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This work presents a new way of exploiting non-uniform file popularity in coded caching networks. Focusing on a fully-connected fully-interfering wireless setting with multiple cache-enabled transmitters and receivers, we show how non-uniform file popularity can be used very efficiently to accelerate the impact of transmitter-side data redundancy on receiver-side coded caching. This approach is motivated by the recent discovery that, under any realistic file-size constraint, having content appear in multiple transmitters can in fact dramatically boost the speed-up factor attributed to coded caching. We formulate an optimization problem that exploits file popularity to optimize the placement of files at the transmitters. We then provide a proof that reduces significantly the variable search space, and propose a new search algorithm that solves the problem at hand. We also prove an analytical performance upper bound, which is in fact met by our algorithm in the regime of many receivers. Our work reflects the benefits of allocating higher cache redundancy to more popular files, but also reflects a law of diminishing returns where for example very popular files may in fact benefit from minimum redundancy. In the end, this work reveals that in the context of coded caching, employing multiple transmitters can be a catalyst in fully exploiting file popularity, as it avoids various asymmetry complications that appear when file popularity is used to alter the receiver-side cache placement., 14 pages, submitted to Transactions on Networking

—Recommendations are employed by Content Providers (CPs) of streaming services in order to boost user engagement and their revenues. Recent works suggest that nudging recommendations towards cached items can reduce operational costs in the caching networks, e.g., Content Delivery Networks (CDNs) or edge cache providers in future wireless networks. However, cache-friendly recommendations could deviate from users’ tastes, and potentially affect the CP’s revenues. Motivated by real-world business models, this work identifies the misalignment of the financial goals of the CP and the caching network provider, and presents a network-economic framework for recommendations. We propose a cooperation mechanism leveraging the Nash bargaining solution that allows the two entities to jointly design the recommendation policy. We consider different problem instances that vary on the extent these entities are willing to share their cost and revenue models, and propose two cooperative policies, CCR and DCR, that allow them to make decisions in a centralized or distributed way. In both cases, our solution guarantees reaching a fair and Pareto optimal allocation of the cooperation gains. Moreover, we discuss the extension of our framework towards caching decisions. A wealth of numerical experiments in realistic scenarios show the policies lead to significant gains for both entities.

Controlling the network cost by delivering popular content to users, as well as improving streaming quality and overall user experience, have been key goals for content providers (CP) in recent years. While proposals to improve performance, through caching or other mechanisms (DASH, multicasting, etc.) abound, recent works have proposed to turn the problem on its head and complement such efforts. Instead of trying to reduce the cost to deliver every possible content to a user, a potentially very expensive endeavour, one could leverage omnipresent recommendations systems to nudge users towards the content of low(er) network cost, regardless of where this cost is coming from. In this paper, we focus on this latter problem, namely optimal policies for “Network Friendly Recommendations” (NFR). A key contribution is the use of a Markov Decision Process (MDP) framework that offers significant advantages, compared to existing works, in terms of both modeling flexibility as well as computational efficiency. Specifically we show that this framework subsumes some state-of-the-art approaches, and can also optimally tackle additional, more sophisticated setups. We validate our findings with real traces that suggest up to almost 2X in cost performance, and 10X in computational speed-up compared to recent state-of-the-art works.

In decentralized optimization environments, each agent $i$ in a network of $n$ nodes has its own private function $f_i$, and nodes communicate with their neighbors to cooperatively minimize the aggregate objective $\sum_{i=1}^n f_i$. In this setting, synchronizing the nodes' updates incurs significant communication overhead and computational costs, so much of the recent literature has focused on the analysis and design of asynchronous optimization algorithms, where agents activate and communicate at arbitrary times without needing a global synchronization enforcer. However, most works assume that when a node activates, it selects the neighbor to contact based on a fixed probability (e.g., uniformly at random), a choice that ignores the optimization landscape at the moment of activation. Instead, in this work we introduce an optimization-aware selection rule that chooses the neighbor providing the highest dual cost improvement (a quantity related to a dualization of the problem based on consensus). This scheme is related to the coordinate descent (CD) method with the Gauss-Southwell (GS) rule for coordinate updates; in our setting however, only a subset of coordinates is accessible at each iteration (because each node can communicate only with its neighbors), so the existing literature on GS methods does not apply. To overcome this difficulty, we develop a new analytical framework for smooth and strongly convex $f_i$ that covers the class of set-wise CD algorithms -- a class that directly applies to decentralized scenarios, but is not limited to them -- and we show that the proposed set-wise GS rule achieves a speedup factor of up to the maximum degree in the network (which is in the order of $\Theta(n)$ for highly connected graphs). The speedup predicted by our analysis is validated in numerical experiments with synthetic data., Comment: Revised writing, added references

Device-to-device (D2D) relaying is a concept, where some users relay data of cell-edge users (CUEs) experiencing a bad channel quality to a base station. While this research topic has received plenty of attention, a critical aspect of the D2D relaying remains a selfish nature of the users and their limited willingness to relay data for others. Thus, we propose a scheme to identify potential candidates for the relaying and provide a sound incentive to these relaying users (RUEs) to motivate them helping other users. First, we provide a detailed theoretical analysis showing when and if the relaying is beneficial for the CUE(s) and related RUE. Second, to choose among all possible incentive-compliant relaying options, we formulate the optimal CUE-to-RUE matching problem maximizing a network-wide performance. Since the optimal solution is hard to obtain for a high number of users, we propose a low-complexity greedy algorithm and prove its constant worst-case approximation guarantees to the optimum. Finally, we derive a closed-form expression for a fair allocation of the resources among the CUEs and the RUEs. The proposed framework more than doubles the users’ capacity and/or reduces the energy consumption by up to 87% comparing to existing incentive-based relaying schemes.

