Délestage de données en D2D : de la modélisation à la mise en oeuvre

Mobile data traffic is expected to reach 24.3 exabytes by 2019. Accommodating this growth in a traditional way would require major investments in the radio access network. In this thesis, we turn our attention to an unconventional solution: mobile data offloading through device-to-device (D2D) communications. Our first contribution is DROiD, an offloading strategy that exploits the availability of the cellular infrastructure as a feedback channel. DROiD adapts the injection strategy to the pace of the dissemination, resulting at the same time reactive and relatively simple, allowing to save a relevant amount of data traffic even in the case of tight delivery delay constraints.Then, we shift the focus to the gains that D2D communications could bring if coupled with multicast wireless networks. We demonstrate that by employing a wise balance of multicast and D2D communications we can improve both the spectral efficiency and the load in cellular networks. In order to let the network adapt to current conditions, we devise a learning strategy based on the multi-armed bandit algorithm to identify the best mix of multicast and D2D communications. Finally, we investigate the cost models for operators wanting to reward users who cooperate in D2D offloading. We propose separating the notion of seeders (users that carry content but do not distribute it) and forwarders (users that are tasked to distribute content). With the aid of the analytic framework based on Pontryagin's Maximum Principle, we develop an optimal offloading strategy. Results provide us with an insight on the interactions between seeders, forwarders, and the evolution of data dissemination.Le trafic mobile global atteindra 24,3 exa-octets en 2019. Accueillir cette croissance dans les réseaux d’accès radio devient un véritable casse-tête. Nous porterons donc toute notre attention sur l'une des solutions à ce problème : le délestage (offloading) grâce à des communications de dispositif à dispositif (D2D). Notre première contribution est DROiD, une stratégie qui exploite la disponibilité de l'infrastructure cellulaire comme un canal de retour afin de suivre l'évolution de la diffusion d’un contenu. DROiD s’adapte au rythme de la diffusion, permettant d'économiser une quantité élevée de données cellulaires, même dans le cas de contraintes de réception très serrées. Ensuite, nous mettons l'accent sur les gains que les communications D2D pourraient apporter si elles étaient couplées avec les transmissions multicast. Par l’utilisation équilibrée d'un mix de multicast, et de communications D2D, nous pouvons améliorer, à la fois, l'efficacité spectrale ainsi que la charge du réseau. Afin de permettre l’adaptation aux conditions réelles, nous élaborons une stratégie d'apprentissage basée sur l'algorithme dit ‘’bandit manchot’’ pour identifier la meilleure combinaison de communications multicast et D2D. Enfin, nous mettrons en avant des modèles de coûts pour les opérateurs, désireux de récompenser les utilisateurs qui coopèrent dans le délestage D2D. Nous proposons, pour cela, de séparer la notion de seeders (utilisateurs qui transportent contenu, mais ne le distribuent pas) et de forwarders (utilisateurs qui sont chargés de distribuer le contenu). Avec l'aide d’un outil analytique basée sur le principe maximal de Pontryagin, nous développons une stratégie optimale de délestage

ENCODE: Encoding NetFlows for Network Anomaly Detection

NetFlow data is a popular network log format used by many network analysts and researchers. The advantages of using NetFlow over deep packet inspection are that it is easier to collect and process, and it is less privacy intrusive. Many works have used machine learning to detect network attacks using NetFlow data. The first step for these machine learning pipelines is to pre-process the data before it is given to the machine learning algorithm. Many approaches exist to pre-process NetFlow data; however, these simply apply existing methods to the data, not considering the specific properties of network data. We argue that for data originating from software systems, such as NetFlow or software logs, similarities in frequency and contexts of feature values are more important than similarities in the value itself. In this work, we propose an encoding algorithm that directly takes the frequency and the context of the feature values into account when the data is being processed. Different types of network behaviours can be clustered using this encoding, thus aiding the process of detecting anomalies within the network. We train several machine learning models for anomaly detection using the data that has been encoded with our encoding algorithm. We evaluate the effectiveness of our encoding on a new dataset that we created for network attacks on Kubernetes clusters and two well-known public NetFlow datasets. We empirically demonstrate that the machine learning models benefit from using our encoding for anomaly detection.Comment: 11 pages, 17 figure

