Search

This report considers different pricing models for a platform based rental system, such as Airbnb. A linear model is assumed for the demand response to price, and existence and uniqueness conditions for Nash equilibria are obtained. The Stackelberg equilibrium prices for the game are also obtained, and an iterative scheme is provided, which converges to the Nash equilibrium. Different cooperative pricing schemes are studied, and splitting of revenues based on the Shapley value is discussed. It is shown that a division of revenue based on the Shapley value gives a revenue to the platform proportional to its control of the market. The demand response function is modified to include user response to quality of service. It is shown that when the cost to provide quality of service is low, both renter and the platform will agree to maximize the quality of service. However, if this cost is high, they may not always be able to agree on what quality of service to provide.; Cet article examine différents modèles de tarification pour un système de location basé sur une plateforme, comme Airbnb. Un modèle linéaire est supposé pour la réponse de la demande au prix, et des conditions d’existence et d’unicité pour les équilibres de Nash sont obtenues. Les prix d’équilibre de Stackelberg pour le jeu sont également obtenus, et un schéma itératif est fourni, qui converge vers l’équilibre de Nash. Différents systèmes de tarification coopérative sont étudiés et la répartition des revenus basée sur la valeur de Shapley est discutée. Il est montré qu’une répartition des revenus basée sur la valeur de Shapley donne un revenu à la plateforme proportionnel à son contrôle du marché. La fonction de réponse à la demande est modifiée pour inclure la réponse de l’utilisateur à la qualité de service. Il est démontré que lorsque le coût de la qualité de service est faible, le locataire et la plateforme s’accordent pour maximiser la qualité de service. Cependant, si ce coût est élevé, ils ne peuvent pas toujours s’entendre sur la qualité de service à fournir.

International audience; This paper considers different pricing models for a platform based rental system, such as Airbnb. A linear model is assumed for the demand response to price, and existence and uniqueness conditions for Nash equilibria are obtained. The Stackelberg equilibrium prices for the game are also obtained, and an iterative scheme is provided, which converges to the Nash equilibrium. Different cooperative pricing schemes are studied, and splitting of revenues based on the Shapley value is discussed. It is shown that a division of revenue based on the Shapley value gives a revenue to the platform proportional to its control of the market. The demand response function is modified to include user response to quality of service. It is shown that when the cost to provide quality of service is low, both renter and the platform will agree to maximize the quality of service. However, if this cost is high, they may not always be able to agree on what quality of service to provide.

International audience; We consider a peer-to-peer electricity marketmodeled as a network game, where End Users (EUs) minimizetheir cost by computing their demand and generation whilesatisfying a set of local and coupling constraints. Their nominaldemand constitutes sensitive information, that they might wantto keep private. We prove that the network game admits aunique Variational Equilibrium, which depends on the privateinformation of all the EUs. A data aggregator is introduced,which aims to learn the EUs’ private information. The EUsmight have incentives to report biased and noisy readings topreserve their privacy, which creates shifts in their strategies.Relying on performative prediction, we define a decision-dependentgame G^stoch to couple the network game with adata market. Two variants of the Repeated Stochastic GradientMethod (RSGM) are proposed to compute the PerformativelyStable Equilibrium solution of G^stoch, that outperform RSGMwith respect to efficiency gap minimization, privacy preservation,and convergence rates in numerical simulations.

Modern portable devices can execute increasingly sophisticated AI models on sensed data. The complexity of such processing tasks is data-dependent and has relevant energy cost. This work develops an Age of Information markovian model for a system where multiple battery-operated devices perform data processing and energy har- vesting in parallel. Part of their computational burden is offloaded to an edge server which polls devices at given rate. The structural properties of the optimal policy for a single device-server system are derived. They permit to derive a new model-free reinforcement learning method specialized for monotone policies, namely Ordered Q-Learning, providing a fast procedure to learn the optimal policy. The method is oblivious to the devices’ battery capacities, the cost and the value of data batch processing and to the dynamics of the energy harvesting process. Finally, the polling strategy of the server is optimized by combining such policy improvement techniques with stochastic approximation methods. Extensive numerical results provide insight into the system properties and demonstrate that the proposed learning algorithms outperform existing baselines.

