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Abies nebrodensis (Lojac.) Mattei is an endemic species of the north-west of Sicily located in an 84 ha area in the Madonie Regional park. The current population is limited to 30 relic adult trees and a fluctuating number of juveniles of natural regeneration. The species is defined as “Critically Endangered” in the Italian list of threatened plants and is classified as CR-D in the 2000 IUCN Red List of Threatened Species. This article reports the key action undertaken by the LIFE4FIR project aimed at preserving A. nebrodensis, and the results obtained so far in three years of activity. OpenArrays SNPs genotyping revealed a high rate of inbreeding in the natural population and that the adult trees are genetically related. Controlled cross-pollination was consequently performed to increase the genetic variability of the progeny. Outbred offspring are currently being grown in the nursery. Reforestation has been planned by using 4000 selected outbred seedlings in 10 areas within Madonie Park to create re-diffusion cores. Support and protection of the relic population have been implemented through regular phytosanitary surveys, as well as new fencing and video surveillance systems against grazing and wild herbivores. A seedbank and cryobank for the long-term germplasm conservation have been established.

International audience; As the internet and connected objects gain more and more in popularity, serving the ever-increasing data traffic becomes a challenge for the mobile operators. The traditional cellular radio access network (RAN), where each base station is co-located with its own processing unit and is responsible for a specific geographic area, has evolved first with the so-called Cloud RAN (C-RAN), and is currently undergoing further architectural evolution under the virtualized RAN (vRAN), Open RAN (O-RAN) and Software-Defined RAN (SD-RAN) architectures. In all these versions, the data processing units can be dynamically centralized into a pool and shared between several base stations, enlarging the geographical view for scheduling and resource allocation algorithms. For instance, resource utilisation is improved by avoiding resource idling during off-peak hours. C-RAN and vRAN gains depend strongly on the clustering scheme of radio units (RRHs and RUs). In this paper, we propose a novel radio clustering algorithm that takes into account both the traffic demand and the position of stations, by using the hyperbolic distance in 3dimensions. We introduce a modified K-means clustering algorithm, called Hyperbolic K-means, and show that this generates geographically compact RU clusters with traffic charge equally shared among them. Application of our algorithm on real-world mobile data traffic, collected from the cities of Nantes and Lille in France, shows an increase in resource utilisation by 25%, and a reduction in deployment cost by 15%, compared to the standard RAN. Furthermore, the performance of our Hyperbolic K-means algorithm is compared extensively against alternative C-RAN clustering proposals from the literature and is shown to outperform them, in resource utilisation as well as in cost reduction.

International audience; In this work, we provide a Machine Learning framework for augmenting the Differentiated Services (DiffServ) protocol with fine-grained dynamic traffic classification. The framework is called L-DiffServ. It is composed of two classification algorithms able to detect the QoS classes of incoming packets only looking at three packet header fields; the first algorithm, referred to as Inter-L-DiffServ, is a semi-supervised classification procedure able to replicate DiffServ classification; the second one, referred to as Intra-L-DiffServ, is an unsupervised algorithm for intra-class classification, useful for classes taking large portions of the overall traffic. We apply the latter to the low priority best-effort class. The performance evaluation shows that our solution is able to dynamically classify packets and to detect new QoS sub-classes hence adapting to traffic aggregate characteristics. We also show that network resource management can be improved exploiting the new generated QoS subclasses: two active queue management algorithms based on WRED and CHOKe show a reduction of the number of sessions affected by packet losses up to 40% with respect to the legacy DiffServ procedure.

International audience; We model the network slice provisioning as an optimization problem including novel mapping and provisioning requirements rising with new radio and core function placement policies. We propose an open-access framework based on an MILP formulation that encompasses flexible functional splitting, with possibly different splitting for different slices and slice subnets, while taking into account different network sharing policies from 5G specifications. We also consider novel mapping and continuity constraints specific to the 5G architectures and beyond. We show by numerical simulations the impact of taking into full and partial consideration these peculiar novel technical constraints.