Caching algorithms try to predict content popularity, and place the content closer to the users. Additionally, nowadays requests are increasingly driven by recommendation systems (RS). These important trends, point to the following: \emph{make RSs favor locally cached content}, this way operators reduce network costs, and users get better streaming rates. Nevertheless, this process should preserve the quality of the recommendations (QoR). In this work, we propose a Markov Chain model for a stochastic, recommendation-driven \emph{sequence} of requests, and formulate the problem of selecting high quality recommendations that minimize the network cost \emph{in the long run}. While the original optimization problem is non-convex, it can be convexified through a series of transformations. Moreover, we extend our framework for users who show preference in some positions of the recommendations' list. To our best knowledge, this is the first work to provide an optimal polynomial-time algorithm for these problems. Finally, testing our algorithms on real datasets suggests significant potential, e.g., $2\times$ improvement compared to baseline recommendations, and 80\% compared to a greedy network-friendly-RS (which optimizes the cost for I.I.D. requests), while preserving at least 90\% of the original QoR. Finally, we show that taking position preference into account leads to additional performance gains.

As mobile traffic is dominated by content services (e.g., video), which typically use recommendation systems, the paradigm of network-friendly recommendations (NFR) has been proposed recently to boost the network performance by promoting content that can be efficiently delivered (e.g., cached at the edge). NFR increase the network performance, however, at the cost of being unfair towards certain contents when compared to the standard recommendations. This unfairness is a side effect of NFR that has not been studied in literature. Nevertheless, retaining fairness among contents is a key operational requirement for content providers. This paper is the first to study the fairness in NFR, and design fair-NFR. Specifically, we use a set of metrics that capture different notions of fairness, and study the unfairness created by existing NFR schemes. Our analysis reveals that NFR can be significantly unfair. We identify an inherent trade-off between the network gains achieved by NFR and the resulting unfairness, and derive bounds for this trade-off. We show that existing NFR schemes frequently operate far from the bounds, i.e., there is room for improvement. To this end, we formulate the design of Fair-NFR (i.e., NFR with fairness guarantees compared to the baseline recommendations) as a linear optimization problem. Our results show that the Fair-NFR can achieve high network gains (similar to non-fair-NFR) with little unfairness., IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2021

International audience; In 5G and beyond network architectures, operators and content providers base their content distribution strategies on Heterogeneous Networks, where macro and small cells are combined to offer better Quality of Service to wireless users. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) joint transmissions are used to further improve performance. In this paper, we address the average delay minimization problem by first formulating it as a static optimization problem. Even though the problem is NP-hard we are able to solve it via an efficient algorithm that guarantees a 1 2-approximation ratio. We then proceed to propose two fully distributed and dynamic caching policies for the same problem. The first one asymptotically converges to the static optimal solution under the Independent Reference Model (IRM). The second one provides better results in practice under real (nonstationary) request processes. Our online policies outperform existing dynamic solutions that are PHY-unaware.

Caching content over CDNs or at the network edge has been solidified as a means to improve network cost and offer better streaming experience to users. Furthermore, nudging the users towards low-cost content has recently gained momentum as a strategy to boost network performance. We focus on the problem of optimal policy design for Network Friendly Recommendations (NFR). We depart from recent modeling attempts, and propose a Markov Decision Process (MDP) formulation. MDPs offer a unified framework that can model a user with random session length. As it turns out, many state-of-the-art approaches can be cast as subcases of our MDP formulation. Moreover, the approach offers flexibility to model users who are reactive to the quality of the received recommendations. In terms of performance, for users consuming an arbitrary number of contents in sequence, we show theoretically and using extensive validation over real traces that the MDP approach outperforms myopic algorithms both in session cost as well as in offered recommendation quality. Finally, even compared to optimal state-of-art algorithms targeting specific subcases, our MDP framework is significantly more efficient, speeding the execution time by a factor of 10, and enjoying better scaling with the content catalog and recommendation batch sizes., Comment: 10 pages double column, accepted at IEEE INFOCOM 2021. This is the reviewed version submitted by the authors

International audience; We consider a dense cellular network, in which a limited-size cache is available at every base station (BS). Coordinating content allocation across the different caches can lead to significant performance gains, but is a difficult problem even when full information about the network and the request process is available. In this paper we present q LRU- Δ , a general-purpose online caching policy that can be tailored to optimize different performance metrics also in presence of coordinated multipoint transmission techniques. The policy requires neither direct communication among BSs, nor a priori knowledge of content popularity and, under stationary request processes, has provable performance guarantees.

Recommender systems have been established as a key component of video streaming services, shaping up to 80% of content requests. Hence, recommendations are employed by the Content Providers (CPs) of these services to increase the viewing time and their revenues. Furthermore, it has been recently suggested that recommendations could be a means to reduce the operational costs of the Content Delivery Networks (CDNs) when they are related to already cached items, i.e., when they are cache-friendly. Clearly, these conflicting objectives, i.e., increasing revenue for the CP and reducing costs for the CDN, can create tensions between the two entities, and hence, prevent the full utilization of recommendations. In this work, we propose a model for capturing these tradeoffs, and an economic mechanism, based on the Nash bargaining solution, for reconciling the potentially conflicting objectives of the CP and the CDN. Our scheme enables the CP and CDN to jointly design the recommendations in a way that balances the revenue gains and cost savings, ensuring a fair and Pareto optimal split of the accrued benefits for both entities. Our numerical experiments in realistic scenarios show that the proposed scheme leads to important financial gains of up to 30%.