International audience; In this paper, we consider the problem of learning a correlated equilibrium of a finite non-cooperative game and show a new adaptive heuristic, called Correlated Perturbed Regret Minimization (CPRM) for this purpose. CPRM combines regret minimization to approach the set of correlated equilibria and a simple device suggesting actions to the players to further stabilize the dynamic. Numerical experiments support the hypothesis of the pointwise convergence of the empirical distribution over action profiles to an approximate correlated equilibrium with all players following the devices' suggestions. Additional simulation results suggest that CPRM is adaptive to changes in the game such as departures or arrivals of players.

This paper provides a complete characterization of the information-energy region of simultaneous information and energy transmission over an additive white Gaussian noise channel in the finite block-length regime with finite sets of channel input symbols. Given a set of channel input symbols, the converse characterizes the tuples of information rate, energy rate, decoding error probability (DEP) and energy outage probability (EOP) that cannot be achieved by any code built using the given set of channel inputs. A novel method for constructing a family of codes that respects the given information rate, energy rate, DEP and EOP requirements is proposed. The achievable region identifies the set of tuples of information rate, energy rate, DEP and EOP that can be achieved by the constructed family of codes. The proposed construction proves to be information rate, energy rate, and EOP optimal. The achieved DEP is, however, sub-optimal, owing to the choice of the decoding regions made during the construction.

International audience; The integration of RIS elements in upcoming 6G networks appears a major breakthrough to extend network coverage and capacity. This paper introduces a new link-layer scheme for RIS-enabled communications building on existing models for the physical layer of RIS technologies. The scheme is able to integrate the selection of precoders/beams in a codebook and scheduling of UEs at once. Furthermore, elements for the integration of the proposed scheme in current 3GPP-5G specifications are addressed. The scheduler combines a slow mechanism operating at the downlink OFDMA frame scale with a standard proportional fair scheduler operating at the OFDMA slot scale and accommodating both LOS and non-LOS UEs. We introduce an optimization framework for the proposed scheduler whose performance is hence simulated in a reference scenario. The spectral efficiency figures in our tests confirm a large gain of the scheme against a baseline direct path scheme. Finally, the scheduler optimization permits to achieve a further improvement of 15-20% for non line-of-sight users.

International audience; This paper characterizes an achievable information-energy region of simultaneous information and energy transmission over an additive white Gaussian noise channel. This analysis is performed in the finite block-length regime with finite constellations. More specifically, a method for constructing a family of codes is proposed and the set of achievable tuples of information rate, energy rate, decoding error probability (DEP) and energy outage probability (EOP) is characterized. Using existing converse results, it is shown that the construction is information rate, energy rate, and EOP optimal. The achieved DEP is, however, sub-optimal.

International audience; Slicing is emerging as a promising technique to support new differentiated services in 5G networks. It provides the necessary flexility and scalability associated with future services. To maintain satisfactory services requirements and high profit for service providers, a slice may be designing according to the varying demands and resource availability. This paper develops a framework for resources allocation between slicing and business layer for multi-tenant slicing, e.g. virtual wireless operators, service providers and smart cities services. This paper proposes a flexible mechanism based on a biding scheme for slicing allocation, which achieve desirable fairness and efficiency among the network slices of the different tenants and their associated users. We then design a practical algorithms to realise the proposed desired solution. We also show through the simulation the efficiency of our approach in term of efficiency and fairness.

Many systems require frequent and regular updates of a certain information. These updates have to be transferred regularly from the source to the destination. We consider scenarios in which an old packet becomes completely obsolete, in the presence of a new packet. In this context, if a new packet arrives at the source while it is transferring a packet, one needs to decide the packet to be dropped. New packet has recent information, but might require more time to transfer. Thus it is not clear as to which packet to be dis- carded, and this is the main focus of the paper. Recently introduced performance metrics, called average age of information (AAoI) and peak age of information (PAoI) of the information available at the destination, are the relevant performance measures. These type of systems do not require storage buffers, of size more than one, at the source queue. We consider single source / multiple sources regularly updating information to a single destination possibly over wireless channels to derive optimal drop policies that optimize the AAoI. We showed that the state independent (static) policies like dropping always the old packets or dropping always the new packets is optimal in many scenarios, among an appropriate set of stationary Markov policies. We consider relevant games when multiple sources compete. In many scenarios, the non-cooperative solution almost minimizes the social objective, the sum of AAoIs of all the sources., Comment: 12 pages and 3 figures

In this paper, we present a resource allocation mechanism for network slices. We consider a dynamic resource allocation model with multiple independent resource providers. We call our allocation mechanism, the soft-max allocation mechanism, where the slices bid for resources and the resource providers allocate resources such that their revenue is close to the maximum revenue possible. We show that this mechanism translates into a game among the slices with a unique Nash equilibrium. We also show that the network utility obtained at this Nash equilibrium is close to the optimal social utility. We then present a stochastic dual sub-gradient algorithm that provably converges to the unique Nash equilibrium.

