogeneous capacities and the customers of each class enter the queue according to an independent Poisson process. Each customer can be processed in parallel by several servers, depending on compatibility constraints described by a bipartite graph between classes and servers, and each server applies first-come-first-served policy to its compatible customers. We first prove that, if the service requirements are independent and exponentially distributed with unit mean, this simple policy yields the same average performance as balanced fairness, an extension to processor-sharing known to be insensitive to the distribution of the service requirements. A more general form of this result, relating order-independent queues to Whittle networks, is also proved. Lastly, we derive new formulas to compute performance metrics.

  These theoretical results are then put into practice. We first propose a scheduling algorithm that extends the principle of round-robin to a cluster where each incoming job is assigned to a pool of computers by which it can subsequently be processed in parallel. Our second proposal is a load-balancing algorithm based on tokens for clusters where jobs have assignment constraints. Both algorithms are approximately insensitive to the job size distribution and adapt dynamically to demand. Their performance can be predicted by applying the formulas derived for the multi-server queue.

Poster

• A Round-Robin Scheduling for Computer Clusters with Compatibility Constraints. T. Bonald et C. Comte
  Présenté à l'école d'été RESCOM 2017
  Résumé Poster

• Membre de l'école de recherche néerlandaise Beta Research School for Operations Management and Logistics.
• Première place au Prix de Thèse de Télécom Paris pour l'année 2020.
• Prix du meilleur papier étudiant à la conférence AlgoTel 2017.

Implication dans la communauté

• Membre du TPC des conférences
  • AlgoTel 2022,
  • IFIP Performance 2021,
  • ASMTA 2021,
  • AlgoTel 2021,
  • IFIP Performance 2020.
• Reviewer pour les journaux
  • Performance Evaluation,
  • Queueing Systems: Theory and Applications,
  • Transactions on Networking.
• Co-organisatrice du workshop Young European Queueing Theorists (YEQT) XIV.
• Co-organisatrice du groupe de lecture Stochastic Networks and Learning dans le groupe Stochastic Operations Research à l'Université Technologique d'Eindhoven (depuis October 2020).

Mobilités

• Université Technologique d'Eindhoven, Pays-Bas. Février–Avril 2018 (sept semaines).
  Collaboration avec Sem Borst et Mark van der Boor.
  
• ENSEEIHT, Toulouse, France. Décembre 2018 (deux semaines).
  Collaboration avec Urtzi Ayesta et Maaike Verloop.

Université Technologique d'Eindhoven

• 2DF20 Stochastics and Simulation for Finance (2021–2022, 2020–2021)
  • Troisième année de licence.
  • En collaboration avec Marko Boon.
• 2WB40 Queueing Systems (2020–2021, 2019–2020)
  • Troisième année de licence.
  • En collaboration avec Sem Borst, Jacques Resing, et Rik Timmerman.
  • Basé sur les notes de lecture Queueing Systems par I. Adan et J. Resing.
  • Notes de cours supplémentaires : Networks of queues.
• 2WB60 Stochastic Performance Modeling (2020–2021, 2019–2020)
  • Troisième année de licence.
  • En collaboration avec Stella Kapodistria.
  • Basé sur le livre Introduction to Modeling and Analysis of Stochastic Systems par V.G. Kulkarni.

Université Paris-Saclay

• ACN903 Propagation in Graphs (2018–2019, 2017–2018, 2016–2017)
  • Deuxième année du master Advanced Communication Networks (ACN).
  • En collaboration avec Fabien Mathieu.
  • Des notebooks Jupyter pour les travaux pratiques en Python sont disponibles sur GitHub.
  • Quelques slides : Markov chains et Power laws.

Télécom Paris

• MDI230 Théorie des files d'attente (2017–2018)
  • Première année de master.
  • En collaboration avec Anaïs Vergne.
  • Quelques notes de cours : Introduction à la théorie des files d'attente et Applications des chaînes de Markov : l'algorithme PageRank.