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International audience; Due to the increasing number of car-centered connected services, making efficient use of limited radio resources is critical in vehicular communications. Hybrid vehicular networks dispose of multiple Radio Access Technologies (RATs) like cellular and vehicle-to-vehicle (V2V) networks, with complementary characteristics that allow for developing smarter network traffic distribution methods. This paper proposes a self-adaptive clustering system for ensuring a suitable trade-off between data aggregation (over the cellular network) and communication congestion due to cluster management (within the V2V network). The systems algorithms use a distributive justice approach for selecting cluster heads, to improve fairness among car drivers and hence help the social acceptability of self-adaptive clustering. Simulation results show that this approach significantly improves fairness over time without affecting network performance. This solution can thus optimize the usage of radio resources, reducing cellular access costs, without the need for uniformization among different mobile operators access plans.

Indoor coverage is a major challenge for 5G millimeter waves (mmWaves). In this paper, we address this problem through a novel theoretical framework that combines stochastic indoor environment modeling with advanced physical propagation simulation. This approach is particularly adapted to investigate indoor-to-indoor 5G mmWave propagation. Its system implementation, so-called iGeoStat, generates parameterized typical environments that account for the indoor spatial variations, then simulates radio propagation based on the physical interaction between electromagnetic waves and material properties. This framework is not dedicated to a particular environment, material, frequency or use case and aims to statistically understand the influence of indoor environment parameters on mmWave propagation properties, especially coverage and path loss. Its implementation raises numerous computational challenges that we solve by formulating an adapted link budget and designing new memory optimization algorithms. The first simulation results for two major 5G applications are validated with measurement data and show the efficiency of iGeoStat to simulate multiple diffusion in realistic environments, within a reasonable amount of time and memory resources. Generated output maps confirm that diffusion has a critical impact on indoor mmWave propagation and that proper physical modeling is of the utmost importance to generate relevant propagation models., Comment: 30 pages, 18 figures, and 7 tables

International audience; Aircraft systems are evolving and being enhanced thanks to new design paradigms leveraging on recent technology advances in embedded systems. However, the Integrated Modular Avionics (IMA) model, used in current avionics, has shown important limitations to accommodate such evolution. These new paradigms demand for much more global system modularity than what IMA is able to offer. Such system evolution has as well an impact on the underlying different networks present on aircrafts. In this context, it is mandatory to investigate the kind and the breadth of adaptation networks need in order to cope with new requirements. To this end, this paper we firstly investigate the current aircrafts network configuration and management procedures. It appears that they lack the features, more specifically, the configuration management features, necessary to support these new use cases. We then look at proposals trying to fulfil this features gap. Each of them, while providing parts of the answers, also come with trade-off or insufficiencies that prevent them from fully answering to the new needs. Then, a new network configuration architecture able to cope with the newly defined configuration management requirements is provided. A comparison to the other approaches is presented, so to highlight how the proposed architecture better fulfils long-term evolution requirements while being less complex and more suitable for current configuration procedures than the other proposal. Finally, a simulation of the configuration architecture is done to provide insights on the new proposed features.

International audience; In recent years, next to Cloud Computing, Network Function Virtualization (NFV) has emerged as one of the most interesting paradigms inside the ICT world, leading to unex-plored scenarios and new features such as Service Chaining. The latter consisting in steering the packets through a sequence of services on their way to the destination. Even more, these services can be located inside different Public Clouds, hence giving us the possibility to exploit for the first time a Multi-Cloud approach. One way to perform Service Chaining is through the new Segment Routing protocol for IPv6. Within this context we have investigated how we could create, inside Amazon Web Services, a Multi-Cloud configuration to provide Service Chaining, leveraging on the Vector Packet Processing (VPP) software router for packet handling and SRv6 processing. We performed extensive measurement campaigns, looking in particular on how VPP and Segment Routing presence could affect the overall performance inside Amazon Web Services and having a first insight on what performance can be achieved when chaining several cloud instances in different geographical regions.

International audience; Software switches are increasingly used in network function virtualization (NFV) to route traffic between virtualized network functions (VNFs) and physical network interface cards (NICs). Understanding of alternative switch designs remains deficient, however, in the absence of a comprehensive, comparative performance analysis. In this paper, we propose a methodology intended to be fair and use it to compare the performance of seven state-of-the-art software switches. We first explore their respective design spaces and then compare their performance under four representative test scenarios. Each scenario corresponds to a specific case of routing NFV traffic between NICs and/or VNFs. Our experimental results show that no single software switch prevails in all scenarios. It is therefore important to choose the one that is best adapted to a given use-case. The presented results and analysis bring a better understanding of design tradeoffs and identify potential bottlenecks that limit the performance of software switches.