International audience

International audience; Macro cell densification with Small Cells (SCs) is an effective solution to cope with traffic increase. To fully benefit from the additional SCs capacity, interference mitigation techniques are needed. Densification in 5G networks with Massive Multiple Input Multiple Output (M-MIMO) deployment needs to rethink interference mitigation to account for highly focused beams and MultiUser (MU) scheduling. This paper presents a low complexity collaborative Proportional Fair (PF) based scheduling that maximizes the throughput and improves fairness of the heterogeneous network. The solution is based on the calculation of a loss factor indicator that each SC provides to the macro cell at each scheduling period. These indicators allow the macro cell MU scheduler to efficiently select the set of users for scheduling, leading to a significant improvement in performance. Numerical results illustrate the interest of the collaborative solution.

International audience; We consider a splittable atomic game with lossy links on a ring in which the cost that each player i minimizes is their own loss rate of packets. The costs are therefore non-additive (unlike costs based on delays or tolls) and moreover, there is no flow conservation (total flow entering a link is greater than the flow leaving it). We derive a closed-form for the equilibrium, which allows us to obtain insight on the structure of the equilibrium. We also derive the globally optimal solution and obtain conditions for the equilibrium to coincide with the globally optimal solution.

International audience; Network Slicing is one of the essential concepts that has been introduced in 5G networks design to support demand expressed by next generation services. Network slicing will also bring new business opportunities for service providers (SPs) and virtual network operators, allowing them to run their virtual, independent business operations on shared physical infrastructure. We consider a marketplace where service providers (SPs) i.e., slice tenants lease the resources from an infrastructure provider (InP) through a network slicing mechanism. They compete to offer a certain communication service to end-users. We show that the competition between SPs can be model using the multiresource Tullock contest (TC) framework, where SPs exert effort by expending costly resource to attract users. We study the competition between the SPs under a static and dynamic resource sharing scheme. In a dynamic resource sharing scheme, SPs are pre-assigned with fixed shares (budgets) of infrastructure, and they are allowed to redistribute their shares and customise their allocation to maximise their profit. The decision problem of SPs is analysed using non-cooperative game theory, and it is shown that the resultant game admits a unique Nash Equilibrium (NE). Furthermore, a distributed reinforcement algorithm is proposed that allows each SP to reach the game's unique Nash equilibrium. Finally, simulations results are conducted to analyse the interaction between market players and the economic efficacy of the network sharing mechanism.

In this work, we propose a Graph Convolutional Neural Networks (GCN) based scheduling algorithm for adhoc networks. In particular, we consider a generalized interference model called the $k$-tolerant conflict graph model and design an efficient approximation for the well-known Max-Weight scheduling algorithm. A notable feature of this work is that the proposed method do not require labelled data set (NP-hard to compute) for training the neural network. Instead, we design a loss function that utilises the existing greedy approaches and trains a GCN that improves the performance of greedy approaches. Our extensive numerical experiments illustrate that using our GCN approach, we can significantly ($4$-$20$ percent) improve the performance of the conventional greedy approach., Comment: 10 pages, Accepted at EAI VALUETOOLS 2021 - 14th EAI International Conference on Performance Evaluation Methodologies and Tools

In this report, a formula for estimating the number of SARS-CoV-2 infections in a population is given. The formula is in terms of the test results and test parameters, i.e., probability of true positives (sensitivity) and the probability of true negatives (specificity). The motivation of this work lies on the fact that, depending on the sensitivity and specificity of the tests, the number of positive results might be drastically different to the number of infected individuals. From this perspective, counting the positive results is not a reliable information source for decision-makingor policy-making. The relevance of the estimation presented in this work is that the precision increases with the number of tests. That is, large testing campaigns lead to a reliable estimation of the fraction of infected individuals in a population. Two conclusions are drawn from this work. First, in order to ensure that a reliable estimation is achieved, testing campaigns must be implemented with tests for which the sum of the sensitivity and the specificity is sufficiently different from one. Second, the key parameter for reducing the estimation error is the number of tests. For large number of tests, as long as the sum of the sensitivity and specificity do not add upto one, the exact values of these parameters have very little impact in the estimation error.; Ce rapport présente une formule mathématique pour estimer le nombre d’infectionsSARS-CoV-2 dans une population donnée. La formule utilise les résultats et les paramètres des tests, c’est-à-dire la probabilité de vrais positifs (sensibilité) et de vrais négatifs (spécificité). Selon la sensibilité et la spécificité des tests, le nombre de résultats positifs peut être radicalement différent du nombre d’individus infectés. Ainsi, le nombre final de résultats rendus positifs n’est pas une source d’information fiable pour la prise de décision ou l’élaboration des directives.Deux conclusions sont tirées de ce travail; afin de garantir l’obtention d’une estimation fiable,des campagnes de tests doivent être mises en oeuvre avec des tests pour lesquels la somme de la sensibilité et de la spécificité est significativement différente de un. De plus, il est prouvé qu’un nombre important de tests conduit à une estimation plus précise du nombre d’infectés. Pour un grand nombre de tests, tant que la somme de la sensibilité et de la spécificité n’est pas égale à un, les valeurs exactes de ces paramètres ont très peu d’impact sur l’erreur d’estimation