StateSec: Stateful Monitoring for DDoS Protection in Software Defined Networks

To be presented at IEEE NetSoft, 3-7 July 2017, Bologna, ItalyInternational audienceSoftware-Defined Networking (SDN) allows for fast reactions to security threats by dynamically enforcing simple forwarding rules as countermeasures. However, in classic SDN all the intelligence resides at the controller, with the switches only capable of performing stateless forwarding as ruled by the controller. It follows that the controller, in addition to network management and control duties, must collect and process any piece of information required to take advanced (stateful) forwarding decisions. This threatens both to overload the controller and to congest the control channel. On the other hand, stateful SDN represents a new concept, developed both to improve reactivity and to offload the controller and the control channel by delegating local treatments to the switches. In this paper, we adopt this stateful paradigm to protect end-hosts from Distributed Denial of Service (DDoS). We propose StateSec, a novel approach based on in-switch processing capabilities to detect and mitigate DDoS attacks. StateSec monitors packets matching configurable traffic features (e.g., IP src/dst, port src/dst) without resorting to the controller. By feeding an entropy-based algorithm with such monitoring features, StateSec detects and mitigates several threats such as (D)DoS and port scans with high accuracy. We implemented StateSec and compared it with a state-of-the-art approach to monitor traffic in SDN. We show that StateSec is more efficient: it achieves very accurate detection levels, limiting at the same time the control plane overhead

Adaptive Mobile Traffic Offloading

ALGOTEL 2014 -- 16èmes Rencontres Francophones sur les Aspects Algorithmiques des TélécommunicationsInternational audienceCellular operators count on the potential of offloading techniques to relieve their overloaded radio access networks. In this paper, we propose, design, and evaluate a re-injection strategy to finely control the opportunistic distribution of popular contents throughout a hybrid mobile network. The idea is to use the infrastructure resources as seldom as possible. Unlike existing techniques that bind re-injection to statically defined objective functions, our proposal adapts to the current network topology. This turns out to be particularly effective in highly dynamic scenario, where clustering prevent contents to diffuse properly. We assess the performance of our strategy by re-running a realistic large-scale (more than 10,000 nodes) vehicular dataset to disseminate contents under different tolerances to delay. The results show significant savings in the infrastructure load between 55% and 63%

Flooding Data in a Cell: Is Cellular Multicast Better than Device-to-Device Communications?

International audienceA natural method to disseminate popular data on cellular networks is to use multicast. Despite having clear advantages over unicast, multicast does not offer any kind of reliability and could result costly in terms of cellular resources in the case at least one of the destinations is at the edge of the cell (i.e., with poor radio conditions). In this paper, we show that, when content dissemination tolerates some delay, providing device-to-device communications over an orthogonal channel increases the efficiency of multicast, concurring also to offload part of the traffic from the infrastructure. Our evaluation simulates an LTE macro-cell with mobile receivers and reveals that the joint utilization of device-to-device communications and multicasting brings significant resource savings while increasing the cellular throughput

DROiD: Adapting to Individual Mobility Pays Off in Mobile Data Offloading

International audienceCellular operators count on the potentials of offloading techniques to relieve their overloaded data channels. Beyond standard access point-based offloading strategies, a promising alternative is to exploit opportunistic direct communication links between mobile devices. Nevertheless, achieving efficient device- to-device offloading is challenging, as communication opportunities are, by nature, dependent on individual mobility patterns. We propose, design, and evaluate DROiD (Derivative Reinjection to Offload Data), an original method to finely control the distribution of popular contents throughout a mobile network. The idea is to use the infrastructure resources as seldom as possible. To this end, DROiD injects copies through the infrastructure only when needed: (i) at the beginning, in order to trigger the dissemination, (ii) if the evolution of the opportunistic dissemination is below some expected pace, and (iii) when the delivery delay is about to expire, in order to guarantee 100% diffusion. Our strategy is particularly effective in highly dynamic scenarios, where sudden creation and dissolution of clusters of mobile nodes prevent contents to diffuse properly.We assess the performance of DROiD by simulating a traffic information service on a realistic large-scale vehicular dataset composed of more than 10,000 nodes. DROiD substantially outperforms other offloading strategies, saving more than 50% of the infrastructure traffic even in the case of tight delivery delay constraints. DROiD allows terminal- to-terminal offloading of data with very short maximum reception delay, in the order of minutes, which is a realistic bound for cellular user acceptance

Full-scale shake table tests of a reinforced concrete structure equipped with a novel active mass damper

This paper presents the results of an experimental program involving shake table testing of two full-scale reinforced concrete frame buildings. These tests were conducted to investigate the effectiveness and reliability of a newly proposed servo-hydraulic Active Mass Damper (AMD) that can be designed to enhance the target seismic performance of a building at multiple earthquake intensity levels. The two nominally identical case-study buildings were intentionally designed to exhibit a “soft story” mechanism at the first level when subject to ground shaking of sufficient intensity, but one was equipped with the newly proposed AMD, installed on the roof. The two specimens were then subject to the same loading protocol consisting of a ground shaking sequence of varying intensity, with the seismic input consisting of a selected natural ground motion. The experimental results demonstrated that the proposed AMD is extremely effective at enhancing building seismic performance. Specifically, the AMD provided peak displacement reductions in the order of 70% and was shown capable of absorbing more than 60% of the total input energy. As a consequence, the un-retrofitted structure suffered nontrivial structural and non-structural damage, while the AMD-retrofitted building remained virtually undamaged at all shaking intensities considered