Vast volumes of time series are becoming more common in part to recent research advancements in big data analytics and sensor/monitoring networks. However, tranmission of time series for visualization services can cause serious bandwidth wastage and extensive network delays if there is no efficient data management. Existing work such as M4 aim to solve this problem by providing high data reduction rates through data aggregation and guaranteeing the reliability of visualization results at the same time. However, current work on M4 does not consider verification in a more practical environment; for example experimentation on web-based servers that are openly accessible by users. In this paper, we propose inter-pixel gradient-based M4 (IGM4) for enhancing the existing M4 scheme, and conduct a study to reduce the amount of data and delay without distorting the results of visualized graph. We build user-friendly web-based system to dealing with the data processing technique, and perform test on the empirical environment. Finally, we present the results of performance evaluation through comparison among the original data, M4, and IGM4 reflecting the various kinds of time series and resolutions.

International audience; Solar energy is currently a popular renewable resource, yet limited daily. In green cellular networks, multiple constraints optimization (MCO) problems arise naturally. For example, a typical objective is to control the power transmission of hybrid base stations (connected to both solar panels and electrical grid) in order to maximize user's average throughput, under the constraints of consumed grid energy and user's blocking rate. However, such problems have been generally proved to be NP-hard. In this paper, we formulate this generic MCO problem as a quantized Markovian cost-reward model, with no assumption on input data. We then propose a novel algorithm, namely Cost-constrained Viterbi Algorithm, which recursively returns the optimal policy with linear computational complexity for this model. As an application, we provide engineering rules for the design of hybrid base stations through extensive simulations. In comparison with brute force method for a simple scenario, we find that our algorithm does achieve the constrained optimal policy.

In recent years, next to Cloud Computing, Network Function Virtualization (NFV) has emerged as one of the most interesting paradigm inside the ICT world, leading to unexplored scenarios and new features such as Service Chaining. The latter is the capability where packets are steered through a sequence of services on their way to the destination. These services could also be located inside different Public Cloud Regions, hence giving us the possibility to exploit for the first time a Multi-Cloud approach. One way to actually perform Service Chaining is through the new Segment Routing protocol for IPv6. Within this context we have started to investigate how we could create inside Amazon Web Services a Multi-Cloud configuration to provide Service Chaining. As an initial step, we have deployed, inside an Amazon Virtual Private Cloud, a software router compatible with Segment Routing called Vector Packet Processing (VPP). Then, we have automated its deployment with Terraform, a CloudOrchestrator tool. Afterwards, taking advantage of our scripts, we have repeated the same configuration in different Amazons Region, connecting them together through Segment Routing protocol. Finally, we started a first extensive measurements campaign, looking in particular on how VPP and Segment Routing presence could affect the overall performance inside Amazon Web Services., Au cours des dernières années, à coté du Cloud Computing, la virtualisation des fonctions de réseau (NFV) est devenue l'un des paradigmes les plus intèressants du monde des ICT, donnant lieu à des scénarios inexplorés et à de nouvelles fonctionnalités telles que Service Chaining. Ce dernier est la capacité où les paquets sont dirigés à travers une séquence de services sur leur chemin vers la destination. Ces services pourraient également etre situés dans différentes régions de cloud public, nous permettant ainsi d'exploiter pour la première fois une approche multi-cloud. Le nouveau protocole de routage par segment pour IPv6 (Segment Routing-SRv6) est un moyen efficace d'enchainer les services. Dans ce contexte, nous avons commencé à étudier comment créer une configuration multi-cloud dans Amazon Web Services afin de fournir un chainage de services. Dans un premier temps, nous avons déployé, au sein d Amazon Virtual Private Cloud, un routeur logiciel compatible avec le routage par segment appelé Vector Packet Processing (VPP). Ensuite, nous avons automatisé son déploiement avec Terraform, un outil pour d'orchestration. Ensuite, profitant de nos scripts, nous avons répété la meme configuration dans diffèrentes régions d'Amazon, en les connectant ensemble via le SRv6. Enfin, nous avons lancé une première campagne de mesures étendue, en examinant en particulier l'impact de la présence de VPP et SRv6 sur les performances globales d'Amazon Web Services.

Part 2: Poster Papers; International audience; Optical Packet Switching is a prominent technology proposing not only a reduction of the energy consumption by the elimination of numerous optical-electrical-optical conversions in electronic switches, but also a decrease of network latencies due to the cut-through nature of packet transmission. However, it is adversely affected by packet contention, preventing its deployment. Solutions have been proposed to tackle the problem: addition of shared electronic buffers to optical switches (then called hybrid opto-electronic switches), customization of TCP protocols, and use of different service classes of packets with distinct switching criteria.In the context of data center networks we investigate a combination of said solutions and show that the hybrid switch, compared to the optical switch, boosts the performance of the data center network. Furthermore, we show that introducing a “Reliable” service class improves performance for this class not only in the case of the hybrid switch, but also brings the optical switch to performance levels comparable to that of the hybrid switch, all the while keeping other classes’ performance on the same level.

International audience

International audience; Interest in software networking has grown significantly since the introduction of Network Function Virtualization (NFV). Software switches are used in NFV to steer traffic between different virtualized network functions and physical Network Interface Cards (NICs). It is becoming more and more important to objectively evaluate and compare the performance of the multiple alternative implementations that have recently been proposed. A comprehensive performance analysis is still missing for two main reasons: (i) the amount of time required to configure and compare all such tools is enormous; (ii) it is very difficult to define a proper methodology to compare different solutions in a fair manner. In this paper, we propose a methodology based on four simple yet representative test scenarios used to evaluate the performance of software switches. We apply this methodology to measure throughput and latency metrics for 6 state-of-the-art software switches namely, OVS-DPDK, snabb, BESS, FastClick, VPP and netmap VALE. Our work constitutes a first step to building a better understanding of design tradeoffs and identifying performance bottlenecks.

