18 research outputs found

    A\c{C}AI: Ascent Similarity Caching with Approximate Indexes

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    Similarity search is a key operation in multimedia retrieval systems and recommender systems, and it will play an important role also for future machine learning and augmented reality applications. When these systems need to serve large objects with tight delay constraints, edge servers close to the end-user can operate as similarity caches to speed up the retrieval. In this paper we present A\c{C}AI, a new similarity caching policy which improves on the state of the art by using (i) an (approximate) index for the whole catalog to decide which objects to serve locally and which to retrieve from the remote server, and (ii) a mirror ascent algorithm to update the set of local objects with strong guarantees even when the request process does not exhibit any statistical regularity

    Online Submodular Maximization via Online Convex Optimization

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    We study monotone submodular maximization under general matroid constraints in the online setting. We prove that online optimization of a large class of submodular functions, namely, weighted threshold potential functions, reduces to online convex optimization (OCO). This is precisely because functions in this class admit a concave relaxation; as a result, OCO policies, coupled with an appropriate rounding scheme, can be used to achieve sublinear regret in the combinatorial setting. We show that our reduction extends to many different versions of the online learning problem, including the dynamic regret, bandit, and optimistic-learning settings.Comment: Under revie

    Apprentissage séquentiel pour l'allocation de ressources dans les réseaux

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    Network resource allocation is a complex and fundamental problem in computer science. It is a process in which components of a networked system aim to provide a faster service to demands, or to reduce the computation or communication load on the system. The main factors that contribute to the complexity of this problem are that the demands arrive to the system in an unpredictable and sequential fashion and may compete for the different network resources. The ubiquity of network resource allocation problems has motivated extensive research to design new policies with provable guarantees. This thesis investigates several instances of the network resource allocation problem and proposes online policies with strong performance guarantees leveraging the online learning framework.First, we study the online caching problem in which demands for files can be served by a local cache to avoid retrieval costs from a remote server. We study no-regret algorithms based on online mirror descent (OMD) strategies. We show that the optimal OMD strategy depends on the request diversity present in a batch of demands. We also prove that, when the cache must store the entire file, rather than a fraction, OMD strategies can be coupled with a randomized rounding scheme that preserves regret guarantees. We also present an extension to cache networks, and we propose a no-regret distributed online policy.Second, we investigate similarity caches that can reply to a demand for an object with similar objects stored locally. We propose a new online similarity caching policy that employs gradient descent to navigate the continuous representation space of objects and find appropriate objects to store in the cache. We provide theoretical convergence guarantees under stationary demands and show the proposed policy reduces service costs incurred by the system for 360-video delivery systems and recommendation systems. Subsequently, we show that the similarity caching problem can be formulated in the online learning framework by utilizing an OMD policy paired with randomized rounding to achieve a no-regret guarantee.Third, we present the novel idea of inference delivery networks (IDNs), networks of computing nodes that coordinate to satisfy machine learning (ML) inference demands achieving the best trade-off between latency and accuracy. IDNs bridge the dichotomy between device and cloud execution by integrating inference delivery at the various tiers of the infrastructure continuum (access, edge, regional data center, cloud). We propose a no-regret distributed dynamic policy for ML model allocation in an IDN: each node dynamically updates its local set of inference models based on demands observed during the recent past plus limited information exchange with its neighboring nodes.Finally, we study the fairness of network resource allocation problem under the alpha-fairness criterion. We recognize two different fairness objectives that naturally arise in this problem: the well-understood slot-fairness objective that aims to ensure fairness at every timeslot, and the less explored horizon-fairness objective that aims to ensure fairness across utilities accumulated over a time horizon. We argue that horizon-fairness comes at a lower price in terms of social welfare. We study horizon-fairness with the regret as a performance metric and show that vanishing regret cannot be achieved in presence of an unrestricted adversary. We propose restrictions on the adversary's capabilities corresponding to realistic scenarios and an online policy that indeed guarantees vanishing regret under these restrictions. We demonstrate the applicability of the proposed fairness framework to a representative resource management problem considering a virtualized caching system where different caches cooperate to serve content requests.L'allocation de ressources dans les r√©seaux est un probl√®me complexe et fondamental en informatique. Il s'agit d'un processus dans lequel les composants d'un syst√®me de r√©seau visent √† fournir un service plus rapide aux demandes ou √† r√©duire la charge de calcul ou de communication sur le syst√®me. Les principaux facteurs qui contribuent √† la complexit√© de ce probl√®me sont que les demandes arrivent au syst√®me de mani√®re impr√©visible et s√©quentielle et peuvent entrer en concurrence pour les diff√©rentes ressources du r√©seau. L'ubiquit√© des probl√®mes d'allocation de ressources dans les r√©seaux a motiv√© des recherches approfondies pour concevoir de nouveaux algorithmes avec des garanties prouvables. Cette th√®se √©tudie plusieurs instances du probl√®me d'allocation de ressources dans les r√©seaux et propose des algorithmes adaptatifs avec de fortes garanties de performances s'appuyant sur le cadre d'apprentissage s√©quentiel.Premi√®rement, nous √©tudions le probl√®me de mise en cache s√©quentiel, dans lequel les demandes de fichiers peuvent √™tre servies par un cache local pour √©viter les co√Ľts de r√©cup√©ration √† partir d'un serveur distant. Nous √©tudions des algorithmes avec des garanties de performance bas√©s sur des strat√©gies de descente miroir (DM). Nous montrons que la strat√©gie DM optimale d√©pend de la diversit√© pr√©sente dans un lot de demandes. Nous prouvons √©galement que, lorsque le cache doit stocker le fichier entier, plut√īt qu'une fraction, les strat√©gies DM peuvent √™tre coupl√©es √† un sch√©ma d'arrondi al√©atoire qui pr√©serve garanties de performance. Nous pr√©sentons de plus une extension aux r√©seaux de caches, et nous proposons un algorithme adaptatif distribu√©. Deuxi√®mement, nous √©tudions les caches de similarit√© qui peuvent r√©pondre √† une demande d'un objet avec des objets similaires stock√©s localement. Nous proposons un nouvel algorithme de mise en cache de similarit√© s√©quentiel qui utilise la descente de gradient pour naviguer dans l'espace de repr√©sentation continue des objets et trouver les objets appropri√©s √† stocker dans le cache. Nous montrons que l'algorithme propos√© r√©duit les co√Ľts de service encourus par le syst√®me pour les syst√®mes de diffusion vid√©o √† 360 degr√©s et les syst√®mes de recommandation. Par la suite, nous montrons que le probl√®me de mise en cache de similarit√© peut √™tre formul√© dans le cadre d'apprentissage s√©quentiel en utilisant un algorithme MD associ√©e √† un arrondi al√©atoire.Troisi√®mement, nous pr√©sentons les r√©seaux de distribution d'inf√©rence (RDI) √©mergents, des r√©seaux de nŇďuds informatiques qui se coordonnent pour satisfaire les demandes d'inf√©rence d'apprentissage automatique (AA) en obtenant le meilleur compromis entre latence et pr√©cision. Nous proposons un algorithme adaptatif distribu√© pour l'allocation de mod√®les d'AA dans un RDI : chaque nŇďud met √† jour dynamiquement son ensemble local de mod√®les d'inf√©rence en fonction des demandes observ√©es au cours du pass√© r√©cent et d'un √©change d'informations limit√© avec ses nŇďuds voisins. Finalement, nous √©tudions l'√©quit√© du probl√®me d'allocation des ressources r√©seau sous le crit√®re d'alpha-fairness. Nous reconnaissons deux objectifs d'√©quit√© diff√©rents qui surgissent naturellement dans ce probl√®me : l'objectif d'√©quit√© de tranche bien compris qui vise √† assurer l'√©quit√© √† chaque tranche de temps, et l'objectif d'√©quit√© d'horizon moins explor√© qui vise √† assurer l'√©quit√© entre les utilit√©s accumul√©es sur un horizon temporel. Nous √©tudions l'√©quit√© de l'horizon avec le regret comme m√©trique de performance et montrons que la disparition du regret ne peut √™tre atteinte en pr√©sence d'un adversaire sans restriction. Nous proposons des restrictions sur les capacit√©s de l'adversaire correspondant √† des sc√©narios r√©alistes et un algorithme adaptatif qui garantit en effet la disparition du regret sous ces restrictions