Joint caching and recommendation has been recently proposed as a new paradigm for increasing the efficiency of mobile edge caching. Early findings demonstrate significant gains for the network performance. However, previous works evaluated the proposed schemes exclusively on simulation environments. Hence, it still remains uncertain whether the claimed benefits would change in real settings. In this paper, we propose a methodology that enables to evaluate joint network and recommendation schemes in real content services by only using publicly available information. We apply our methodology to the YouTube service, and conduct extensive measurements to investigate the potential performance gains. Our results show that significant gains can be achieved in practice; e.g., 8 to 10 times increase in the cache hit ratio from cache-aware recommendations. Finally, we build an experimental testbed and conduct experiments with real users; we make available our code and datasets to facilitate further research. To our best knowledge, this is the first realistic evaluation (over a real service, with real measurements and user experiments) of the joint caching and recommendations paradigm. Our findings provide experimental evidence for the feasibility and benefits of this paradigm, validate assumptions of previous works, and provide insights that can drive future research., arXiv admin note: text overlap with arXiv:1806.02704

International audience; Densification through small cells and caching in base stations have been proposed to deal with the increasing demand for Internet content and the related overload on the cellular infrastructure. However, these solutions are expensive to install and maintain. Instead, using vehicles acting as mobile caches might represent an interesting alternative. In our work, we assume that users can query nearby vehicles for some time, and be redirected to the cellular infrastructure when the deadline expires. Beyond reducing costs, in such an architecture, through vehicle mobility, a user sees a much larger variety of locally accessible content within only few minutes. Unlike most of the related works on delay tolerant access, we consider the impact on the user experience by assigning different retrieval deadlines per content. In our paper, we provide the following contributions: (i) we model analytically such a scenario; (ii) we formulate an optimization problem to maximize the traffic offloaded while ensuring user experience guarantees; (iii) we propose two variable deadline policies; (iv) we perform realistic trace-based simulations, and we show that, even with low technology penetration rate, more than 60% of the total traffic can be offloaded which is around 20% larger compared to existing allocation policies.

International audience; On streaming platforms such as Youtube and Netflix, recommendations influence a large share of content consumption. In this context, user experience depends on both the quality of the recommendations (QoR) and the quality of service (QoS) of the delivered content. However, network decisions (such as caching) affecting QoS are usually made without explicit knowledge of the recommender's actions. Similarly, recommendation decisions are made without considering the potential delivery quality of the recommended content. In this paper, we propose to jointly optimize caching and recommendations in a generic network of caches, towards maximizing the quality of experience (QoE). This coincides with the recent trend for large content providers to also act as Content Delivery Network (CDN) owners. We formulate this joint optimization problem and prove that it can be approximated up to a constant. To the best of our knowledge, this is the first polynomial algorithm to achieve a constant approximation ratio for the joint problem. Our numerical experiments show important performance gains of the proposed algorithm over baseline schemes and existing algorithms.

International audience; In 5G and beyond network architectures, operators and content providers base their content distribution strategies on Heterogeneous Networks, where macro and small(er) cells are combined to offer better Quality of Service (QoS) to wireless users. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) transmissions are used to further improve performance. The problem of optimally utilizing the cache space in dense and heterogeneous cell networks has been extensively studied under the name of "FemtoCaching." However, related literature usually assumes relatively simple physical layer (PHY) setups and known or stationary content popularity. In this paper, we address these issues by proposing a class of fully distributed and dynamic caching algorithms that take advantage of CoMP capabilities towards minimizing PHY-aware metrics, such as end-to-end (E2E) delay. Our policies outperform existing dynamic solutions that are PHY-unaware, under both synthetic and real (non-stationary) request processes, and converge to efficient centralized solutions, in static setups.

Streaming platforms, like Netflix and YouTube, strive to offer high streaming quality (SQ), in terms of bitrate, delays, etc., to their users. Meanwhile, a significant share of content consumption of these platforms is heavily influenced by recommendations. In this setting, the user's overall experience is a product of both the user's interest in a recommended content, i.e., the recommendation quality (RQ), and the SQ of this content. However, network decisions (like caching) that affect the SQ are usually made without considering the recommender's actions. Likewise, recommendations are chosen independently of the potential delivery quality. In this paper, we define a metric of streaming experience (MoSE) that captures the fundamental tradeoff between the SQ and RQ. We aim to jointly optimize caching and recommendations in a generic network of caches, with the objective of maximizing this metric. This is in line with the recent trend for content providers to simultaneously act as Content Delivery Network owners, implying that the same entity may handle both caching and recommendation decisions. We formulate this joint optimization problem and prove that it can be approximated up to a constant factor. To the best of our knowledge, this is the first polynomial algorithm to achieve a constant approximation ratio for the joint problem. Moreover, our numerical experiments show important performance gains of our algorithm over baseline schemes and existing algorithms in the literature.

Cognitive radio networks (CRNs) have been proposed to exploit licensed bands opportunistically, with secondary users’ (SU) activity being subordinated to primary users (PU). The two most popular types of spectrum access in the CRN are interweave and underlay . There is no clear consensus which one provides better results, for different metrics, despite the fact that there has been a lot of research dedicated to each mode. In this paper, we analyze this problem theoretically, providing formulas that are in closed form. These expressions allow the performance comparison of interweave and underlay modes under a unified network setup. Our focus are two metrics, throughput and delay , which we analyze relying on the renewal-reward theory and queueing theory, respectively. These results enable an SU to decide what mode to use depending on what the optimization objective is, given the key network parameters. Furthermore, relying on the results of our analysis, we propose hybrid policies, which are dynamic, and allow the SU to switch between the two modes at any point. These policies offer an additional performance improvement of up to 50%. We validate our results with extensive realistic simulations.

Sistemas de recomendação estão cada vez mais presentes na rotina dos usuários da Internet. Portanto, plataformas como Youtube e Netflix procuram melhorar suas recomendações para fornecer uma melhor experiência aos seus usuários. No entanto, a experiência dos usuários depende de diversos fatores. Em particular, sistemas de cache têm uma grande influência na qualidade de experiência (QoE), visto que eles impactam métricas de qualidade de serviço (QoS), como atraso e vazão, experenciadas pelos usuários. Nosso objetivo é estudar a viabilidade de um sistema de recomendação sensível a QoS que maximize a QoE. Para tal, nós conduzimos experimentos com usuários reais com perfis distintos. Cada usuário é solicitado a avaliar diferentes vídeos, que variam em termos de conteúdo e de QoS. Dadas nossas descobertas quanto à relação entre QoS e QoR (quality of recommendation), investigamos seus impactos no modelo de sistema de recomendação. Um classificador baseado em árvore de decisão alcançou acurácia de 77% usando validação cruzada, e nos permite entender melhor o processo de tomada de decisão do usuário.