International audience; This paper considers the problem of optimizing the trajectory of an Unmanned Aerial Vehicle (UAV) Base Station (BS). A map is considered, characterized by a traffic intensity of users to be served. The UAV BS must travel from a given initial location at an initial time to a final position within a given duration and serves the traffic on its way. The problem consists in finding the optimal trajectory that minimizes a certain cost depending on the velocity and on the amount of served traffic. The problem is formulated using the framework of Lagrangian mechanics. When the traffic intensity is quadratic (single-phase), we derive closed-form formulas for the optimal trajectory. When the traffic intensity is bi-phase, necessary conditions of optimality are provided and an Alternating Optimization Algorithm is proposed, that returns a trajectory satisfying these conditions. The Algorithm is initialized with a Model Predictive Control (MPC) online algorithm. Numerical results show how the trajectory is improved with respect to the MPC solution.

Research interest on Information Centric Networking (ICN) has been sharply growing. Although new architectures, algorithms, and analytical models have been proposed, their evaluation remains often isolated and not rigorously verified by the research community. This paper initially portrays the composition of open source software tools available for ICN, certifying the predominance of Content Centric Networking (CCN)/Named Data Networking (NDN) simulators. Then, inspired by similar works related to the P2P field, it surveys related research papers to qualify the ICN literature produced so far, finding that a large fraction of contributions either uses custom, proprietary, and unavailable software, or even plainly fails to mention any information in this regard. By adopting a rigorous methodology, in the second part of the paper four simulators, namely ndnSIM, ccnSim, CCNPL-Sim, and Icarus, are cross-compared under several key aspects. Our findings confirm both their accuracy with respect to reference theoretical models in simple settings, and their consistency in more complex scenario. Additionally, our analysis can both assist researchers in the choice of the tool that best fits their needs, and provide guidelines to avoid common pitfalls in the ICN performance evaluation.

International audience; Page load time (PLT) is still the most common application Quality of Service (QoS) metric to estimate the Quality of Experience (QoE) of Web users. Yet, recent literature abounds with interesting proposals for alternative metrics (e.g., Above The Fold, SpeedIndex and variants) that aim at closely capturing how users perceive the Webpage rendering process. However, these novel metrics are typically computationally expensive, as they require to monitor and post-process videos of the rendering process, and have failed to be widely deployed. In this demo, we show our implementation of an open-source Chrome extension that implements a practical and lightweight method to measure the approximated Above-the-Fold (AATF) time, as well as others Web performance metrics. The idea is, instead of accurately monitoring the rendering output, to track the download time of the last visible object on screen (i.e., "above the fold"). Our plugin also has options to save detailed reports for later analysis, a functionality ideally suited for researchers wanting to gather data from Web experiments.

Optical functionalities, such as optical packet switching and wavelength conversion, have had little use in practice despite their potential benefits to data networks by lifting performance bottlenecks and curbing energy consumption. Enabling them requires network-level handling strategies to exploit their full capability in ways that are compatible with network paradigms. We explore such strategies, and quantify some of their benefits at the network level.

International audience

International audience; The Locator/Identifier Separation Protocol (LISP) separates classical IP addresses into two categories: one for identifying terminals, the other for routing. To associate identifiers and locators LISP needs a specific mechanism, called mapping system. This technology is still at an early stage but two experimental platforms have already been deployed in the Internet: LISP Beta Network and LISP-Lab. However, only the LISP Beta Network is monitored with LISPmon that partially monitors the mapping system once a day. To accompany the growth of LISP, a dynamic and complete monitoring system is required. Therefore, we propose LISP-Views, a dynamic versatile large scale LISP monitoring architecture. LISP-Views allows to automatically conduct comprehensive and objective measurements. After running LISP-Views in the wild for several months and comparing the monitoring results with LISPmon, we confirm that LISP-Views provides more detailed and accurate information. We observe the different behaviours between every network entity within mapping system, and also explore the current LISP performance for further improvements.

This paper considers the problem of designing the user trajectory in a device-to-device communications setting. We consider a pair of pedestrians connected through a D2D link. The pedestrians seek to reach their respective destinations while using the D2D link for data exchange applications such as file transfer, video calling, and online gaming. In order to enable better D2D connectivity, the pedestrians are willing to deviate from their respective shortest paths, at the cost of reaching their destinations slightly late. A generic trajectory optimization problem is formulated and solved for the case when full information about the problem in known in advance. Motivated by the D2D user's need to keep their destinations private, we also formulate a regularized variant of the problem that can be used to develop a fully online algorithm. The proposed online algorithm is quite efficient, and is shown to achieve a sublinear \emph{offline} regret while satisfying the required mobility constraints exactly. The theoretical results are backed by detailed numerical tests that establish the efficacy of the proposed algorithms under various settings., Comment: To appear in IEEE INFOCOM 2018, April 2018, Honolulu HI

