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International audience ; In 5G and beyond network architectures, operators and content providers base their content distribution strategies on small cell networks. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) Joint Transmissions are used to improve performance. Online solutions for average delay minimization problem have been studied in the related literature, although only under the strong assumption that files have equal sizes. In this paper we aim to fill this gap and propose an online caching policy, qLRU-HS, that takes into account heterogeneous sizes and asymptotically converges to the optimal cache allocation under the Independent Reference Model. Our experiments confirm such convergence in practice and reveal that qLRU-HS outperforms other state-of-the-art solutions. ; Dans les architectures des réseaux 5G et au-delà, les opérateurs et les fournisseurs de contenu basent leurs stratégies de distribution de contenu sur les réseaux Small-Cell. Au-dessus de ces réseaux, la mise en cache et les transmissions conjointes CoMP sont utilisées pour améliorer les performances. Des solutions en ligne pour le problème de minimisation du délai moyen ont été étudiées dans la littérature connexe, mais uniquement sous l'hypothèse forte que les fichiers ont des tailles égales. Dans cet article, nous cherchons à combler cette lacune et proposons une politique de mise en cache en ligne, qLRU-HS, qui prend en compte les tailles hétérogènes et converge asymptotiquement vers l'allocation optimale du cache sous le Independence Reference Model (IRM). Nos expériences confirment cette convergence dans la pratique et révèlent que qLRU-HS surpasse les autres solutions de l'état de l'art.

International audience ; In 5G and beyond network architectures, operators and content providers base their content distribution strategies on small cell networks. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) Joint Transmissions are used to improve performance. Online solutions for average delay minimization problem have been studied in the related literature, although only under the strong assumption that files have equal sizes. In this paper we aim to fill this gap and propose an online caching policy, qLRU-HS, that takes into account heterogeneous sizes and asymptotically converges to the optimal cache allocation under the Independent Reference Model. Our experiments confirm such convergence in practice and reveal that qLRU-HS outperforms other state-of-the-art solutions. ; Dans les architectures des réseaux 5G et au-delà, les opérateurs et les fournisseurs de contenu basent leurs stratégies de distribution de contenu sur les réseaux Small-Cell. Au-dessus de ces réseaux, la mise en cache et les transmissions conjointes CoMP sont utilisées pour améliorer les performances. Des solutions en ligne pour le problème de minimisation du délai moyen ont été étudiées dans la littérature connexe, mais uniquement sous l'hypothèse forte que les fichiers ont des tailles égales. Dans cet article, nous cherchons à combler cette lacune et proposons une politique de mise en cache en ligne, qLRU-HS, qui prend en compte les tailles hétérogènes et converge asymptotiquement vers l'allocation optimale du cache sous le Independence Reference Model (IRM). Nos expériences confirment cette convergence dans la pratique et révèlent que qLRU-HS surpasse les autres solutions de l'état de l'art.

International audience ; In 5G and beyond network architectures, operators and content providers base their content distribution strategies on small cell networks. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) Joint Transmissions are used to improve performance. Online solutions for average delay minimization problem have been studied in the related literature, although only under the strong assumption that files have equal sizes. In this paper we aim to fill this gap and propose an online caching policy, qLRU-HS, that takes into account heterogeneous sizes and asymptotically converges to the optimal cache allocation under the Independent Reference Model. Our experiments confirm such convergence in practice and reveal that qLRU-HS outperforms other state-of-the-art solutions. ; Dans les architectures des réseaux 5G et au-delà, les opérateurs et les fournisseurs de contenu basent leurs stratégies de distribution de contenu sur les réseaux Small-Cell. Au-dessus de ces réseaux, la mise en cache et les transmissions conjointes CoMP sont utilisées pour améliorer les performances. Des solutions en ligne pour le problème de minimisation du délai moyen ont été étudiées dans la littérature connexe, mais uniquement sous l'hypothèse forte que les fichiers ont des tailles égales. Dans cet article, nous cherchons à combler cette lacune et proposons une politique de mise en cache en ligne, qLRU-HS, qui prend en compte les tailles hétérogènes et converge asymptotiquement vers l'allocation optimale du cache sous le Independence Reference Model (IRM). Nos expériences confirment cette convergence dans la pratique et révèlent que qLRU-HS surpasse les autres solutions de l'état de l'art.

Voir aussi actes de la conférence : ValueTools - 6th International Conference on Performance Evaluation Methodologies and Tools - 2012 (2012) (http://hal.inria.fr/hal-00760915) ; This paper provides building blocks for the performance evaluation of Content Centric-like Networks (CCNs). In CCNs if a cache receives a request for a content it does not store ({\em miss}), it forwards the request to a higher-level cache, if any, or to the server. When located, the document is routed on the reverse-path and a copy is placed in each cache along the path. In this paper we consider a cache replacement policy based on Time-to-Lives (TTLs) like in a DNS network. A local TTL is set when the content is first stored at the cache and is renewed every time the cache can satisfy a request for this content (at each hit). The content is removed when the TTL expires. Under the assumption that requests follow a renewal process and the TTLs are exponential random variables, we determine exact formulas for the performance metrics of interest (average cache occupancy, hit and miss probabilities/rates) for some specific architectures (a linear network and a tree network with one root node and $N$ leaf nodes). For more general topologies and general TTL distributions, we propose an approximate solution. Numerical results show the approximations to be accurate, with relative errors smaller than $10^{-3}$ and $10^{-2}$ respectively for exponentially distributed and constant TTLs. ; Cet article développe des briques de base pour l'évaluation des performances de réseaux orientés contenus. Dans ces réseaux lorsqu'un n{\oe}ud ou cache reçoit une requête pour un contenu qu'il ne posséde pas il la transmet á un ou plusieurs caches de niveau supérieur. Une fois le contenu localisé il est envoyé et stocké á tous les caches qui ont reçu la requête ainsi qu'á l'utilisateur. Dans cet article nous nous intéressons á une politique de gestion des caches qui utilise des temporisateurs (TTL pour {\em Time-to-Live}). A chaque arrivée d'un contenu dans un ...

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.

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.

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.

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.

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.

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.

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.

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.

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 algorithms usually 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 and an analytical model to capture the visibility of publishers over a News Feed. First, 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 bias incurred by the 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 found that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views.

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.

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.