With an increasing demand for flexible management in software-defined networks (SDNs), it becomes critical to minimize the network policy update time. Although major SDN controllers are now optimized for rapid network update at the control plane, there is still room for data plane optimization in terms of update time, when using TCAM-based physical SDN commodity-off-the-shelf switches. A slow update directly affects network performance and creates bottlenecks. To minimize the flow entry update time, a dependency graph, a kind of directed acyclic graph (DAG), can be used for the access management of flow entries at the switch. Thanks to the DAG, unnecessary entry movements, which are the main factor slowing down flow entry updates, can be avoided. However, existing algorithms show limitations when updates become very frequent. We propose a new flow entry update algorithm, called FastRule, that exploits a greedy strategy with an efficient data structure to accelerate flow entry update with a DAG approach. Moreover, we also adjust our algorithm for other flow table layouts to make it scalable. We elaborate on the correctness of FastRule and test our algorithm using a hardware switch. Compared with existing algorithms, the evaluation shows that our algorithm is about $100 \times $ faster than state-of-the-art solutions with a flow table of $1k$ size.

International audience; Edge computing penetration in mobile access networks is the next barrier to break in communication networks. The virtualization of radio access functions currently under study is expected to trigger the deployment of edge cloud facilities in telecom operator points-of-presence and central offices, to serve the virtualization of both application servers and network functions. The problem of clustering network access points for their assignment to edge cloud facilities has been addressed in the literature. Nonetheless, the inclusion of key-performance indicators such as robustness against traffic variations in the optimization process can increase its complexity excessively while hindering the achievable performance. Leveraging on previous work in this area, in this paper we explore how to reduce time and spatial complexity while introducing additional a robust access point assignment target by using a spatial clustering pre-processing in the optimization problem, grouping together access points based on their spatio-temporal traffic profile. By extensive simulation against real traffic traces and network maps, we show under which conditions we can outperform existing methods at the state of the art. The obtained results show that our approach helps reducing time and space complexity for small to medium instances, indicating the geographical scale at which these operations could be run in a near-real-time manner.

This paper synthetically presents the SYRROCA (SYstem Radiography and ROot Cause Analysis) network automation framework at the state of the art, and details its experimental platform sufficiently enough to understand its technical demonstration. The framework aims to learn nominal operating conditions of a softwarized network service and characterize anomalies in real-time, while offering a compact system state representation called radiography. This representation can provide to operational teams with a real-time insight on anomalies at physical and virtualized layers. The related technical demonstration showcases how SYRROCA can detect real-time anomalies of different nature on a containerized vIMS (virtual IP Multimedia Subsystem) service managed by Kubernetes.

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLF-PHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions., Comment: This paper appears in the Proceedings of IEEE Global Communications Conference (GLOBECOM) 2020. A full version appears in IEEE Transactions on Wireless Communications. arXiv:2004.12539

International audience; In this work, we model the network slice provi-sioning as an optimization problem including novel mapping and provisioning requirements rising with new 5G radio and core function placement policies. We propose an MILP-based formulation that joins different functional splitting strategies with different network function sharing policies and novel mapping continuity constraints from 5G specifications. We show by numerical simulations the impact of taking into full and partial consideration these peculiar sets of novel technical constraints.

In this paper we address the distributed cross-chain swap problem in the blockchain context where multiple agents exchange assets across multiple blockchain systems (e.g. trading Bitcoins for Litecoins or Ethers). We present a mathematical framework allowing to characterize blockchain swap protocols as the combination of a publishing and a commitment phase, where contracts are respectively published and then committed. We characterize the equilibria of existing cross-chain swap protocols (i.e., blockchain swap protocols exchanging assets among different blockchains). More precisely, we prove that following a swap protocol characterized by concurrent publishing of exchange contracts and snap (immediate) assets transfers is a Nash equilibrium. Furthermore, we prove that for protocols with a sequential publishing and commitment of the assets transfers, following the prescribed protocol is a sub-game perfect equilibrium.