International audience; Full-duplex (FD) is a principle in which a transceiver can receive and transmit on the same time-frequency radio resource. Assuming perfect self-interference cancellation (self-IC), FD can potentially double the spectral efficiency (SE) of a given point-to-point communication. However in cellular networks, we may be far from this upper bound due to base stations (BSs) and users interference. In particular, even if the overall SE is improved, the uplink (UL) performance is degraded compared to a traditional half-duplex (HD) system. In this paper, we propose and evaluate a new duplex-switching (DS) policy in which BSs can adopt FD- or HD-mode according to the position of their scheduled users. This system is analyzed using stochastic geometry in terms of average SE (ASE) and signal-to-interference-plus-noise ratio (SINR). The proposed scheme allows to trade-off the downlink (DL) for the UL performance when comparing to a FD scenario. In terms of cell performance (UL+DL), our DS policy even outperform both HD and FD systems when the parameters are optimized.

International audience; Originally used to assist network-layer fragmentation and reassembly, the IP identification field (IP-ID) has been used and abused for a range of tasks, from counting hosts behind NAT, to detect router aliases and, lately, to assist detection of censorship in the Internet at large. These inferences have been possible since, in the past, the IP- ID was mostly implemented as a simple packet counter: however, this behavior has been discouraged for security reasons and other policies, such as random values, have been suggested. In this study, we propose a framework to classify the different IP-ID behaviors using active probing from a single host. Despite being only minimally intrusive, our technique is significantly accurate (99% true positive classification) robust against packet losses (up to 20%) and lightweight (few packets suffices to discriminate all IP-ID behaviors). We then apply our technique to an Internet-wide census, where we actively probe one alive target per each routable /24 subnet: we find that that the majority of hosts adopts a constant IP-IDs (39%) or local counter (34%), that the fraction of global counters (18%) significantly diminished, that a non marginal number of hosts have an odd behavior (7%) and that random IP-IDs are still an exception (2%).

The Locator/Identifier Separation Protocol (LISP) has been proposed to tackle the BGP scalability issue encountered in legacy Internet architecture, while introducing additional benefits. The key idea of LISP is to split the conventional IP addressing space into two categories: Identifier and Locator. Identifier is used for identification and Locator is used for routing. It is currently under standardization in IETF and is deployed in the wild at the same time, thanks to two testbeds: the LISP Beta Network and the LISP-Lab project. The interworking mechanism is proposed to ensure the communication between LISP-speaking sites and legacy Internet. The performance of LISP interworking with legacy Internet is of paramount importance to promote the adoption of LISP in future Internet. The existing research efforts to evaluate LISP interworking lack of large scale experimental results. The objective of this paper is to fill this gap by providing a latency evaluation and routing path measurement about LISP interworking mechanism in real networks. The work is based on a 2 weeks measurement campaign by using RIPE Atlas, which is the largest existing Internet measurement infrastructure for both IPv4 and IPv6. Experimental results show that LISP introduces additional latency, especially for close destinations, but negligible for intercontinental long-distance destinations. The additional latency highly depends on the position of the new network element being in charge of the communication between LISP-sites and the legacy Internet. Although LISP introduces some stretch, its performance is generally stable and its latency is not very high compared to natively forwarding without using LISP.

International audience; In the Internet, Autonomous Systems continuously exchange routing information via the BGP protocol: the large number of networks involved and the verbosity of BGP result in a huge stream of updates. Making sense of all those messages remains a challenge today. In this paper, we leverage the notion of "primary path" (i.e., the most used inter-domain path of a BGP router toward a destination prefix for a given time period), reinterpreting updates by grouping them in terms of primary paths unavailability periods, and illustrate how BGP dynamics analysis would benefit from working with primary paths. Our contributions are as follows. First, through measurements, we validate the existence of primary paths: by analyzing BGP updates announced at the LINX RIS route collector spanning a three months period, we show that primary paths are consistently in use during the observation period. Second, we quantify the benefits of primary paths for BGP dynamics analysis on two use cases : Internet tomography and anomaly detection. For the latter, using three months of anomalous BGP events documented by BGPmon as reference, we show that primary paths could be used for detecting such events (hijacks and outages), testifying of the increased semantic they provide.

International audience; We report on possible 50% throughput increase in Optical Packet Switching (OPS) data-center networks by replacing all-optical switches with optical switches with shared electronic buffers further enhanced with TCP Stop-and-Wait algorithms.