    Apprentissage séquentiel pour l'allocation de ressources dans les réseaux

    No full text
    Network resource allocation is a complex and fundamental problem in computer science. It is a process in which components of a networked system aim to provide a faster service to demands, or to reduce the computation or communication load on the system. The main factors that contribute to the complexity of this problem are that the demands arrive to the system in an unpredictable and sequential fashion and may compete for the different network resources. The ubiquity of network resource allocation problems has motivated extensive research to design new policies with provable guarantees. This thesis investigates several instances of the network resource allocation problem and proposes online policies with strong performance guarantees leveraging the online learning framework.First, we study the online caching problem in which demands for files can be served by a local cache to avoid retrieval costs from a remote server. We study no-regret algorithms based on online mirror descent (OMD) strategies. We show that the optimal OMD strategy depends on the request diversity present in a batch of demands. We also prove that, when the cache must store the entire file, rather than a fraction, OMD strategies can be coupled with a randomized rounding scheme that preserves regret guarantees. We also present an extension to cache networks, and we propose a no-regret distributed online policy.Second, we investigate similarity caches that can reply to a demand for an object with similar objects stored locally. We propose a new online similarity caching policy that employs gradient descent to navigate the continuous representation space of objects and find appropriate objects to store in the cache. We provide theoretical convergence guarantees under stationary demands and show the proposed policy reduces service costs incurred by the system for 360-video delivery systems and recommendation systems. Subsequently, we show that the similarity caching problem can be formulated in the online learning framework by utilizing an OMD policy paired with randomized rounding to achieve a no-regret guarantee.Third, we present the novel idea of inference delivery networks (IDNs), networks of computing nodes that coordinate to satisfy machine learning (ML) inference demands achieving the best trade-off between latency and accuracy. IDNs bridge the dichotomy between device and cloud execution by integrating inference delivery at the various tiers of the infrastructure continuum (access, edge, regional data center, cloud). We propose a no-regret distributed dynamic policy for ML model allocation in an IDN: each node dynamically updates its local set of inference models based on demands observed during the recent past plus limited information exchange with its neighboring nodes.Finally, we study the fairness of network resource allocation problem under the alpha-fairness criterion. We recognize two different fairness objectives that naturally arise in this problem: the well-understood slot-fairness objective that aims to ensure fairness at every timeslot, and the less explored horizon-fairness objective that aims to ensure fairness across utilities accumulated over a time horizon. We argue that horizon-fairness comes at a lower price in terms of social welfare. We study horizon-fairness with the regret as a performance metric and show that vanishing regret cannot be achieved in presence of an unrestricted adversary. We propose restrictions on the adversary's capabilities corresponding to realistic scenarios and an online policy that indeed guarantees vanishing regret under these restrictions. We demonstrate the applicability of the proposed fairness framework to a representative resource management problem considering a virtualized caching system where different caches cooperate to serve content requests.L'allocation de ressources