International audience; Legacy and novel network services are expected to be migrated and designed to be deployed in fully virtualized environments. Starting with 5G, NFV becomes a formally required brick in the specifications, for services integrated within the infrastructure provider networks. This evolution leads to deployment of virtual resources Virtual-Machine (VM)-based, container-based and/or server-less platforms, all calling for a deep virtualization of infrastructure components. Such a network softwarization also unleashes further logical network virtualization, easing multi-layered, multi-actor and multi-access services, so as to be able to fulfill high availability, security, privacy and resilience requirements. However, the derived increased components heterogeneity makes the detection and the characterization of anomalies difficult, hence the relationship between anomaly detection and corresponding reconfiguration of the NFV stack to mitigate anomalies. In this article we propose an unsupervised machine-learning data-driven approach based on Long-Short-Term-Memory (LSTM) autoencoders to detect and characterize anomalies in virtualized networking services. With a radiography visualization, this approach can spot and describe deviations from nominal parameter values of any virtualized network service by means of a lightweight and iterative mean-squared reconstruction error analysis of LSTM-based autoencoders. We implement and validate the proposed methodology through experimental tests on a vIMS proof-of-concept deployed using Kubernetes.

International audience; The Third Generation Partnership Project (3GPP) was established to bring together several standards organization to address the needs related to the specification of mobile broadband. Originally targeting the evolution from 2G to 3G, 3GPP continues at the forefront of the standardization effort leading to 5G and beyond. Given the growing diversity of services and the explosion of mobile users year by year, evolving architectures have then been successively proposed by the group in order to expand the limits of legacies systems. We overview this evolution, summarizing the transformation of core systems and their related decomposition, aggregation, and mapping operations from 3G to 5G systems. We also present an in-depth description of 5G Core based on recent technical documents delivered by 3GPP. We end with the identification of potential operational and technical challenges in 5G systems.

International audience; Efficient bot detection is a crucial security matter and widely explored in the past years. Recent approaches supplant flow-based detection techniques and exploit graph-based features, incurring however in scalability issues, with high time and space complexity. Bots exhibit specific communication patterns: they use particular protocols, contact specific domains, hence can be identified by analyzing their communication with the outside. A way we follow to simplify the communication graph and avoid scalability issues is looking at frequency distributions of protocol attributes capturing the specificity of botnets behaviour. We propose a bot detection technique named BotFP, for BotFin-gerPrinting, which acts by (i) characterizing hosts behaviour with attribute frequency distribution signatures, (ii) learning benign hosts and bots behaviours through either clustering or supervised Machine Learning (ML), and (iii) classifying new hosts either as bots or benign ones, using distances to labelled clusters or relying on a ML algorithm. We validate BotFP on the CTU-13 dataset, which contains 13 scenarios of bot infections, connecting to a Command-and-Control (C&C) channel and launching malicious actions such as port scanning or Denial-of-Service (DDoS) attacks. Compared to state-of-the-art techniques, we show that BotFP is more lightweight, can handle large amounts of data, and shows better accuracy.

International audience; Blockchain is a technology making the shared registry concept from distributed systems a reality for a number of application domains, from the cryptocurrency one to potentially any industrial system requiring decentralized, robust, trusted and automated decision making in a multi-stakeholder situation. Nevertheless, the actual advantages in using blockchain instead of any other traditional solution (such as centralized databases) are not completely understood to date, or at least there is a strong need for a vademecum guiding designers toward the right decision about when to adopt blockchain or not, which kind of blockchain better meets use-case requirements, and how to deploy it. In this article we aim at providing the community with such a vademecum, while giving a general presentation of blockchain that goes beyond its usage in Bitcoin, surveying a selection of the vast literature that emerged in the last few years. We draw the key requirements and their evolution when passing from permissionless to permissioned blockchains, presenting the differences between proposed and experimented consensus mechanisms, and describing existing blockchain platforms.