In this report, a formula for estimating the prevalence ratio of a disease in a population that is tested with imperfect tests is given. The formula is in terms of the fraction of positive test results and test parameters, i.e., probability of true positives (sensitivity) and the probability of true negatives (specificity). The motivation of this work arises in the context of the COVID-19 pandemic in which estimating the number of infected individuals depends on the sensitivity and specificity of the tests. In this context, it is shown that approximating the prevalence ratio by the ratio between the number of positive tests and the total number of tested individuals leads to dramatically high estimation errors, and thus, unadapted public health policies. The relevance of estimating the prevalence ratio using the formula presented in this work is that precision increases with the number of tests. Two conclusions are drawn from this work. First, in order to ensure that a reliable estimation is achieved with a finite number of tests, testing campaigns must be implemented with tests for which the sum of the sensitivity and the specificity is sufficiently different from one. Second, the key parameter for reducing the estimation error is the number of tests. For large number of tests, as long as the sum of the sensitivity and specificity is different from one, the exact values of these parameters have very little impact on the estimation error.; Ce rapport présente une formule mathématique pour estimer le nombre d’infections SARS-CoV-2 dans une population donnée. La formule utilise les résultats et les paramètres des tests, c’est-à-dire la probabilité de vrais positifs (sensibilité) et de vrais négatifs (spécificité). Selon la sensibilité et la spécificité des tests, le nombre de résultats positifs peut être radicalement différent du nombre d’individus infectés. Ainsi, le nombre final de résultats rendus positifs n’est pas une source d’information fiable pour la prise de décision ou l’élaboration des directives.Deux conclusions sont tirées de ce travail; afin de garantir l’obtention d’une estimation fiable,des campagnes de tests doivent être mises en oeuvre avec des tests pour lesquels la somme de la sensibilité et de la spécificité est significativement différente de un. De plus, il est prouvé qu’un nombre important de tests conduit à une estimation plus précise du nombre d’infectés. Pour un grand nombre de tests, tant que la somme de la sensibilité et de la spécificité n’est pas égale à un, les valeurs exactes de ces paramètres ont très peu d’impact sur l’erreur d’estimation.

Frequent variations in throughput make mobile networks a challenging environment for video streaming. Current video players deal with those variations by matching video quality to network throughput. However, this adaptation strategy results in frequent changes of video resolution and bitrate, which negatively impacts the users' streaming experience. Alternatively, keeping the video quality constant would improve the experience, but puts additional demand on the network. Downloading high quality content when channel quality is low requires additional resources, because data transfer efficiency is linked to channel quality. In this paper, we present a predictive Channel Quality based Buffering Strategy (CQBS) that lets the video buffer grow when channel quality is good, and relies on this buffer when channel quality decreases. Our strategy is the outcome of a Markov Decision Process. The underlying Markov chain is conditioned on 377 real-world LTE channel quality traces that we have collected using an Android mobile application. With our strategy, mobile network providers can deliver constant quality video streams, using less network resources.

We study a stochastic game framework with dynamic set of players, for modeling and analyzing their computational investment strategies in distributed computing. Players obtain a certain reward for solving the problem or for providing their computational resources, while incur a certain cost based on the invested time and computational power. We first study a scenario where the reward is offered for solving the problem, such as in blockchain mining. We show that, in Markov perfect equilibrium, players with cost parameters exceeding a certain threshold, do not invest; while those with cost parameters less than this threshold, invest maximal power. Here, players need not know the system state. We then consider a scenario where the reward is offered for contributing to the computational power of a common central entity, such as in volunteer computing. Here, in Markov perfect equilibrium, only players with cost parameters in a relatively low range in a given state, invest. For the case where players are homogeneous, they invest proportionally to the `reward to cost' ratio. For both the scenarios, we study the effects of players' arrival and departure rates on their utilities using simulations and provide additional insights.