International audience; Caching content at the edge of mobile networks is considered as a promising way to deal with the data tsunami. In addition to caching at fixed base stations or user devices, it has been recently proposed that an architecture with public or private transportation acting as mobile relays and caches might be a promising middle ground. While such mobile caches have mostly been considered in the context of delay tolerant networks, in this paper we argue that they could be used for low cost video streaming without the need to impose any delay on the user. Users can prefetch video chunks into their playout buffer from encountered vehicle caches (at low cost) or stream from the cellular infrastructure (at higher cost) when their playout buffer empties while watching the content. Our main contributions are: (i) to model the playout buffer in the user device and analyze its idle periods which correspond to bytes downloaded from the infrastructure; (ii) to optimize the content allocation to mobile caches, to minimize the expected number of non-offloaded bytes. We perform trace-based simulations to support our findings showing that up to 60 percent of the original traffic could be offloaded from the main infrastructure.

Caching has recently attracted a lot of attention in the wireless communications community, as a means to cope with the increasing number of users consuming web content from mobile devices. Caching offers an opportunity for a win-win scenario: nearby content can improve the video streaming experience for the user, and free up valuable network resources for the operator. At the same time, recent works have shown that recommendations of popular content apps are responsible for a significant percentage of users requests. As a result, some very recent works have considered how to nudge recommendations to facilitate the network (e.g., increase cache hit rates). In this paper, we follow up on this line of work, and consider the problem of designing cache friendly recommendations for long viewing sessions; specifically, we attempt to answer two open questions in this context: (i) given that recommendation position affects user click rates, what is the impact on the performance of such network-friendly recommender solutions? (ii) can the resulting optimization problems be solved efficiently, when considering both sequences of dependent accesses (e.g., YouTube) and position preference? To this end, we propose a stochastic model that incorporates position-aware recommendations into a Markovian traversal model of the content catalog, and derive the average cost of a user session using absorbing Markov chain theory. We then formulate the optimization problem, and after a careful sequence of equivalent transformations show that it has a linear program equivalent and thus can be solved efficiently. Finally, we use a range of real datasets we collected to investigate the impact of position preference in recommendations on the proposed optimal algorithm. Our results suggest more than 30\% improvement with respect to state-of-the-art methods.

International audience; Using vehicles equipped with small caches as small base stations has recently been proposed as an interesting middle ground between caching at fixed base stations (which has higher CAPEX/OPEX), and caching at user devices (which has resource limitations). A typical problem in this setup is which content to store in which vehicles. The correct answer depends on the application. Indeed, if the stored content will be streamed (not downloaded), then this offers a natural delay tolerance: latter parts of the content do not need to be downloaded immediately from expensive links (e.g., macro-cells), but could be fetched from encountered vehicles cheaply. In an earlier work, we formulated a related optimal cache allocation problem, in which the proposed solution stores a content in its entirety. In light of recent statistics suggesting that different parts of a content (e.g., YouTube clips) are not watched equally frequently, this method is suboptimal. In this paper, we thus consider per-chunk allocation, and propose a simple two-step heuristic that first allocates the vehicular cloud capacity among content items, then efficiently distributes the capacity for a specific content among its chunks. Trace-driven simulation results suggest that chunk-based allocation can lead to considerable gains.

Caching has been successfully applied in wired networks, in the context of Content Distribution Networks (CDNs), and is quickly gaining ground for wireless systems. Storing popular content at the edge of the network (e.g. at small cells) is seen as a `win-win' for both the user (reduced access latency) and the operator (reduced load on the transport network and core servers). Nevertheless, the much smaller size of such edge caches, and the volatility of user preferences suggest that standard caching methods do not suffice in this context. What is more, simple popularity-based models commonly used (e.g. IRM) are becoming outdated, as users often consume multiple contents in sequence (e.g. YouTube, Spotify), and this consumption is driven by recommendation systems. The latter presents a great opportunity to bias the recommender to minimize content access cost (e.g. maximizing cache hit rates). To this end, in this paper we first propose a Markovian model for recommendation-driven user requests. We then formulate the problem of biasing the recommendation algorithm to minimize access cost, while maintaining acceptable recommendation quality. We show that the problem is non-convex, and propose an iterative ADMM-based algorithm that outperforms existing schemes, and shows significant potential for performance improvement on real content datasets.

Opportunistic or delay-tolerant networks (DTNs) may be used to enable communication in case of failure or lack of infrastructure (disaster, censorship, remote areas) and to complement existing wireless technologies (cellular, WiFi). Wireless peers communicate when in contact, forming an impromptu network, whose connectivity graph is highly dynamic and only partly connected. In this harsh environment, communication algorithms are mostly local search heuristics, choosing a solution among the locally available ones. Furthermore, they are routinely evaluated through simulations only, as they are hard to model analytically. Even when more insight is sought from models, these usually assume homogeneous node meeting rates, thereby ignoring the attested heterogeneity and nontrivial structure of human mobility. We propose DTN-Meteo, a new unified analytical model that maps an important class of DTN optimization problems over heterogeneous mobility/contact models into a Markov chain traversal over the relevant solution space. (Heterogeneous) meeting probabilities between different pairs of nodes dictate the chain's transition probabilities and determine neighboring solutions. Local optimization algorithms can accept/reject candidate transitions (deterministically or randomly), thus "modulating" the above transition probabilities. We apply our model to two example problems: routing and content placement. We predict the performance of state-of-the-art algorithms (SimBet, BubbleRap) in various real and synthetic mobility scenarios and show that surprising precision can be achieved against simulations, despite the complexity of the problems and diversity of settings. To our best knowledge, this is the first analytical work that can accurately predict performance for utility-based algorithms and heterogeneous node contact rates.