International audience; Quality of Service (QoS) management in IP networks today relies on static configuration of classes of service definitions and related forwarding priorities. Packets are actually classified according to the DiffServ architecture based on the RFC 4594, typically thanks to static configuration or filters matching packet features, at network access equipment. In this paper, we propose a dynamic classification procedure, referred to as Learning-powered DiffServ (L-DiffServ), able to detect the distinctive characteristics of traffic and to dynamically assign service classes to IP packets. The idea is to apply semi-unsupervised Machine Learning techniques, such as Linear Discriminant Analysis (LDA) and K-Means, with a proper customization to take into account the issues related to packet-level analysis, i.e. unbalanced distribution of traffic among classes and selection of proper IP header related features. The performance evaluation highlights that L-DiffServ is able to change dynamically the classification outcome, providing an higher number of classes than DiffServ. This last result represents the first step toward a more granular differentiation of IP traffic.

International audience; Efficient bot detection is a crucial security matter and has been widely explored in the past years. Recent approaches supplant flow-based detection techniques and exploit graph-based features, incurring however in scalability issues in terms of time and space complexity. Bots exhibit specific communication patterns: they use particular protocols, contact specific domains, hence can be identified by analyzing their communication with the outside. To simplify the communication graph, we look at frequency distributions of protocol attributes capturing the specificity of botnets behaviour. In this paper, we propose a bot detection technique named BotFP, for BotFinger-Printing, which acts by (i) characterizing hosts behaviour with attribute frequency distribution signatures, (ii) learning behaviour of benign hosts and bots through a clustering technique, and (iii) classifying new hosts based on distances to labelled clusters. We validate our solution on the CTU-13 dataset, which contains 13 scenarios of bot infections, connecting to a Command-and-Control (C&C) channel and launching malicious actions such as port scanning or Denial-of-Service (DDoS) attacks. Our approach applies to various bot activities and network topologies. The approach is lightweight, can handle large amounts of data, and shows better accuracy than state-of-the-art techniques.

International audience; The 5G infrastructure brings a key novelty in net-worked systems design that is a new resource provisioning entity, the so-called "network slice". A network slice is meant to serve end-to-end services as a composition of different network and system resources as the radio, the link and a variety of computing resources (CPU, RAM, storage), generally each managed by a distinct decision-maker (platform, provider, orchestrator or controller). Naturally, centralized slice orchestration approaches have been proposed, where a multi-domain orchestrator allocates the resources, using a multi-resource allocation rule. Nonetheless, while simplifying the algorithmic approach, centralization can come at the expense of scalability and performance. In this paper, we propose new ways to decentralize the slice resource allocation problem, using cascade or parallel resource allocations. We provide an exhaustive analysis of the advantages and disadvantages of the different approaches together with a numerical analysis in a realistic environment.

The fundamental tasks of the control plane in software defined networking (SDN) are to customize forwarding policies for the data plane and to provide global network view for applications. The logically centralized control plane design brings benefits in terms of network programmability and can largely ease network management. However, it also increases efficiency concerns. One practical control plane challenge is path computation, because it can require a significant amount of computation load if the network scale is large and the path requests from applications are frequent. In this paper, our goal is to build a high-performance control plane for path computation using multiple controllers. Previous works attempt to improve control plane efficiency by balancing only the load for data plane behavior between multiple controllers. Going beyond conventional wisdom, we designed ParaCon, a solution we propose to speed up the control plane by distributing the load of path computation. We also address the consistency and synchronization overhead challenges related to ParaCon design. To the best of our knowledge, ParaCon is the first attempt that utilizes node parallelism in SDN path computation. We evaluated ParaCon using both Mininet and real-world clusters. Our results show that the path computing time of ParaCon can achieve a speedup of $10{\times }$ over Floyd (used in POX) and Dijkstra (used in ONOS) baseline implementations for networks with hundreds of nodes.