We consider a multi-population epidemic model with one or more (almost) isolated communities and one mobile community. Each of the isolated communities has contact within itself and, in addition, contact with the outside world but only through the mobile community. The contact rate between the mobile community and the other communities is assumed to be controlled. We first derive a multidimensional ordinary differential equation (ODE) as a mean-field fluid approximation to the process of the number of infected nodes, after appropriate scaling. We show that the approximation becomes tight as the sizes of the communities grow. We then use a singular perturbation approach to reduce the dimension of the ODE and identify an optimal control policy on this system over a fixed time horizon via Pontryagin's minimum principle. We then show that this policy is close to optimal, within a certain class, on the original problem for large enough communities. From a phenomenological perspective, we show that the epidemic may sustain in time in all communities (and thus the system has a nontrivial metastable regime) even though in the absence of the mobile nodes the epidemic would die out quickly within each of the isolated communities., 16 pages (two column format), 4 figures

International audience; We consider in this paper the Hawk-Dove game in which each of infinitely many individuals, involved with pairwise encounters with other individuals, can decide whether to act aggressively (Hawk) or peacefully (Dove). Each individual is characterized by its strength. The strength distribution among the population is assumed to be fixed and not to vary in time. If both individuals involved in an interaction are Hawks, there will be a fight, the result of which will be determined by the strength of each of the individuals involved. The larger the difference between the strength of the individuals is, the larger is the cost for the weaker player involved in the fight. Our goal is to study the influence of the parameters (such as the strength level distribution) on the equilibrium of the game. We show that for some parameters there exists a threshold equilibrium policy while for other parameters there is no equilibrium policy at all.

International audience; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

Traditionally science is done using the reductionism paradigm. Artificial intelligence does not make an exception and it follows the same strategy. At the same time, network science tries to study complex systems as a whole. This synopsis presents my PhD thesis which takes an alternative approach to the reductionism strategy, with the aim to advance both fields, advocating that major breakthroughs can be made when these two are combined. The thesis illustrates this bidirectional relation by: (1) proposing a new method which uses artificial intelligence to improve network science algorithms (i.e. a new centrality metric which computes fully decentralized the nodes and links importance, on the polylogarithmic scale with respect to the number of nodes in the network); and (2) proposing two methods which take inspiration from network science to improve artificial intelligence algorithms (e.g. quadratic acceleration in terms of memory requirements and computational speed of artificial neural network fully connected layers during both, training and inference).

International audience; Adaptive video streaming improves users' quality of experience (QoE), while using the network efficiently. In the last few years, adaptive video streaming has seen widespread adoption and has attracted significant research effort. We study a dynamic system of random arrivals and departures for different classes of users using the adaptive streaming industry standard DASH (Dynamic Adaptive Streaming over HTTP). Using a Markov chain based analysis, we compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and switching rate. We validate our model by simulations, which show a very close match. Our study of the playout buffer is based on client adaptation scheme, which makes efficient use of the network while improving users' QoE. We prove that for buffer-based variants, the average video bit-rate matches the average channel rate. Hence, we would see quality switches whenever the average channel rate does not match the available video bit rates. We give a sufficient condition for setting the playout buffer threshold to ensure that quality switches only between adjacent quality levels.

International audience; We propose a setting for two-phase opinion dynamics in social networks, where the final opinion of a node in the first phase acts as its initial biased opinion in the second phase. In this setting, we study the problem of two camps aiming to maximize adoption of their respective opinions by strategically investing on nodes, where the effectiveness of a camp's investment on a node depends on the node's initial bias. We propose an extension of Friedkin-Johnsen model for our setting, and hence formulate the utility functions of the camps. For the non-competitive case where only one camp invests, we present a polynomial time algorithm for determining an optimal way to split the camp's budget between the two phases. For the case of competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time. Our main conclusion is that, if nodes attribute high weightage to their initial biases, it is advantageous to have a high investment in the first phase, so as to exploit the manipulated biases in the second phase.