International audience; Caching content at the edge of mobile networks is considered as a promising way to deal with the data tsunami. In addition to caching at fixed base stations or user devices, it has been recently proposed that an architecture with public or private transportation acting as mobile relays and caches might be a promising middle ground. In previous work, we have assumed users are streaming video files, and analyzed how many replicas of each video file to cache in such a vehicular fleet, towards minimizing the amount of bits per file downloaded from (expensive) infrastructure links. However, this work has been assuming that a vehicle will store the entire content, or none of it. In practice, later chunks have an inherent “delay tolerance” as there is more time to find them before they must be played out. What is more, numerous studies as well as everyday experience suggest that most files (e.g. YouTube) ones are not entirely watched. This makes the previous policies suboptimal, as fewer (or no) replicas could be allocated to late chunks of a file and more to the most popular chunks. In this work, we formulate an optimization problem to compute the optimal allocation per chunk, to minimize the load on the cellular infrastructure, and we show that significant performance gains can be achieved compared to per content allocation policies.

International audience; Network densification via small cells is considered as a key step to cope with the data tsunami. Caching data at small cells or even user devices is also considered as a promising way to alleviate the backhaul congestion this densification might cause. However, the former suffers from high deployment and maintenance costs, and the latter from limited resources and privacy issues with user devices. We argue that an architecture with (public or private) vehicles acting as mobile caches and communication relays might be a promising middle ground. In this paper, we assume such a vehicular cloud is in place to provide video streaming to users, and that the operator can decide which content to store in the vehicle caches. Users can then greedily fill their playout buffer with video pieces of the streamed content from encountered vehicles, and turn to the infrastructure immediately when the playout buffer is empty, to ensure uninterrupted streaming. Our main contribution is to model the playout buffer in the user device with a queuing approach, and to provide a mathematical formulation for the idle periods of this buffer, which relate to the bytes downloadedfrom the cellular infrastructure. We also solve the resulting content allocation problem, and perform trace based simulations to finally show that up to 50% of the original traffic could be offloaded from the main infrastructure.

International audience; The increasing demand for mobile data is overloading the cellular infrastructure. Small cells and edge caching is being explored as an alternative, but installation and maintenance costs for sufficient coverage are significant. In this work, weperform a preliminary study of an alternative architecture based on two main ideas: (i) using vehicles as mobile caches that can be accessed by user devices; compared to small cells, vehicles are more widespread and require lower costs; (ii) combining the mobility of vehicles with delayed content access to increase the number of cache hits (and reduce the load on the infrastructure). Contrary to standard DTN-type approaches, in our system max delays are guaranteed to be kept to a few minutes (beyond this deadline, the content is fetched from the infrastructure). We first propose an analytical framework to compute the optimal number of content replicas that one should cache, in order to minimize the infrastructure load. We then investigate how to optimally refresh these caches to introduce new contents, as well as to react to the temporal variability in content popularity. Simulations suggest that our vehicular cloud considerably reduces the infrastructure load in urban settings, assuming modest penetration rates and tolerable content access delays.

Mobile users are envisioned to exploit direct communication opportunities between their portable devices, in order to enrich the set of services they can access through cellular or WiFi networks. Sharing contents of common interest or providing access to resources or services between peers can enhance a mobile node's capabilities, offload the cellular network, and disseminate information to nodes without Internet access. Interest patterns, i.e., how many nodes are interested in each content or service (popularity), as well as how many users can provide a content or service (availability) impact the performance and feasibility of envisioned applications. In this paper, we establish an analytical framework to study the effects of these factors on the delay and success probability of a content/service access request through opportunistic communication. We also apply our framework to the mobile data offloading problem and provide insights for the optimization of its performance. We validate our model and results through realistic simulations, using datasets of real opportunistic networks.

Caching popular content at the edge of future mobile networks has been widely considered in order to alleviate the impact of the data tsunami on both the access and backhaul networks. A number of interesting techniques have been proposed, including femto-caching and "delayed" or opportunistic cache access. Nevertheless, the majority of these approaches suffer from the rather limited storage capacity of the edge caches, compared to the tremendous and rapidly increasing size of the Internet content catalog. We propose to depart from the assumption of hard cache misses, common in most existing works, and consider "soft" cache misses, where if the original content is not available, an alternative content that is locally cached can be recommended. Given that Internet content consumption is increasingly entertainment-oriented, we believe that a related content could often lead to complete or at least partial user satisfaction, without the need to retrieve the original content over expensive links. In this paper, we formulate the problem of optimal edge caching with soft cache hits, in the context of delayed access, and analyze the expected gains. We then show using synthetic and real datasets of related video contents that promising caching gains could be achieved in practice.

Interference is an inherent characteristic of wireless (multihop) communications. Adding interference-awareness to important control functions, e.g., routing, could significantly enhance the overall network performance. Despite some initial efforts, it is not yet clearly understood how to best capture the effects of interference in routing protocol design. Most existing proposals aim at inferring its effect by actively probing the link. However, active probe measurements impose an overhead and may often misrepresent the link quality due to their interaction with other networking functions. Therefore, in this paper we follow a different approach and: 1) propose a simple yet accurate analytical model for the effect of interference on data reception probability, based only on passive measurements and information locally available at the node; 2) use this model to design an efficient interference-aware routing protocol that performs as well as probing-based protocols, yet avoids all pitfalls related to active probe measurements. To validate our proposal, we have performed experiments in a real testbed, setup in our indoor office environment. We show that the analytical predictions of our interference model exhibit good match with both experimental results as well as more complicated analytical models proposed in related literature. Furthermore, we demonstrate that a simple probeless routing protocol based on our model performs at least as good as well-known probe-based routing protocols in a large set of experiments including both intraflow and interflow interference.