International audience; The Multipath Transport Control Protocol (MPTCP) is undergoing a rapid deployment after a recent and quick standardization. MPTCP allows a network node to use multiple network interfaces and IP paths concurrently, which can lead to several advantages for the user in terms of performance and reliability. In this paper, we describe an MPTCP implementation in the Network Simulator 3 (ns3), comparing it with both the Linux implementation and previous ns3 implementations. We show that it is compatible with the Linux implementation and that it has a desirable similar behavior in traffic handling. Our goal is to allow researchers develop and evaluate new features of MPTCP using our simulator in a much faster way than they would with a kernel implementation, hence boosting MPTCP research.

International audience; Among the novelties introduced by 5G networks, the formalization of the 'network slice' as a resource allocation unit is an important one. In legacy networks, resources such as link bandwidth, spectrum, computing capacity are allocated independently of each other. In 5G environments, a network slice is meant to directly serve end-to-end services, or verticals: behind a network slice demand, a tenant expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. The resource allocation decision encompasses, therefore, a combination of different resources. In this paper, we address the problem of fairly sharing multiple resources between slices, in the critical situation in which the network does not have enough resources to fully satisfy slice demands. We model the problem as a multi-resource allocation problem, proposing a versatile optimization framework based on the Ordered Weighted Average (OWA) operator. We show how, adapting the OWA utility function, our framework can generalize classical single-resource allocation methods, existing multi-resource allocation solutions at the state of the art, and implement novel multi-resource allocation solutions. We compare analytically and by extensive simulations the different methods in terms of fairness and system efficiency. We conclude the paper adapting the proposed framework to Service Level Agreement (SLA)-driven services. Two algorithms, considering minimum capacity requirements and time-fairness are proposed and tested.

International audience; In resource allocation systems, inaccurate information sharing situations are such that users can be aware, up to a small error, about the other users' demands and the available global resource (which can be insufficient to meet the overall demand). Consequently, given an allocation rule, users can predict an allocation that will not necessarily coincide with the actual one. In this work, we provide an estimation of the error for a number of allocation rules and compare their robustness in inaccurate information sharing settings.

International audience; Network slicing in 5G aims to provide an end-to-end partition of the physical network that is optimized for the service it has to supply. Each slice needs to fulfill a Service Level Agreement (SLA), that is a contract between the slice provider and the tenants on the quality of service and reliability, expressed for a diverse set of physical resources (spectrum, link capacity, computing power, etc). For the multi-resource allocation problem in network slicing, we provide two scheduling algorithms that take into account SLA requirements in terms of minimum and nominal resource quantity demands. We show that the algorithm that considers the availability rate of the service, in addition to providing the minimum capacity, has better performances in terms of time-fairness. For both scheduling algorithms we consider a user delaying policy able to take into account SLA priority and latency requirements.

International audience; Virtual Network Functions as a Service (VNFaaS) is a promising business whose technical directions consist of providing network functions as a Service instead of delivering standalone network appliances, leveraging a virtualized environment named NFV Infrastructure (NFVI) to provide higher scalability and reduce maintenance costs. Operating the NFVI under stringent availability guarantees is fundamental to ensure the proper functioning of the VNFaaS against software attacks and failures, as well as common physical device failures. Indeed the availability of a VNFaaS relies on the failure rate of its single components, namely the physical servers, the hypervisor, the VNF software, and the communication network. In this paper, we propose a versatile orchestration model able to integrate an elastic VNF protection strategy with the goal to maximize the availability of an NFVI system serving multiple VNF demands. The elasticity derives from (i) the ability to use VNF protection only if needed, or (ii) to pass from dedicated protection scheme to shared VNF protection scheme when needed for a subset of the VNFs, (iii) to integrate traffic split and load-balancing as well as mastership role election in the orchestration decision, (iv) to adjust the placement of VNF masters and slaves based on the availability of the different system and network components involved. We propose a VNF orchestration algorithm based on Variable Neighboring Search, able to integrate both protection schemes in a scalable way and capable to scale, while outperforming standard online policies.