International audience; To optimize routing of flows in datacenters, SDN controllers receive a packet-in message whenever a new flow appears in the network. Unfortunately, flow arrival rates can peak to millions per second [1], impairing the ability of controllers to treat them on time. Flow scheduling, e.g. [2], copes with such sheer numbers by segmenting the traffic between elephant and mice flows and by treating elephant flows in priority, as they disrupt short lived TCP flows and create bottlenecks. We formulate a flow segmentation problem that segment elephant from mice flows; the aim is to schedule a maximum amount of traffic under a constraint on the maximum rate of packet-in events. We propose a learning algorithm able to perform optimal online flow segmentation. Our solution, based on stochastic approximation techniques, is implemented at the switch level and updated by the controller, with minimal signaling over the control channel. Our approach is blind, i.e., it is agnostic to the flow size distribution. It is also adaptive, since it can track traffic variations over time. We prove its convergence properties and its message complexity. Moreover, we specialize our solution to be robust to traffic classification errors and we provide conditions under which our algorithm still converges to the optimal solution. Extensive numerical experiments characterize the performance of our approach in vitro. Finally, results of the implementation in a real OpenFlow controller demonstrate the viability of our method as a solution in production environments.

International audience; Opinion dynamics is a natural and well-known phenomenon in a system of cognitive agents, and is a well-studied topic across several disciplines. We study the problem of optimally investing in nodes of a social network in a competitive setting, wherein two camps attempt to maximize adoption of their opinions by the population. This problem is highly relevant to applications such as elections, viral marketing, propagation of ideas or behaviors, etc. In particular, we propose an extension of the popular DeGroot-Friedkin model, show its convergence, and hence formulate the problem as a zero-sum game, where the players are the two camps and their strategies are the amounts to be invested on each node of the social network. We consider several settings, namely, when the influence of a camp on a node is a concave function of its investment on that node, when one of the camps has uncertain information about the values of the model parameters, and when a camp aims at maximizing competitor's investment required to drive the average opinion in its favor. We also study this game under common coupled constraints concerning the combined investment of the camps on each node, and analytically derive the optimal investment strategies of both the camps. The introduction of common coupled constraints results in maxmin value to be greater than or equal to the minmax value, an opposite inequality to the case of general functions; this can be perceived as a consequence of the first mover advantage in our game. We further study the possibility of campaigning in multiple phases, where the final opinion of a node in a phase acts as its initial bias for the next phase. We show that the utility functions of the two camps involve what can be interpreted as a multiphase version of Katz centrality. Focusing on opinion dynamics in two phases, we analyze their optimal investment strategies and derive the extent of loss that a camp would incur if it acted myopically. We then look at the setting in which a camp's influence on a node depends on the node's initial bias, for which we present a polynomial time algorithm for optimally splitting the budget between the two phases. We conduct a simulation study on real-world social networks for all the considered settings.

International audience

International audience; In this paper, we study a sequential routing game networks where M users send traffic to a destination on a line. Each user arrives at time epoch with a given capacity. Then, he ships its demand over time on a shared resource. The state of a player evolves according to whether he decides to transmit or not. The decision of each user is thus spatio-temporal control. We provide explicit expression of the equilibrium of such systems and compare it to the global optimum case. In particular, we compute the price of anarchy of such scheme and identify a Braess-type paradox in the context of sequential routing game.

International audience; We consider the problem of 'fair' scheduling the resources to one of the many mobile stations by a centrally controlled base station (BS). The BS is the only entity taking decisions in this framework based on truthful information from the mobiles on their radio channel. We study the well-known family of parametric -fair scheduling problems from a gametheoretic perspective in which some of the mobiles may be noncooperative. We first show that if the BS is unaware of the noncooperative behavior from the mobiles, the noncooperative mobiles become successful in snatching the resources from the other cooperative mobiles, resulting in unfair allocations. If the BS is aware of the noncooperative mobiles, a new game arises with BS as an additional player. It can then do better by neglecting the signals from the noncooperative mobiles. The BS, however, becomes successful in eliciting the truthful signals from the mobiles only when it uses additional information (signal statistics). This new policy along with the truthful signals from mobiles forms a Nash Equilibrium (NE) which we call a Truth Revealing Equilibrium. Finally, we propose new iterative algorithms to implement fair scheduling policies that robustify the otherwise non-robust (in presence of noncooperation) fair scheduling algorithms.