Communication networks, whether they are wired or wireless, have traditionally been assumed to be connected at least most of the time. However, emerging applications such as emergency response, special operations, smart environments, VANETs, etc. coupled with node heterogeneity and volatile links (e.g. due to wireless propagation phenomena and node mobility) will likely change the typical conditions under which networks operate. In fact, in such scenarios, networks may be mostly disconnected, i.e., most of the time, end-to-end paths connecting every node pair do not exist. To cope with frequent, long-lived disconnections, opportunistic routing techniques have been proposed in which, at every hop, a node decides whether it should forward or store-and-carry a message. Despite a growing number of such proposals, there still exists little consensus on the most suitable routing algorithm(s) in this context. One of the reasons is the large diversity of emerging wireless applications and networks exhibiting such “episodic” connectivity. These networks often have very different characteristics and requirements, making it very difficult, if not impossible, to design a routing solution that fits all. In this paper, we first break up existing routing strategies into a small number of common and tunable routing modules (e.g. message replication, coding, etc.), and then show how and when a given routing module should be used, depending on the set of network characteristics exhibited by the wireless application. We further attempt to create a taxonomy for intermittently connected networks. We try to identify generic network characteristics that are relevant to the routing process (e.g., network density, node heterogeneity, mobility patterns) and dissect different “challenged” wireless networks or applications based on these characteristics. Our goal is to identify a set of useful design guidelines that will enable one to choose an appropriate routing protocol for the application or network in hand. Finally, to demonstrate the utility of our approach, we take up some case studies of challenged wireless networks, and validate some of our routing design principles using simulations.

Realistic mobility models are fundamental to evaluate the performance of protocols in mobile ad hoc networks. Unfortunately, there are no mobility models that capture the non-homogeneous behaviors in both space and time commonly found in reality, while at the same time being easy to use and analyze. Motivated by this, we propose a time-variant community mobility model, referred to as the TVC model, which realistically captures spatial and temporal correlations. We devise the communities that lead to skewed location visiting preferences, and time periods that allow us to model time dependent behaviors and periodic re-appearances of nodes at specific locations. To demonstrate the power and flexibility of the TVC model, we use it to generate synthetic traces that match the characteristics of a number of qualitatively different mobility traces, including wireless LAN traces, vehicular mobility traces, and human encounter traces. More importantly, we show that, despite the high level of realism achieved, our TVC model is still theoretically tractable. To establish this, we derive a number of important quantities related to protocol performance, such as the average node degree, the hitting time, and the meeting time, and provide examples of how to utilize this theory to guide design decisions in routing protocols., Comment: 14 pages, 9 figures

International audience; Emerging challenged networks require new protocols and strategies to cope with a high degree of mobility, high de- lays and unknown, possibly non-existing routes within the network. Researchers have proposed di erent store-carry- and-forward protocols for data delivery in challenged net- works. These have been complemented with appropriate drop and scheduling policies that deal with the limitations of the nodes' bu ers and the limited duration of opportunis- tic encounters in these networks. Nevertheless, the vast majority of these protocols and strategies are designed for end-to-end transmissions. Yet, a paradigm shift from the traditional way of addressing the endpoints in the network has been occurring towards content-centric networking. To this end, we present CEDO, a content-centric dissemination algorithm for challenged networks. CEDO aims at maximiz- ing the total delivery-rate of distributed content in a setting where a range of contents of di erent popularity may be requested and stored, but nodes have limited resources. It achieves this by maintaining a delivery-rate utility per con- tent that is proportional to the content miss rate and that is used by the nodes to make appropriate drop and scheduling decisions. This delivery-rate utility can be estimated locally by each node using unbiased estimators fed by sampled infor- mation on the mobile network obtained by gossiping. Both simulations and theory suggest that CEDO achieves its set goal, and outperforms a baseline LRU-based policy by 72%, even in relatively small scenarios. The framework followed by CEDO is general enough to be applied to other global performance objectives as well.

Cognitive Networks have been proposed to opportunistically discover and exploit (temporarily) unused licensed spectrum bands. With the exception of TV white spaces, secondary users (SUs) can access the medium only intermittently, due to deferring to primary user (PU) transmissions and scanning for new channels. This raises the following questions: (i) what sort of delays can an SU expect on a channel given the PU utilization of this channel? (ii) how do specific characteristics of the PU activity patterns (e.g. burstiness) further affect performance? These questions are of key importance for the design of efficient algorithms for scheduling, spectrum handoff, etc. In this paper, we propose a queueing analytical model to answer them. We model the PU activity pattern as an ON-OFF alternating renewal process with generic ON and OFF durations, and derive a closed form expression for packet delays by solving a variant of the M/G/1 queue. Contrary to the common belief that low utilization channels are good channels, we show that the expected SU delay on a channel, and thus the best channel to use, is a subtle interplay between the ON and OFF duration distributions of the primary users, and the SU traffic load. We validate our analysis against simulations for different PU activity profiles.

We propose a transport protocol capable of dynamically adapting to network and receiver properties in multi-destination, multi-channel wireless networks. The key feature of our solution resides in its ability to convey common traffic to a group of users, while at the same time distributing information to each user as quickly as possible. This is achieved by clustering receivers in groups, each group being served at a suitable throughput. We emphasize in this study on the two groups of receivers case. We show analytically and through OMNet++ simulations that groups formation is decided by the wireless link performance and the proportion of receivers constituting each group. Our solution captures dynamically these effects. Indeed, our transport is capable to cope transparently with wireless links changes (i.e specturm handoff) by adapting dynamically its transmission rate and groups composition. It is therefore adapted for point-to-multipoint cognitive radio networks.