Major interest is currently given to the integration of clusters of virtualization servers, also referred to as ‘cloudlets’, into the access network to allow higher performance and reliability in the access to mobile edge computing services. We tackle the facility location problem arising in the planning of these networks. Due to the complexity of the network topology, and the number of operational constraints, methods from the literature are hard to adapt. While in [A. Ceselli, M. Premoli, S. Secci, “Cloudlet Network Design Optimization”, in Proc. of IFIP Networking 2015, 20–25 May, 2015, Toulouse, France] we discussed the application issues, considering a real test case, in this paper we focus on the algorithmic ones, providing matheuristics solution algorithms for the static case, and an experimental insight on their computational behavior.

International audience; Last years have witnessed more and more DDoS attacks towards high-profile websites, as the Mirai botnet attack on September 2016, or more recently the memcached attack on March 2018, this time with no botnet required. These two outbreaks were not detected nor mitigated during their spreading, but only at the time they happened. Such attacks are generally preceded by several stages, including infection of hosts or device fingerprinting; being able to capture this activity would allow their early detection. In this paper, we propose a technique for the early detection of emerging botnets and newly exploited vulnerabilities, which consists in (i) splitting the detection process over different network segments and retaining only distributed anomalies, (ii) monitoring at the port-level, with a simple yet efficient change-detection algorithm based on a modified Z-score measure. We argue how our technique, named Split-and-Merge, can ensure the detection of large-scale zero-day attacks and drastically reduce false positives. We apply the method on two datasets: the MAWI dataset, which provides daily traffic traces of a transpacific backbone link, and the UCSD Network Telescope dataset which contains unsolicited traffic mainly coming from botnet scans. The assumption of a normal distribution-for which the Z-score computation makes sense-is verified through empirical measures. We also show how the solution generates very few alerts; an extensive evaluation on the last three years allows identifying major attacks (including Mirai and memcached) that current Intrusion Detection Systems (IDSs) have not seen. Finally, we classify detected known and unknown anomalies to give additional insights about them.

Given an existing Mobile Edge Cloud (MEC) network including virtualization facilities of limited capacity, and a set of mobile Access Points (AP) whose data traffic demand changes over time, we aim at finding plans for assigning APs traffic to MEC facilities so that the demand of each AP is satisfied and MEC facility capacities are not exceeded, yielding high level of service to the users. Since demands are dynamic we allow each AP to be assigned to different MEC facilities at different points in time, accounting for suitable switching costs. We propose a general data-driven framework for our application including an optimization core, a data pre-processing module, and a validation module to test plans accuracy. Our optimization core entails a combinatorial problem that is a multi-period variant of the Generalized Assignment Problem: we design a Branch-and-Price algorithm that, although exact in nature,. performs well also as a matheuristics when combined with early stopping. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach is both computationally effective and accurate when employed for prescriptive analytics. (C) 2018 Elsevier Ltd. All rights reserved.

International audience; We present in this paper a novel solution for the interconnection of LISP (Locator/Identifier Separation Protocol) mapping systems. Our solution, named LISP-MSX, differs from existing approaches in that it allows for complete mapping systems technology independence and for their decentralized interconnection, by means of novel control-plane primitives to LISP and routing protocols, hence guaranteeing faster mappings resolution

International audience; In networking and computing, resource allocation is typically addressed using classical resource allocation protocols as the proportional rule, the max-min fair allocation, or solutions inspired by cooperative game theory. In this paper, we argue that, under awareness about the available resource and other users demands, a cooperative setting has to be considered in order to revisit and adapt the concept of fairness. Such a complete information sharing setting is expected to happen in 5G environments, where resource sharing among tenants (slices) needs to be made acceptable by users and applications, which therefore need to be better informed about the system status via ad-hoc (northbound) interfaces than in legacy environments. We identify in the individual satisfaction rate the key aspect of the challenge of defining a new notion of fairness in systems with complete information sharing and, consequently, a more appropriate resource allocation algorithm. We generalize the concept of user satisfaction considering the set of admissible solutions for bankruptcy games and we adapt to it the fairness indices. Accordingly, we propose a new allocation rule we call Mood Value: for each user, it equalizes our novel game-theoretic definition of user satisfaction with respect to a distribution of the resource. We test the mood value and a new fairness index through extensive simulations about the cellular frequency scheduling use-case, showing how they better support the fairness analysis. We complete the paper with further analysis on the behavior of the mood value in the presence of multiple competing providers and with cheating users.