Forming, in a decentralized fashion, an optimal network topology while balancing multiple, possibly conflicting objectives like cost, high performance, security and resiliency to viruses is a challenging endeavor. In this paper, we take a game-formation approach to network design where each player, for instance an autonomous system in the Internet, aims to collectively minimize the cost of installing links, of protecting against viruses, and of assuring connectivity. In the game, minimizing virus risk as well as connectivity costs results in sparse graphs. We show that the Nash Equilibria are trees that, according to the Price of Anarchy (PoA), are close to the global optimum, while the worst-case Nash Equilibrium and the global optimum may significantly differ for small infection rate and link installation cost. Moreover, the types of trees, in both the Nash Equilibria and the optimal solution, depend on the virus infection rate, which provides new insights into how viruses spread: for high infection rate τ, the path graph is the worst- and the star graph is the best-case Nash Equilibrium. However, for small and intermediate values of τ, trees different from the path and star graphs may be optimal.

International audience; Companies or users that want to do advertising on Online Social Networks, need to know in which News Feed it would be more efficient to post, at what time of day the posts would have more visibility and which topic of message they should choose to reach popularity. We propose to answer these questions with the help of the Routing Game Theory, which considers a finite number of players and splittable demands. First, we propose a visibility measure on a News Feed. Next, we present our game based on visibility measures, which is similar to the Weighted Allocation Game. Following this consideration, we provide the uniqueness of the Nash Equilibrium, a characterization via concave programming, and its closed form. Then, we propose a two time scale decentralized algorithm where each user estimates the total flow of messages in each News Feed and uses stochastic gradient algorithm to update their own flows. Finally, we use a real data set in order to estimate the parameters of our model. The goal of this model is to provide to the Online Social Network Authority a better knowledge of posting behavior of competitive users.

In small cell networks, high mobility of users results in frequent handoff and thus severely restricts the data rate for mobile users. To alleviate this problem, we propose to use heterogeneous, two-tier network structure where static users are served by both macro and micro base stations, whereas the mobile (i.e., moving) users are served only by macro base stations having larger cells; the idea is to prevent frequent data outage for mobile users due to handoff. We use the classical two-tier Poisson network model with different transmit powers (cf [1]), assume independent Poisson process of static users and doubly stochastic Poisson process of mobile users moving at a constant speed along infinite straight lines generated by a Poisson line process. Using stochastic geometry, we calculate the average downlink data rate of the typical static and mobile (i.e., moving) users, the latter accounted for handoff outage periods. We consider also the average throughput of these two types of users defined as their average data rates divided by the mean total number of users co-served by the same base station. We find that if the density of a homogeneous network and/or the speed of mobile users is high, it is advantageous to let the mobile users connect only to some optimal fraction of BSs to reduce the frequency of handoffs during which the connection is not assured. If a heterogeneous structure of the network is allowed, one can further jointly optimize the mean throughput of mobile and static users by appropriately tuning the powers of micro and macro base stations subject to some aggregate power constraint ensuring unchanged mean data rates of static users via the network equivalence property (see [2])., 13 pages, 5 diagrams, 2 plots, 3 images

International audience; The paper establishes a general approach for analyzing queueing models with repeated inhomogeneous vacations. The server goes on for a vacation if the inactivity prolongs more than the vacation trigger duration. Once the system enters in vacation mode, it may continue for several consecutive vacations, possibly with a {\em different} probability distribution. We study a simple $M/G/1$ queue, which has the advantage of being tractable analytically. The theoretical model is applied to the problem of power saving for mobile devices in which the sleep durations of a device correspond to the server vacations. Various system performance metrics such as the frame response time and the economy of energy are derived. A constrained optimization problem is formulated to maximize the economy of energy in power save mode with QoS constraints. An illustration of the proposed methods is shown with a WiMAX system scenario to obtain design parameters for better performance. Our analysis allows us not only to optimize the system parameters for a given traffic intensity but also to propose parameters that provide the best performance under worst case conditions.

International audience; We study a non-zero sum game considered on the solutions of a hybrid dynamical system that evolves in continuous time and that is subjected to abrupt changes of parameters. The changes of the parameters are synchronized with (and determined by) the changes of the states/actions of two Markov decision processes, each of which is controlled by a player that aims at minimizing his or her objective function. The lengths of the time intervals between the " jumps " of the parameters are assumed to be small. We show that an asymptotic Nash equilibrium of such hybrid game can be constructed on the basis of a Nash equilibrium of a deterministic averaged dynamic game.