in IEEE Transactions on Mobile Computing, vol. 11, no. 9, September 2012.; International audience; In order to achieve data delivery in Delay Tolerant Networks (DTN), researchers have proposed the use of store-carry-and-forward protocols: a node there may store a message in its buffer and carry it along for long periods of time, until an appropriate forwarding opportunity arises. This way, messages can traverse disconnected parts of the network. Multiple message replicas are often propagated to further increase delivery probability. This combination of long-term storage and message replication imposes a high storage and bandwidth overhead. Thus, efficient scheduling and drop policies are necessary to: (i) decide on the order by which messages should be replicated when contact durations are limited, and (ii) which messages should be discarded when node buffers operate close to their capacity. In this paper, we propose a practical and efficient joint scheduling and drop policy that can optimize different performance metrics, such as average delay and delivery probability. We first use the theory of encounter-based message dissemination to derive the optimal policy based on global knowledge about the network. Then, we introduce a method that estimates all necessary parameters using locally collected statistics. Based on this, we derive a distributed scheduling and drop policy that can approximate the performance of the optimal policy in practice. Using simulations based on synthetic and real mobility traces, we show that our optimal policy and its distributed variant outperform existing resource allocation schemes for DTNs. Finally, we study how sampled statistics can reduce the signaling overhead of our algorithm and examine its behavior under different congestion regimes. Our results suggest that close to optimal performance can be achieved even when nodes sample a small percentage of the available statistics.

International audience; The rapid proliferation of advanced mobile devices has created a growing demand for data content. Existing approaches (e.g. relying on cellular infrastructures) cannot keep up with the large volume of content generated and requested, without the deployment of new expensive infrastructure. Exchanging content of interest opportunistically, when two nodes are in range, presents a low cost and high bandwidth alternative for popular, bulky content. Yet, efficiently collecting, storing, and sharing the content while preventing selfish users from impairing collaborative ones, poses major challenges. In this paper, we present MobiTrade, a collaborative content dissemination system on top of a delay tolerant network. It allows users to head out in the real world, express locally their interests, and wait to get notified whenever an encountered device has content(s) matching these interests. Even though interactions are done between neighboring wireless devices (locally), MobiTrade implements a trading scheme that motivates mobile devices to act as merchants and carry content across the network to satisfy each other's interests. Users continuously profile the type of content requested and the collaboration level of encountered devices. Based on this knowledge, an appropriate utility function is used to rank these requests and collect an optimal inventory of data that maximizes the expected value of stored content for future encounters. Using NS3 simulations based on synthetic and real mobility traces, we show that MobiTrade achieves up to 2 times higher query success rates compared to other content dissemination schemes. Furthermore, we show that MobiTrade successfully isolates selfish, non-collaborative devices. Finally, using a simple game theoretic framework we show that turning on our MobiTrade mechanism is an efficient Nash Equilibrium.

Routing in wireless mesh networks has been an active area of research for many years, with many proposed routing protocols selecting shortest paths that minimize the path hop count. Whereas minimum hop count is the most popular metric in wired networks, in wireless networks interference- and energy- related considerations give rise to more complex trade-offs. Therefore, a variety of routing metrics has been proposed especially for wireless mesh networks providing routing algorithms with high flexibility in the selection of best path as a compromise among throughput, end-to-end delay, and energy consumption. In this paper, we present a detailed survey and taxonomy of routing metrics. These metrics may have broadly different optimization objectives (e.g., optimize application performance, maximize battery lifetime, maximize network throughput), different methods to collect the required information to produce metric values, and different ways to derive the end-to-end route quality out of the individual link quality metrics. The presentation of the metrics is highly comparative, with emphasis on their strengths and weaknesses and their application to various types of network scenarios. We also discuss the main implications for practitioners and identify open issues for further research in the area.

International audience; Delay Tolerant Networks are wireless networks where disconnections may occur frequently due to propagation phenomena, node mobility, and power outages. Propagation delays may also be long due to the operational environment (e.g. deep space, underwater). In order to achieve data delivery in such challenging networking environments, researchers have proposed the use of \emph{store-carry-and-forward} protocols: there, a node may store a message in its buffer and carry it along for long periods of time, until an appropriate forwarding opportunity arises. Additionally, multiple message replicas are often propagated to increase delivery probability. This combination of long-term storage and replication imposes a high storage overhead on untethered nodes (e.g. handhelds). Thus, efficient buffer management policies are necessary to decide which messages should be discarded, when node buffers are operated close to their capacity. In this paper, we propose efficient buffer management policies for delay tolerant networks. We show that traditional buffer management drop policies like drop-tail or drop-front fail to consider all relevant information in this context and are, thus, sub-optimal. Using the theory of encounter-based message dissemination, we propose an optimal buffer management policy based on global knowledge about the network. Our policy can be tuned either to minimize the average delivery delay or to maximize the average delivery ratio. Finally, we introduce a distributed algorithm that uses statistical learning to approximate the required global knowledge of the optimal algorithm, in practice. Using simulations based on a synthetic mobility model and real mobility traces, we show that our buffer management policy based on statistical learning successfully approximates the performance of the optimal policy in all considered scenarios. At the same time, our policies outperform existing ones in terms of both average delivery ratio and delivery delay.

Communication networks (wired or wireless) have traditionally been assumed to be connected at least most of the time. However, emerging applications such as emergency response, special operations, smart environments, VANETs, etc. coupled with node heterogeneity and volatile links (e.g. due to wireless propagation phenomena and node mobility) will likely change the typical conditions under which networks operate. In fact, in such scenarios, networks may be mostly disconnected, i.e., most of the time, end-to-end paths connecting every node pair do not exist. To cope with frequent, long-lived disconnections, {\em opportunistic routing} techniques have been proposed in which, at every hop, a node decides whether it should either forward and/or store-and-carry a message. As a result, a number of message replicas may be created and routed independently (``spraying''). Most opportunistic routing schemes to-date perform {\em greedy} replication handing over a copy of a message to the first nodes encountered. Yet, in a network with heterogeneous nodes, where some nodes may be much ``better'' relays than others, such greedy schemes waste a lot of message replicas (and thus energy, storage space, etc.) on ``useless'' relays. For this reason, we propose the idea of \emph{utility-based replication}, where some \emph{fitness} or \emph{utility} function is maintained for all nodes in a distributed fashion, and a small budget of message replicas is allocated according to this utility only to the fittest nodes. We describe a number of variations using different utility functions, and show that an improvement of up to 5-6 times in delay can be achieved over greedy algorithms.