We characterize a multi tier network with classical macro cells, and multi radio access technology (RAT) small cells, which are able to operate in microwave and millimeter-wave (mm-wave) bands. The small cells are assumed to be deployed along roads modeled as a Poisson line process. This characterization is more realistic as compared to the classical Poisson point processes typically used in literature. In this context, we derive the association and RAT selection probabilities of the typical user under various system parameters such as the small cell deployment density and mm-wave antenna gain, and with varying street densities. Finally, we calculate the signal to interference plus noise ratio (SINR) coverage probability for the typical user considering a tractable dominant interference based model for mm-wave interference. Our analysis reveals the need of deploying more small cells per street in cities with more streets to maintain coverage, and highlights that mm-wave RAT in small cells can help to improve the SINR performance of the users., Comment: A 7-page version is submitted to IEEE GLOBECOM 2017

This paper considers the problem of receive antenna selection (AS) in a multiple-antenna communication system having a single radio-frequency (RF) chain. The AS decisions are based on noisy channel estimates obtained using known pilot symbols embedded in the data packets. The goal here is to minimize the average packet error rate (PER) by exploiting the known temporal correlation of the channel. As the underlying channels are only partially observed using the pilot symbols, the problem of AS for PER minimization is cast into a partially observable Markov decision process (POMDP) framework. Under mild assumptions, the optimality of a myopic policy is established for the two-state channel case. Moreover, two heuristic AS schemes are proposed based on a weighted combination of the estimated channel states on the different antennas. These schemes utilize the continuous valued received pilot symbols to make the AS decisions, and are shown to offer performance comparable to the POMDP approach, which requires one to quantize the channel and observations to a finite set of states. The performance improvement offered by the POMDP solution and the proposed heuristic solutions relative to existing AS training-based approaches is illustrated using Monte Carlo simulations.

In this paper, we consider a hybrid vehicular network, in which vehicles transmit data via the cellular network and dispose of a Vehicle-to-Vehicle (V2V) interface. In this context, we propose an auto-adaptive multi-hop clustering algorithm, which optimizes the usage of the cellular radio resource under the constraint of a maximum packet loss rate (PLR) in the V2V network. The larger the V2V-based clusters are, the higher the data compression ratio at the cluster head is, and the smaller the amount of required resource on the cellular link becomes. However, PLR becomes higher due to the collisions on the V2V channel when increasing the number of hops for cluster enlargement. The proposed algorithm thus dynamically adapts the maximum number of hops in clusters according to the vehicular traffic density. Through simulations, we show that it performs better in terms of aggregated cellular data and packet loss rate than any fixed-hop clustering algorithm in a dynamic scenario.

International audience; In large low-power networks of battery-driven sen- sors, power outages are a major concern and communication rates have to be carefully designed in order to optimize energy consumption, network connectivity and sensors lifetime. In some IoT use cases, power can be supplied to sensors by way of renewable energy automatic harvesting (solar panels, etc.). Given the high variability of energy arrival processes, energy consump- tion in sensors, in particular caused by sensor transmissions to the sink, has to be then aligned with energy harvesting patterns, in order to maximize throughput while avoiding power outages that may arise when the battery is empty. This paper proposes ODMAC++, an extension to a well-known protocol for sensor transmission scheduling in a WSN. ODMAC++ relies on learning techniques to adapt sensors communication rate to energy harvesting patterns, and uses a beaconing mechanism for which the frequency is adjusted based on past measurements related to the harvested energy process. Simulations based on analytical energy arrival models and on real solar radiation measurements indicate that ODMAC++ is able to avoid power outages and to cope with battery limitation and energy variations due to variability in time.

International audience; Multicast/Broadcast Single Frequency Network (MBSFN) is envisioned to be a key technology for business and mission critical communications. The need arises to define simple and efficient dimensioning rules for such networks. The Signal to Interference plus Noise Ratio (SINR) is an important key performance parameter since other metrics such as outage probability and capacity can be deduced from it. In this work, we propose an analytical model to derive an approximate closed-form formula of the SINR in a MBSFN. Our model takes into account Inter-Symbol Interference due to the different propagation delays between the User Equipment (UE) and its serving evolved Nodes-B (eNBs). The comparison with Monte Carlo simulations shows that our approach provides accurate results when shadowing standard deviation is low. When shadowing is highly variable, our model, while less accurate, outperforms the traditional approach based on Fenton-Wilkinson. This phenomenon is due to the fact that several eNBs serve the same UE so that shadowing on every individual link compensate.

Two concurrent factors challenge the evaluation of large-scale cache networks: complex algorithmic interactions, which are hardly represented by analytical models, and catalog/network size, which limits the scalability of event-driven simulations. To solve these limitations, we propose a new hybrid technique, that we colloquially refer to as ModelGraft, which combines elements of stochastic analysis within a simulative MonteCarlo approach. In ModelGraft, large scenarios are mapped to a downscaled counterpart built upon Time-To-Live (TTL) caches, to achieve CPU and memory scalability. Additionally, a feedback loop ensures convergence to a consistent state, whose performance accurately represents those of the original system. Finally, the technique also retains simulation simplicity and flexibility, as it can be seamlessly applied to numerous forwarding, meta-caching, and replacement algorithms. We implement and make ModelGraft available as an alternative simulation engine of ccnSim. Performance evaluation shows that, with respect to classic event-driven simulation, ModelGraft gains over two orders of magnitude in both CPU time and memory complexity, while limiting accuracy loss below 2%. Ultimately, ModelGraft pushes the boundaries of the performance evaluation well beyond the limits achieved in the current state of the art, enabling the study of Internet-scale scenarios with content catalogs comprising hundreds of billions objects.