dans les r√©seaux est un probl√®me complexe et fondamental en informatique. Il s'agit d'un processus dans lequel les composants d'un syst√®me de r√©seau visent √† fournir un service plus rapide aux demandes ou √† r√©duire la charge de calcul ou de communication sur le syst√®me. Les principaux facteurs qui contribuent √† la complexit√© de ce probl√®me sont que les demandes arrivent au syst√®me de mani√®re impr√©visible et s√©quentielle et peuvent entrer en concurrence pour les diff√©rentes ressources du r√©seau. L'ubiquit√© des probl√®mes d'allocation de ressources dans les r√©seaux a motiv√© des recherches approfondies pour concevoir de nouveaux algorithmes avec des garanties prouvables. Cette th√®se √©tudie plusieurs instances du probl√®me d'allocation de ressources dans les r√©seaux et propose des algorithmes adaptatifs avec de fortes garanties de performances s'appuyant sur le cadre d'apprentissage s√©quentiel.Premi√®rement, nous √©tudions le probl√®me de mise en cache s√©quentiel, dans lequel les demandes de fichiers peuvent √™tre servies par un cache local pour √©viter les co√Ľts de r√©cup√©ration √† partir d'un serveur distant. Nous √©tudions des algorithmes avec des garanties de performance bas√©s sur des strat√©gies de descente miroir (DM). Nous montrons que la strat√©gie DM optimale d√©pend de la diversit√© pr√©sente dans un lot de demandes. Nous prouvons √©galement que, lorsque le cache doit stocker le fichier entier, plut√īt qu'une fraction, les strat√©gies DM peuvent √™tre coupl√©es √† un sch√©ma d'arrondi al√©atoire qui pr√©serve garanties de performance. Nous pr√©sentons de plus une extension aux r√©seaux de caches, et nous proposons un algorithme adaptatif distribu√©. Deuxi√®mement, nous √©tudions les caches de similarit√© qui peuvent r√©pondre √† une demande d'un objet avec des objets similaires stock√©s localement. Nous proposons un nouvel algorithme de mise en cache de similarit√© s√©quentiel qui utilise la descente de gradient pour naviguer dans l'espace de repr√©sentation continue des objets et trouver les objets appropri√©s √† stocker dans le cache. Nous montrons que l'algorithme propos√© r√©duit les co√Ľts de service encourus par le syst√®me pour les syst√®mes de diffusion vid√©o √† 360 degr√©s et les syst√®mes de recommandation. Par la suite, nous montrons que le probl√®me de mise en cache de similarit√© peut √™tre formul√© dans le cadre d'apprentissage s√©quentiel en utilisant un algorithme MD associ√©e √† un arrondi al√©atoire.Troisi√®mement, nous pr√©sentons les r√©seaux de distribution d'inf√©rence (RDI) √©mergents, des r√©seaux de nŇďuds informatiques qui se coordonnent pour satisfaire les demandes d'inf√©rence d'apprentissage automatique (AA) en obtenant le meilleur compromis entre latence et pr√©cision. Nous proposons un algorithme adaptatif distribu√© pour l'allocation de mod√®les d'AA dans un RDI : chaque nŇďud met √† jour dynamiquement son ensemble local de mod√®les d'inf√©rence en fonction des demandes observ√©es au cours du pass√© r√©cent et d'un √©change d'informations limit√© avec ses nŇďuds voisins. Finalement, nous √©tudions l'√©quit√© du probl√®me d'allocation des ressources r√©seau sous le crit√®re d'alpha-fairness. Nous reconnaissons deux objectifs d'√©quit√© diff√©rents qui surgissent naturellement dans ce probl√®me : l'objectif d'√©quit√© de tranche bien compris qui vise √† assurer l'√©quit√© √† chaque tranche de temps, et l'objectif d'√©quit√© d'horizon moins explor√© qui vise √† assurer l'√©quit√© entre les utilit√©s accumul√©es sur un horizon temporel. Nous √©tudions l'√©quit√© de l'horizon avec le regret comme m√©trique de performance et montrons que la disparition du regret ne peut √™tre atteinte en pr√©sence d'un adversaire sans restriction. Nous proposons des restrictions sur les capacit√©s de l'adversaire correspondant √† des sc√©narios r√©alistes et un algorithme adaptatif qui garantit en effet la disparition du regret sous ces restrictions