Traditionally, network optimization is used to provide good configurations in real network system problems based on mathematical models and statistical assumptions. Recently, this paradigm is evolving, fueled by an explosion of availability of data. The modern trend in networking problems is to tap into the power of data to extract models and deal with uncertainty. This thesis proposes algorithmic frameworks for wireless networks, based both on classical or data-driven optimization and machine learning. We target two use cases, user association and cloud resource reservation.The baseline approach for user association, connecting wireless devices to the base station that provides the strongest signal, leads to very inefficient configurations even in current wireless networks. We focus on tailoring user association based on resource efficiency and service requirement satisfaction, depending on the underlying network demand. We first study distributed user association with priority QoS guarantees, then scalable centralized load balancing based on computational optimal transport and finally robust user association based on approximate traffic prediction.Moving to the topic of cloud resource reservation, we develop a novel framework for resource reservation in worst-case scenaria, where the demand is engineered by an adversary aiming to harm our performance. We provide policies that have ``no regret'' and guarantee asymptotic feasibility in budget constraints under such workloads. More importantly we expand to a general framework for online convex optimization (OCO) problems with long term budget constraints complementing the results of recent literature in OCO.; Traditionnellement, l'optimisation du réseau est utilisée afin de fournir de bonnes options de configuration basées sur des modèles mathématiques et des hypothèses statistiques. La tendance actuelle en ce qui concerne des problèmes de réseau consiste à exploiter le pouvoir des données afin d’extraire des modèles et gérer les incertitudes. Cette thèse, propose des cadres algorithmiques pour les réseaux sans fil, basés à la fois sur l'optimisation classique ou des données et sur l'apprentissage automatique. Nous ciblons deux cas, l'association d'utilisateurs et la réservation de ressources en nuages. Dans le premier cas, l'approche de base pour l'association d'utilisateurs qui consiste à connecter des périphériques sans fil à la station de base fournissant le signal le plus puissant, conduit à des configurations inefficaces. De prime abord, nous étudions, l’association d’utilisateurs distribués avec des garanties de qualité de service prioritaires, puis nous prenons en considération l’équilibrage de charge centralisé évolutif, basé sur le transport optimal par calcul et enfin nous prenons l’exemple d’une association d’utilisateurs robuste basée sur la prédiction de trafic approximative.Ensuite, nous développons un nouveau cadre pour la réservation de ressources en nuages dans les scénarios les plus défavorables, dans lequel la demande est gérée par un adversaire. Nous proposons des politiques «no regret» et nous garantissons une faisabilité asymptotique des contraintes budgétaires. Nous développons un cadre général, pour des problèmes d’optimisation, convexe en ligne (OCO) avec contraintes budgétaires à long terme complétant les résultats de la littérature récente.

The large diffusion of handheld devices is leading to an exponential growth of the mobile traffic demand which is already overloading the core network. To deal with such a problem, several works suggest to store content (files or videos) in small cells or user equipments. In this thesis, we push the idea of caching at the edge a step further, and we propose to use public or private transportation as mobile small cells and caches. In fact, vehicles are widespread in modern cities, and the majority of them could be readily equipped with network connectivity and storage. The adoption of such a mobile cloud, which does not suffer from energy constraints (compared to user equipments), reduces installation and maintenance costs (compared to small cells). In our work, a user can opportunistically download chunks of a requested content from nearby vehicles, and be redirected to the cellular network after a deadline (imposed by the operator) or when her playout buffer empties. The main goal of the work is to suggest to an operator how to optimally replicate content to minimize the load on the core network. The main contributions are: (i) Modelling. We model the above scenario considering heterogeneous content size, generic mobility and a number of other system parameters. (ii) Optimization. We formulate some optimization problems to calculate allocation policies under different models and constraints. (iii) Performance analysis. We build a MATLAB simulator to validate the theoretical findings through real trace-based simulations. We show that, even with low technology penetration, the proposed caching policies are able to offload more than 50 percent of the mobile traffic demand., La prolifération des appareils portables mène à une croissance du trafic mobile qui provoque une surcharge du cœur du réseau cellulaire. Pour faire face à un tel problème, plusieurs travaux conseillent de stocker les contenus (fichiers et vidéos) dans les small cells. Dans cette thèse, nous proposons d'utiliser les véhicules comme des small cells mobiles et de cacher les contenus à bord, motivés par le fait que la plupart d'entre eux pourra facilement être équipée avec de la connectivité et du stockage. L'adoption d'un tel cloud mobile réduit les coûts d'installation et de maintenance et présente des contraintes énergétiques moins strictes que pour les small cells fixes. Dans notre modèle, un utilisateur demande des morceaux d'un contenu aux véhicules voisins et est redirigé vers le réseau cellulaire après une deadline ou lorsque son playout buffer est vide. L'objectif du travail est de suggérer à un opérateur comment répliquer de manière optimale les contenus afin de minimiser le trafic mobile dans le cœur du réseau. Les principales contributions sont : (i) Modélisation. Nous modélisons le scénario ci-dessus en tenant compte de la taille des contenus, de la mobilité et d'un certain nombre d'autres paramètres. (ii) Optimisation. Nous formulons des problèmes d'optimisation pour calculer les politiques d'allocation sous différents modèles et contraintes. (iii) Analyse des performances. Nous développons un simulateur MATLAB pour valider les résultats théoriques. Nous montrons que les politiques de mise en cache proposées dans cette thèse sont capables de réduire de plus que 50% la charge sur le cœur du réseau cellulaire.