The Information-centric network (ICN) is a novel architecture identifying data as a first class citizen, and caching as a prominent low-level feature. Yet, efficiently using large storage (e.g., 1TB) at line rate (e.g., 10 Gbps) is not trivial: in our previous work, we proposed an ICN router design equipped with hierarchical caches, that exploits peculiarities of the ICN traffic arrival process. In this paper, we implement this proposal in the NDN Forwarding Daemon (NFD), and carry on a thorough experimental evaluation of its performance with an emulation methodology on common off the shelf hardware. Our study testifies to the interest and feasibility of the approach.

International audience; Long Term Evolution (LTE), designed by 3rd Generation Partnership Project (3GPP) to increase the capacity of radio mobile communications, has been endorsed by multiple public protection and disaster relief organizations as a next generation technology for Professional Mobile Radio (PMR) networks, which convey business and mission critical communications. One of the main services of PMR is the group communication that can be seen as a Multimedia Broadcast Multicast Service (MBMS). LTE offers functionality to transmit this type of flows either by MBMS over Single Frequency Network (MBSFN), or Single-Cell Point-To-Multipoint (SC-PTM). In this paper, we compare MBSFN, SC-PTM and unicast transmissions in terms of radio quality, system spectral efficiency and cell coverage. Our main conclusion is that SC-PTM together with Transmission Time Interval (TTI) bundling transmissions offers a flexible solution to trade coverage off for capacity.

We use information theoretic achievable rate formulas for the multi-relay channel to study the problem of as-you-go deployment of relay nodes. The achievable rate formulas are for full-duplex radios at the relays and for decode-and-forward relaying. Deployment is done along the straight line joining a source node and a sink node at an unknown distance from the source. The problem is for a deployment agent to walk from the source to the sink, deploying relays as he walks, given that the distance to the sink is exponentially distributed with known mean. As a precursor, we apply the multi-relay channel achievable rate formula to obtain the optimal power allocation to relays placed along a line, at fixed locations. This permits us to obtain the optimal placement of a given number of nodes when the distance between the source and sink is given. Numerical work suggests that, at low attenuation, the relays are mostly clustered near the source in order to be able to cooperate, whereas at high attenuation they are uniformly placed and work as repeaters. We also prove that the effect of path-loss can be entirely mitigated if a large enough number of relays are placed uniformly between the source and the sink. The structure of the optimal power allocation for a given placement of the nodes, then motivates us to formulate the problem of as-you-go placement of relays along a line of exponentially distributed length, and with the exponential path-loss model, so as to minimize a cost function that is additive over hops. The hop cost trades off a capacity limiting term, motivated from the optimal power allocation solution, against the cost of adding a relay node. We formulate the problem as a total cost Markov decision process, establish results for the value function, and provide insights into the placement policy and the performance of the deployed network via numerical exploration., 21 pages. arXiv admin note: substantial text overlap with arXiv:1204.4323

International audience; In recent years, the progress in both hardware and software allows user-space applications to capture packets at 10 Gb/s line rate or more, with cheap COTS hardware. However, processing packets at such rates with software is still far from being trivial. In the literature, this challenge has been extensively studied for network intrusion detection systems, where per-packet operations are easy to parallelize with support of hardware acceleration. Conversely, the scalability of statistical traffic analyzers (STAs) is intrinsically complicated by the need to track per-flow state to collect statistics. This challenge has received less attention so far, and it is the focus of this work. We present and discuss design choices to enable a STA to collects hundreds of per-flow metrics at a multi-10-Gb/s line rate. We leverage a handful of hardware advancements proposed over the last years (e.g., RSS queues, NUMA architecture), and we provide insights on the trade-offs they imply when combined with state-of-the-art packet capture libraries and the multi-process paradigm. We outline the principles to design an optimized STA, and we implement them to engineer DPDKStat, a solution combining the Intel DPDK framework with the traffic analyzer Tstat. Using traces collected from real networks, we demonstrate that DPDKStat achieves 40 Gb/s of aggregated rate with a single COTS PC

International audience; This work focuses on the evaluation of Web quality of experience as perceived by actual users and in particular on the impactof HTTP/1 vs HTTP/2. We adopt an experimental methodology that uses real web pages served through a realistic testbed where we control network, protocol, and application configuration. Users are asked to browse such pages and provide their subjective feedback, which we leverage to obtain the Mean Opinion Score (MOS), while the testbed records objective metrics.The collected dataset comprises over 4,000 grades that we explore to tackle the question whether HTTP/2 improves users experience, to whatextent, and in which conditions. Findings show that users report marginal differences, with 22\%, 52\%, 26\% of HTTP/2 MOS being better, identical, or worse than HTTP/1, respectively. Even in scenarios that favor HTTP/2, results are not as sharp as expected. This is in contrast with objective metrics, which instead record a positive impact with HTTP/2 usage. This shows the complexity of understanding the web experience and the need to involve actual users in the quality assessment process.