    Online learning for network resource allocation

    No full text
    L'allocation de ressources dans les r√©seaux est un probl√®me complexe et fondamental en informatique. Il s'agit d'un processus dans lequel les composants d'un syst√®me de r√©seau visent √† fournir un service plus rapide aux demandes ou √† r√©duire la charge de calcul ou de communication sur le syst√®me. Les principaux facteurs qui contribuent √† la complexit√© de ce probl√®me sont que les demandes arrivent au syst√®me de mani√®re impr√©visible et s√©quentielle et peuvent entrer en concurrence pour les diff√©rentes ressources du r√©seau. L'ubiquit√© des probl√®mes d'allocation de ressources dans les r√©seaux a motiv√© des recherches approfondies pour concevoir de nouveaux algorithmes avec des garanties prouvables. Cette th√®se √©tudie plusieurs instances du probl√®me d'allocation de ressources dans les r√©seaux et propose des algorithmes adaptatifs avec de fortes garanties de performances s'appuyant sur le cadre d'apprentissage s√©quentiel.Premi√®rement, nous √©tudions le probl√®me de mise en cache s√©quentiel, dans lequel les demandes de fichiers peuvent √™tre servies par un cache local pour √©viter les co√Ľts de r√©cup√©ration √† partir d'un serveur distant. Nous √©tudions des algorithmes avec des garanties de performance bas√©s sur des strat√©gies de descente miroir (DM). Nous montrons que la strat√©gie DM optimale d√©pend de la diversit√© pr√©sente dans un lot de demandes. Nous prouvons √©galement que, lorsque le cache doit stocker le fichier entier, plut√īt qu'une fraction, les strat√©gies DM peuvent √™tre coupl√©es √† un sch√©ma d'arrondi al√©atoire qui pr√©serve garanties de performance. Nous pr√©sentons de plus une extension aux r√©seaux de caches, et nous proposons un algorithme adaptatif distribu√©. Deuxi√®mement, nous √©tudions les caches de similarit√© qui peuvent r√©pondre √† une demande d'un objet avec des objets similaires stock√©s localement. Nous proposons un nouvel algorithme de mise en cache de similarit√© s√©quentiel qui utilise la descente de gradient pour naviguer dans l'espace de repr√©sentation continue des objets et trouver les objets appropri√©s √† stocker dans le cache. Nous montrons que l'algorithme propos√© r√©duit les co√Ľts de service encourus par le syst√®me pour les syst√®mes de diffusion vid√©o √† 360 degr√©s et les syst√®mes de recommandation. Par la suite, nous montrons que le probl√®me de mise en cache de similarit√© peut √™tre formul√© dans le cadre d'apprentissage s√©quentiel en utilisant un algorithme MD associ√©e √† un arrondi al√©atoire.Troisi√®mement, nous pr√©sentons les r√©seaux de distribution d'inf√©rence (RDI) √©mergents, des r√©seaux de nŇďuds informatiques qui se coordonnent pour satisfaire les demandes d'inf√©rence d'apprentissage automatique (AA) en obtenant le meilleur compromis entre latence et pr√©cision. Nous proposons un algorithme adaptatif distribu√© pour l'allocation de mod√®les d'AA dans un RDI : chaque nŇďud met √† jour dynamiquement son ensemble local de mod√®les d'inf√©rence en fonction des demandes observ√©es au cours du pass√© r√©cent et d'un √©change d'informations limit√© avec ses nŇďuds voisins. Finalement, nous √©tudions l'√©quit√© du probl√®me d'allocation des ressources r√©seau sous le crit√®re d'alpha-fairness. Nous reconnaissons deux objectifs d'√©quit√© diff√©rents qui surgissent naturellement dans ce probl√®me : l'objectif d'√©quit√© de tranche bien compris qui vise √† assurer l'√©quit√© √† chaque tranche de temps, et l'objectif d'√©quit√© d'horizon moins explor√© qui vise √† assurer l'√©quit√© entre les utilit√©s accumul√©es sur un horizon temporel. Nous √©tudions l'√©quit√© de l'horizon avec le regret comme m√©trique de performance et montrons que la disparition du regret ne peut √™tre atteinte en pr√©sence d'un adversaire sans restriction. Nous proposons des restrictions sur les capacit√©s de l'adversaire correspondant √† des sc√©narios r√©alistes et un algorithme adaptatif qui garantit en effet la disparition du regret sous ces restrictions.Network resource allocation is a complex and fundamental problem in computer science. It is a process in which components of a networked system aim to provide a faster service to demands, or to reduce the computation or communication load on the system. The main factors that contribute to the complexity of this problem are that the demands arrive to the system in an unpredictable and sequential fashion and may compete for the different network resources. The ubiquity of network resource allocation problems has motivated extensive research to design new policies with provable guarantees. This thesis investigates several instances of the network resource allocation problem and proposes online policies with strong performance guarantees leveraging the online learning framework.First, we study the online caching problem in which demands for files can be served by a local cache to avoid retrieval costs from a remote server. We study no-regret algorithms based on online mirror descent (OMD) strategies. We show that the optimal OMD strategy depends on the request diversity present in a batch of demands. We also prove that, when the cache must store the entire file, rather than a fraction, OMD strategies can be coupled with a randomized rounding scheme that preserves regret guarantees. We also present an extension to cache networks, and we propose a no-regret distributed online policy.Second, we investigate similarity caches that can reply to a demand for an object with similar objects stored locally. We propose a new online similarity caching policy that employs gradient descent to navigate the continuous representation space of objects and find appropriate objects to store in the cache. We provide theoretical convergence guarantees under stationary demands and show the proposed policy reduces service costs incurred by the system for 360-video delivery systems and recommendation systems. Subsequently, we show that the similarity caching problem can be formulated in the online learning framework by utilizing an OMD policy paired with randomized rounding to achieve a no-regret guarantee.Third, we present the novel idea of inference delivery networks (IDNs), networks of computing nodes that coordinate to satisfy machine learning (ML) inference demands achieving the best trade-off between latency and accuracy. IDNs bridge the dichotomy between device and cloud execution by integrating inference delivery at the various tiers of the infrastructure continuum (access, edge, regional data center, cloud). We propose a no-regret distributed dynamic policy for ML model allocation in an IDN: each node dynamically updates its local set of inference models based on demands observed during the recent past plus limited information exchange with its neighboring nodes.Finally, we study the fairness of network resource allocation problem under the alpha-fairness criterion. We recognize two different fairness objectives that naturally arise in this problem: the well-understood slot-fairness objective that aims to ensure fairness at every timeslot, and the less explored horizon-fairness objective that aims to ensure fairness across utilities accumulated over a time horizon. We argue that horizon-fairness comes at a lower price in terms of social welfare. We study horizon-fairness with the regret as a performance metric and show that vanishing regret cannot be achieved in presence of an unrestricted adversary. We propose restrictions on the adversary's capabilities corresponding to realistic scenarios and an online policy that indeed guarantees vanishing regret under these restrictions. We demonstrate the applicability of the proposed fairness framework to a representative resource management problem considering a virtualized caching system where different caches cooperate to serve content requests