Effects of the equilibrium atmosphere on Taleggio cheese storage in micro perforated packaging

Taleggio is an Italian smear-ripened cheese, whose complex microbiota demands the optimisation of the pack-aging system to avoid excessive changes during storage. Metabolic processes of the cheese rind microbiota can be usefully exploited in equilibrium modified atmosphere packaging (EMAP) by balancing microbiota respiration and film permeation. Here, we present the application of three different micro perforated EMAPs as models for smear-ripened cheese compared to two control packaging configurations. Analyses of the main microbial groups, headspace gas, textural profile, and sensory properties were performed to find the best packaging for storage. Results showed that two of the alternative micro perforated packaging systems were able to control the excessive changes during storage, thus limiting fungal overgrowth and allowing the typical development of smear microbiota with minor changes to hardness and cohesiveness. Finally, the sensory evaluation positively favoured one of the alternatively packed cheeses based on its compactness, typical dairy traits, and minor off-flavours. These findings showed that EMAP can be a valid alternative solution to control the storage of Taleggio cheese. Further studies could be conducted to evaluate this system on other smear cheeses

Device-to-device data Offloading : from model to implementation

Le trafic mobile global atteindra 24,3 exa-octets en 2019. Accueillir cette croissance dans les réseaux d’accès radio devient un véritable casse-tête. Nous porterons donc toute notre attention sur l'une des solutions à ce problème : le délestage (offloading) grâce à des communications de dispositif à dispositif (D2D). Notre première contribution est DROiD, une stratégie qui exploite la disponibilité de l'infrastructure cellulaire comme un canal de retour afin de suivre l'évolution de la diffusion d’un contenu. DROiD s’adapte au rythme de la diffusion, permettant d'économiser une quantité élevée de données cellulaires, même dans le cas de contraintes de réception très serrées. Ensuite, nous mettons l'accent sur les gains que les communications D2D pourraient apporter si elles étaient couplées avec les transmissions multicast. Par l’utilisation équilibrée d'un mix de multicast, et de communications D2D, nous pouvons améliorer, à la fois, l'efficacité spectrale ainsi que la charge du réseau. Afin de permettre l’adaptation aux conditions réelles, nous élaborons une stratégie d'apprentissage basée sur l'algorithme dit ‘’bandit manchot’’ pour identifier la meilleure combinaison de communications multicast et D2D. Enfin, nous mettrons en avant des modèles de coûts pour les opérateurs, désireux de récompenser les utilisateurs qui coopèrent dans le délestage D2D. Nous proposons, pour cela, de séparer la notion de seeders (utilisateurs qui transportent contenu, mais ne le distribuent pas) et de forwarders (utilisateurs qui sont chargés de distribuer le contenu). Avec l'aide d’un outil analytique basée sur le principe maximal de Pontryagin, nous développons une stratégie optimale de délestage.Mobile data traffic is expected to reach 24.3 exabytes by 2019. Accommodating this growth in a traditional way would require major investments in the radio access network. In this thesis, we turn our attention to an unconventional solution: mobile data offloading through device-to-device (D2D) communications. Our first contribution is DROiD, an offloading strategy that exploits the availability of the cellular infrastructure as a feedback channel. DROiD adapts the injection strategy to the pace of the dissemination, resulting at the same time reactive and relatively simple, allowing to save a relevant amount of data traffic even in the case of tight delivery delay constraints.Then, we shift the focus to the gains that D2D communications could bring if coupled with multicast wireless networks. We demonstrate that by employing a wise balance of multicast and D2D communications we can improve both the spectral efficiency and the load in cellular networks. In order to let the network adapt to current conditions, we devise a learning strategy based on the multi-armed bandit algorithm to identify the best mix of multicast and D2D communications. Finally, we investigate the cost models for operators wanting to reward users who cooperate in D2D offloading. We propose separating the notion of seeders (users that carry content but do not distribute it) and forwarders (users that are tasked to distribute content). With the aid of the analytic framework based on Pontryagin's Maximum Principle, we develop an optimal offloading strategy. Results provide us with an insight on the interactions between seeders, forwarders, and the evolution of data dissemination

The Cost of Being Altruistic: Optimal D2D Offloading under Rewarding Conditions

International audienceExisting device-to-device (D2D) offloading techniques assume that all nodes storing data are potential forwarders. This leads to suboptimal results whenever the system has to reward forwarders. How to design a global strategy that keeps the number of seed users low (to save cellular bandwidth) and selects the appropriate set of forwarders (to know which ones to reward) remains an open issue. We formulate this question as a stochastic control problem that we solve using an application of Pontryagin's Maximum Principle (PMP). We provide a framework that works under a generic cost model. We show analytically that an optimal solution exists and compute when operators benefit from this policy