Streaming of video content over the Internet is experiencing an unprecedented growth. While video permeates every application, it also puts tremendous pressure in the network—to support users having heterogeneous accesses and expecting a high quality of experience, in a furthermore cost-effective manner. In this context, future internet paradigms, such as information centric networking (ICN), are particularly well suited to not only enhance video delivery at the client (as in the dynamic adaptive streaming over HTTP (DASH) approach), but to also naturally and seamlessly extend video support deeper in the network functions. In this paper, we contrast ICN and transmission control protocol/internet protocol (TCP/IP) with an experimental approach, where we employ several state-of-the-art DASH controllers (PANDA, AdapTech, and BOLA) on an ICN versus TCP/IP network stack. Our campaign, based on tools that we developed and made available as open-source software, includes multiple clients (homogeneous vesrus heterogeneous mixture and synchronous vesrus asynchronous arrivals), videos (up to 4k resolution), channels (e.g., DASH profiles, emulated WiFi and LTE, and real 3G/4G traces), and levels of integration with an ICN network (i.e., vanilla named data networking (NDN), wireless loss detection and recovery at the access point, and load balancing). Our results clearly illustrate, as well as quantitatively assess, the benefits of ICN-based streaming, warning about potential pitfalls that are however easy to avoid.

International audience; This paper investigates the subcarrier and power allocation for the downlink of a multicarrier non-orthogonal multiple access (MC-NOMA) system. A three-step algorithm is proposed to deal with the sum rate maximization problem. First, we assume each user could use all the subcarriers si- multaneously and apply the synchronous iterative waterfilling algorithm (SIWA) to obtain a power vector for each user. Second, subcarriers are assigned to users by a heuristic greedy method based on the achieved power allocation result of Step 1. Third, SIWA is used once again to further improve the system performance with the obtained subcarrier assignment result of Step 2. In Step 3, where the number of multiplexed users is no more than two, the convergence of SIWA is proved analytically. Numerical results show that the proposed resource allocation algorithm achieves comparable performance to an existing near- optimal solution but with much lower time complexity.

An online truthful budgeted matching problem is considered for a bipartite graph, where the right vertices are available ahead of time, and individual left vertices arrive sequentially. On arrival of a left vertex, its edge utilities (or weights) to all the right vertices and a corresponding cost (or bid) are revealed. If a left vertex is matched to any of the right vertices, then it has to be paid at least as much as its cost. The problem is to match each left vertex instantaneously and irrevocably to any one of the right vertices, if at all, to find the maximum weight matching that is truthful, under a payment budget constraint. Truthfulness condition requires that no left vertex has any incentive of misreporting its cost. Assuming that the vertices arrive in an uniformly random order (secretary model) with arbitrary utilities, a truthful algorithm is proposed that is $24\beta$-competitive (where $\beta$ is the ratio of the maximum and the minimum utility) and satisfies the payment budget constraint. Direct applications of this problem include crowdsourcing auctions, and matching wireless users to cooperative relays in device-to-device enabled cellular network., Comment: To appear in NetEcon 2016 and Performance Evaluation Review

International audience

Ground-breaking innovations in transport, such as autonomous vehicles, the European Local Dynamic Map (LDM) and related on-line services heavily depend on reliable vehicular connectivity. In the most likely scenario, hybrid vehicular networks will use the IEEE 802.11p protocol for vehicle-to-vehicle (V2V) communication, and the cellular network (e.g. LTE or 5G) as a gateway to remote servers. Both technologies have their own flaws as IEEE 802.11p available bandwidth drops quickly with the accretion of vehicles in the vicinity, and intensive cellular usage can be costly. In this context we intend to minimize this cost while ensuring system reliability. Clustering vehicles can significantly reduce cellular network usage when the Cluster Head (CH) is the only node that communicates with the cellular Base Station (BS) and also performs data aggregation. Other nodes communicate with the CH using multi-hop forwarding over IEEE 802.11p. There is however a tradeoff in cluster design. On the positive side, large clusters lead to high aggregation levels and thus low usage of the uplink cellular resources. On the negative side, large clusters also increase packet losses in the IEEE 802.11p network affecting communication reliability. In this paper, we study the impact of the number of communication hops on the average size of formed clusters, on data compression, and on IEEE 802.11p packet losses in various vehicle densities. We present a new clustering algorithm which delegates the CH election to the cellular BS, significantly improving the cluster formation compared to CH self-election algorithms. We propose a dynamic clustering approach that adapts the cluster size to the vehicle density and optimize data compression under the constraint of

Information Centric Networking (ICN) paradigms nicely fit the world of wireless sensors, whose devices have tight constraints. In this poster, we compare two alternative designs for secure association of new IoT devices in existing ICN deployments, which are based on asymmetric and symmetric cryptography respectively. While the security properties of both approaches are equivalent, an interesting trade-off arises between properties of the protocol vs properties of its implementation in current IoT boards. Indeed, while the asymmetric-keys based approach incurs a lower traffic overhead (of about 30%), we find that its implementation is significantly more energy- and time-consuming due to the cost of cryptographic operations (it requires up to 41x more energy and 8x more time).