National audience

National audience

International audience; The emerging technologies of IoT (Internet of Things), SDN (Softwar Defined Networking), and NFV (Network Function Virtualization) have great potential for the information service innovation in the Cloud and big data era. In this paper, we study the architecture models of IoT, SDN with NFV implementation. We present a general SDN-based IoT framework with NFV implantation in order to take advantages of SDN and NFV and help for the design of IoT architecture.

This paper proposes a new prediction process to explain and predict popularity evolution of YouTube videos. We exploit our recent study on the classification of YouTube videos in order to predict the evolution of videos' view-count. This classification allows to identify important factors of the observed popularity dynamics. Our experimental results show that our prediction process is able to reduce the average prediction errors compared to a state-of-the-art baseline model. We also evaluate the impact of adding popularity criteria in our classification., Paper withdrawn due to conflict of interest in conference call for paper

The Net Neutrality issue has been at the center of debate worldwide lately. Some countries have established laws so that principles of Net Neutrality are respected, the Netherlands being the latest country in Europe. Among the questions that have been discussed in these debates are whether to allow agreements between service and content providers, i.e. to allow some preferential treatment by an operator to traffic from some subset of providers. Our goal in this paper is to analyze the impact of non neutral pricing and agreements on the Internet users and on the content providers. Each one of several Internet users have to decide in which way to split their demand among several content providers. The cost for an Internet user depends on whether the content providers have an agreement with the Internet Service Provider in which the Internet user is connected to. In addition, the requests coming from users depend on the preference of the consumer in the different CP. We model the choice of how to split the demands and the pricing aspects faced by the content providers as a hierarchical game model composed of a congestion game at the lower level and a noncooperative pricing game at the upper level. We show that agreement between providers have a positive impact on the equilibrium performance of the Internet users. We further show that at equilibrium, the content provider on the contrary, does not benefit from the agreement.

Large antenna arrays can be used to generate highly focused beams that support very high data rates and reduced energy consumption. However, optimal beam focusing requires large amount of feedback from the users in order to choose the best beam, especially in Frequency Division Duplex (FDD) mode. This paper develops a methodology for designing a multilevel codebook of beams in an environment with low number of multipaths. The antenna design supporting the focused beams is formulated as an optimization problem. A multilevel codebook of beams is constructed according to the coverage requirements. An iterative beam scheduling is proposed that searches through the codebook to select the best beam for a given user. The methodology is applied to a mass event and to a rural scenario, both analyzed using an event-based network simulator. Very significant gains are obtained for both scenarios. It is shown that the more dominant the Line of Sight (LoS) component, the higher the gain achieved by the multilevel beamforming., Comment: Orange Labs Report

RÉSUMÉ. Cet article est une étude de l’évolution du nombre de vues des contenus dans You- Tube. Nous proposons dans un premier temps plusieurs modèles inspirés de l’économie et de la biologie pour caractériser les courbes d’évolution des nombres de vues des vidéos. Dans un deuxième temps, nous proposons une méthode automatique de classification de ces courbes en les associant à l’un des différents modèles suggérés. Nous montrons, sur un large ensemble de données, que 90% des vidéos peuvent être associées à l’un de ces modèles avec une erreur moyenne inférieure à 5%. Une étude empirique est menée au sujet de l’impact de la popularité et des catégories de vidéos sur l’évolution des nombres de vues. Enfin, cette classification est utilisée dans un exemple de méthode de prédiction de la popularité des vidéos.

International audience; We consider a game of timing between advertisers, or other content creators, who compete for position and exposure over a shared publication medium such as an on-line classified list. Posted items (such as ads, messages, multimedia, or comments) are ordered according to their posting times, with recent posts displayed at the top positions. The effectiveness of each item depends on its current display position, as well as on a time-dependent exposure function which represents the collective exposure of the medium. It is assumed that each of a Poisson-distributed number of advertiser may choose the posting time of his item within a finite time interval, with the goal of maximizing the total exposure of this item. We formulate and the problem as a non-cooperative game between advertisers, and analyze the Nash equilibrium profile of this game in terms of existence, uniqueness, computation and efficiency. Explicit expressions are derived for the case where the relative importance of the posting positions are geometrically decreasing.

International audience; We study a mean field approximation of the M/M/$\infty$ queuing system. This queue is often used to model the number of cellular phone users in a cell. We assume that congestion here has a positive impact on the performance so that the more there are users, the less it is costly to offer a service per cell phone, for example, if a base station broadcasts a film then the cost per customer decreases. We obtain closed-form formulas for the equilibria. We show that the mean-field approximation becomes tight as the workload increases, thus the results obtained for the mean-field model well approximate the discrete one.