    No-Regret Caching via Online Mirror Descent

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    We study an online caching problem in which requests can be served by a local cache to avoid retrieval costs from a remote server. The cache can update its state after a batch of requests and store an arbitrarily small fraction of each content. We study no-regret algorithms based on Online Mirror Descent (OMD) strategies. We show that the optimal OMD strategy depends on the request diversity present in a batch. We also prove that, when the cache must store the entire content, rather than a fraction, OMD strategies can be coupled with a randomized rounding scheme that preserves regret guarantees

    Enabling Long-term Fairness in Dynamic Resource Allocation

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    International audienceWe study the fairness of dynamic resource allocation problem under the őĪ-fairness criterion. We recognize two different fairness objectives that naturally arise in this problem: the well-understood slot-fairness objective that aims to ensure fairness at every timeslot, and the less explored horizon-fairness objective that aims to ensure fairness across utilities accumulated over a time horizon. We argue that horizon-fairness comes at a lower price in terms of social welfare. We study horizon-fairness with the regret as a performance metric and show that vanishing regret cannot be achieved in presence of an unrestricted adversary. We propose restrictions on the adversary's capabilities corresponding to realistic scenarios and an online policy that indeed guarantees vanishing regret under these restrictions. We demonstrate the applicability of the proposed fairness framework to a representative resource management problem considering a virtualized caching system where different caches cooperate to serve content requests

    AÇAI: Ascent Similarity Caching with Approximate Indexes

    No full text
    International audienceSimilarity search is a key operation in multimedia retrieval systems and recommender systems, and it will play an important role also for future machine learning and augmented reality applications. When these systems need to serve large objects with tight delay constraints, edge servers close to the end-user can operate as similarity caches to speed up the retrieval. In this paper we present AÇAI, a new similarity caching policy which improves on the state of the art by using (i) an (approximate) index for the whole catalog to decide which objects to serve locally and which to retrieve from the remote server, and (ii) a mirror ascent algorithm to update the set of local objects with strong guarantees even when the request process does not exhibit any statistical regularity

    Ascent Similarity Caching with Approximate Indexes

    No full text
    International audienceSimilarity search is a key operation in multimedia retrieval systems and recommender systems, and it will play an important role also for future machine learning and augmented reality applications. When these systems need to serve large objects with tight delay constraints, edge servers close to the enduser can operate as similarity caches to speed up the retrieval. In this paper we present AC ¬łAI, a new similarity caching policy which improves on the state of the art by using (i) an (approximate) index for the whole catalog to decide which objects to serve locally and which to retrieve from the remote server, and (ii) a mirror ascent algorithm to update the set of local objects with strong guarantees even when the request process does not exhibit any statistical regularity

    No-Regret Caching via Online Mirror Descent

    No full text
    International audienceWe study an online caching problem in which requests can be served by a local cache to avoid retrieval costs from a remote server. The cache can update its state after a batch of requests and store an arbitrarily small fraction of each content. We study no-regret algorithms based on Online Mirror Descent (OMD) strategies. We show that the choice of OMD strategy depends on the request diversity present in a batch and that OMD caching policies may outperform traditional eviction-based policies
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