The analysis of Internet-scale Information-centric networks, and of cache networks in general, poses scalability issues like CPU and memory requirements, which can not be easily targeted by neither state-of-the-art analytical models nor well designed event-driven simulators. This demo focuses on showcasing performance of our new hybrid methodology, named ModelGraft, which we release as a simulation engine of the open-source ccnSim simulator: being able to seamlessly use a classic event-driven or the novel hybrid engine dramatically improves the flexibility and scalability of current simulative and analytical tools. In particular, ModelGraft combines elements and intuitions of stochastic analysis into a MonteCarlo simulative approach, offering a reduction of over two orders of magnitude in both CPU time and memory occupancy, with respect to the purely event-driven version of ccnSim, notably one of the most scalable simulators for Information-centric networks. This demo consists in gamifying the aforementioned comparison: we represent ModelGraft vs event-driven simulation as two athletes running a 100-meter competition using sprite-based animations. Differences between the two approaches in terms of CPU time, memory occupancy, and results accuracy, are highlighted in the score-board.

Large scale deployments of general cache networks, such as Content Delivery Networks or Information Centric Networking architectures, arise new challenges regarding their performance evaluation for network planning. On the one hand, analytical models can hardly represent in details all the interactions of complex replacement, replication, and routing policies on arbitrary topologies. On the other hand, the sheer size of networks and content catalogs makes event-driven simulation techniques inherently non-scalable. We propose a new technique for the performance evaluation of large-scale caching systems that intelligently integrates elements of stochastic analysis within a MonteCarlo simulative approach, that we colloquially refer to as ModelGraft. Our approach (i) leverages the intuition that complex scenarios can be mapped to a simpler equivalent scenario that builds upon Time-To-Live (TTL) caches, it (ii) significantly downscales the scenario to lower computation and memory complexity, while, at the same time, preserving its properties to limit accuracy loss, finally, it (iii) is simple to use and robust, as it autonomously converges to a consistent state through a feedback-loop control system, regardless of the initial state. Performance evaluation shows that, with respect to classic event-driven simulation, ModelGraft gains over two orders of magnitude in both CPU time and memory complexity, while limiting accuracy loss below 2%. In addition, we show that ModelGraft extends performance evaluation well beyond the boundaries of classic approaches, by enabling study of Internet-scale scenarios with content catalogs comprising hundreds of billions objects.

While the potential advantages of geographic forwarding in wireless sensor networks (WSN) have been demonstrated for a while now, research in applying Information Centric Networking (ICN) has only gained momentum in the last few years. In this paper, we bridge these two worlds by proposing an ICN-compliant and secure implementation of geographic forwarding for ICN. We implement as a proof of concept the Greedy Perimeter Stateless Routing (GPSR) algorithm and compare its performance to that of vanilla ICN forwarding. We also evaluate the cost of security in 802.15.4 networks in terms of energy, memory and CPU footprint. We show that in sparse but large networks, GPSR outperforms vanilla ICN forwarding in both memory footprint and CPU consumption. However, GPSR is more energy intensive because of the cost of communications.

In this work a novel family of decentralised caching policies for wireless networks is introduced, referred to as spatial multi-LRU. These improve cache-hit probability by exploiting multi-coverage. Two variations are proposed, the multi-LRU-One and -All, which differ in the number of replicas inserted in the covering edge-caches. The evaluation is done under spatial traffic that exhibits temporal locality, with varying content catalogue and dependent demands. The performance metric is hit probability and the policies are compared to (1) the single-LRU and (2) an upper bound for all centralised policies with periodic popularity updates. Numerical results show the multi-LRU policies outperform both comparison policies. The reason is their passive adaptability to popularity changes. Between the -One and -All variation, which one is preferable strongly depends on the available storage space and on traffic characteristics. The performance also depends on the popularity shape., Comment: 6 pages, double column, invited paper in 5G Workshop, part of ISTC'16, Brest, France

The \emph{Locator/Identifier Separation Protocol} (LISP) introduces several benefits to the Internet architecture, yet, since it is just in the initial deployment stage, comprehensive understanding of its integration performance with legacy Internet becomes essential. We leverage on RIPE Atlas, the largest Internet measurement infrastructure, to conduct large scale measurements analysis to provide the feedback to improve LISP technology. The preliminary evaluations show that LISP generally has a reliable performance, compared with the existing Internet. From our vantage point, we observe that LISP introduces a non-negligible latency for the European and North American destinations, occasionally some extremely large delay, however, it shows a faster connection for the Asian intercontinental transmission.

Use of IP-layer anycast has increased in the last few years beyond the DNS realm. Yet, existing measurement techniques to identify and enumerate anycast replicas exploit specifics of the DNS protocol, which limits their applicability to this particular service. With this paper, we not only propose and thoroughly validate a protocol-agnostic technique for anycast replicas discovery and geolocation, but also provide the community with open source software and datasets to replicate our experimental results, as well as facilitating the development of new techniques such as ours. In particular, our proposed method achieves thorough enumer-ation and city-level geolocalization of anycast instances from a set of known vantage points. The algorithm features an iterative workflow, pipelining enumeration (an optimization problem using latency as input) and geolocalization (a classification problem using side channel information such as city population) of anycast replicas. Results of a thorough validation campaign show our algorithm to be robust to measurement noise, and very lightweight as it requires only a handful of latency measurements.