International audience; Mobile devices are equipped with limited processing power and battery charge. A mobile computation offloading framework is a software that provides better user experience in terms of computation time and energy consumption, also taking profit from edge computing facilities. This article presents User-Level Online Offloading Framework (ULOOF), a lightweight and efficient framework for mobile computation offloading. ULOOF is equipped with a decision engine that minimizes remote execution overhead, while not requiring any modification in the device's operating system. By means of real experiments with Android systems and simulations using large-scale data from a major cellular network provider, we show that ULOOF can offload up to 73% of computations, and improve the execution time by 50% while at the same time significantly reducing the energy consumption of mobile devices.

International audience; In the Bitcoin blockchain, rewarding methods for remunerating miners participating in a pool have to meet certain requirements in order to guarantee the proper functioning of the cryptocurrency ecosystem. In particular, these allocation rules reward pool participants in proportion to their contribution in the transaction validation process. Deployed rewarding methods met fairness concerns at the expense of vulnerability to miners exploiting pools' attractiveness for deciding when to mine for a pool and when to 'hop' to another one resulting more attractive: a phenomenon called pool-hopping. The most used score-based methods are designed to prevent this practice, but are not completely hopping proof. In this work, we propose a methodology to analyze the pool-hopping phenomenon, focusing on the detection of pool-hoppers. Analyzing those Bitcoin transactions that pools create for rewarding its participants, it is possible to determine time epochs where miners worked. Thus, we analyze those miners that have worked intermittently for pools adopting a rewarding system which pays out for each validated block. This evaluation leads us qualifying the miners that have hopped along with their hopping behavior and financial performance.

IFIP Networking 2018, Zürich, SUISSE, 14-/05/2018 - 16/05/2018; International audience; Orchestrating network and computing resources in Mobile Edge Computing (MEC) is an important item in the networking research agenda. In this paper, we propose a novel algorithmic approach to solve the problem of dynamically assigning base stations to MEC facilities, while taking into consideration multiple time-periods, and computing load switching and access latency costs. In particular, leveraging on an existing state of the art on mobile data analytics, we propose a methodology to integrate arbitrary time-period aggregation methods into a network optimization framework. We notably apply simple consecutive time period aggregation and agglomerative hierarchical clustering. Even if the aggregation and optimization methods represent techniques which are different in nature, and whose aim is partially overlapping, we show that they can be integrated in an efficient way. By simulation on real mobile cellular datasets, we show that, thanks to the clustering, we can scale with the number of time-periods considered, that our approach largely outperforms the case without time-period aggregations in terms of MEC access latency, and at which extent the use of clustering and time aggregation affects computing time and solution quality.

International audience; Smart devices equipped with multiple network interfaces are becoming commonplace. Nevertheless, even though multiple interfaces can be used to connect to the Internet, their capabilities have not been fully utilized yet because the default TCP/IP stack supports only a single interface for communication. This situation is now changing due to the emergence of multipath protocols on different network stack layers. For example, many IP level approaches have been proposed utilizing tunneling mechanisms for hiding multipath transmission from the transport protocols. Several working groups under IEEE and IETF are actively standardizing multipath transmission on the link layer and transport layer. Application level approaches enable multipath transmission capability by establishing multiple transport connections and distributing data over them. Given all these efforts, it is beneficial and timely to summarize the state-of-the-art, compare their pros and cons, and discuss about the future directions. To that end, we present a survey on multipath transmission and make several major contributions: 1) we present a complete taxonomy pertaining to multipath transmission, including link, network, transport, application, and cross layers; 2) we survey the state-of-the-art for each layer, investigate the problems that each layer aims to address, and make comprehensive assessment of the solutions; and 3) based on the comparison, we identify open issues and pinpoint future directions for multipath transmission research.

International audience; —We present different design options to implement Augmented Multipath Transmission Control Protocol (A-MPTCP) communications via a Locator/Identifier Separation Protocol (LISP) Traffic Engineering (TE) overlay network. MPTCP allows a TCP connection using multiple subflows to maximize resource usage. LISP is a routing and addressing architecture that provides new semantics for IP communications, by separating the device identity (endpoint identifier) from its location (routing locator) using two different numbering spaces. Our proposition is to adopt a LISP overlay network with traffic engineering capabilities to steer MPTCP subflows across wide-area Internet networks. The resulting augmentation consists of a subflow forwarding that can reach edge bottleneck capacity and surround inter-domain transit bottlenecks and inefficient paths. It can be particularly useful for cases where, even if endpoints are single-homed, inter-domain path diversity can be grasped by the LISP-TE network overlay. We specify the different modes at which this augmentation can take place, from stateless and light modes with very limited management in the network, to stateful and advanced modes implementable by a network provider desiring a higher control on the network. Based on extensive experimentation on the worldwide LISP testbed, we show that the achievable gains up 25% in throughput, while identifying required further improvements.

International audience; The real-time detection of crowded spots in access networks is considered nowadays a necessary step in the evolution of mobile cellular networks as it can be of great benefit for many use-cases. On the one hand, a dynamic positioning of contents and computing resources in the most crowded regions can lower connection latency and data loss and can allow us to have a seamless service provided for the users, without performance degradation across the network. On the other hand, a dynamic resource allocation among access points taking into account their loads can enhance the user's quality of service and indeed network performances. In this context, using real mobile data traces from a cellular network operator in France, provided us with a temporal and spatial analysis of user content consumption habits in different French Metropolitan areas (Paris, Lyon and Nice). Furthermore, we put to use a real-time crowded spot estimator computed using two user mobility metrics, using a linear regression approach. Evaluating our estimator against more than one million user databases from a major French network operator, it appears to be an excellent crowd detection solution of cellular and backhauling network management. We show that its error count definitely decreases with the cell load, and it becomes very small for reasonable crowded spot load reaching S. Hoteit Ecole d'ingénieurs du numérique ISEP 2 Sahar Hoteit et al. upper thresholds. We also show that our crowded spot estimator is time and city-independent as it shows a stable behavior for different times of the day and for different cities with different topographies. Furthermore, compared to another crowded spot estimator from the literature, we show that our proposed estimator offers more suitable and accurate results in terms of crowded spot estimation for the three selected areas.

International audience; Multipath communications at the Internet scale have been a myth for a long time, with no actual protocol being deployed so that multiple paths could be taken by a same connection on the way towards an Internet destination. Recently, the Multipath Transport Control Protocol (MPTCP) extension was standardized and is undergoing a quick adoption in many use-cases, from mobile to fixed access networks, from data-centers to core networks. Among its major benefits – i.e., reliability thanks to backup path rerouting; throughput increase thanks to link aggregation; and confidentiality thanks to harder capacity to intercept a full connection – the latter has attracted lower attention. How interesting would it be using MPTCP to exploit multiple Internet-scale paths hence decreasing the probability of man-in-the-middle (MITM) attacks is a question to which we try to answer. By analyzing the Autonomous System (AS) level graph, we identify which countries and regions show a higher level of robustness against MITM AS-level attacks, for example due to core cable tapping or route hijacking practices.

International audience; Major interest is currently given to the integration of clusters of virtualization servers, also referred to as 'cloudlets' or 'edge clouds', into the access network to allow higher performance and reliability in the access to mobile edge computing services. We tackle the edge cloud network design problem for mobile access networks. The model is such that the virtual machines (VMs) are associated with mobile users and are allocated to cloudlets. Designing an edge cloud network implies first determining where to install cloudlet facilities among the available sites, then assigning sets of access points, such as base stations to cloudlets, while supporting VM orchestration and considering partial user mobility information, as well as the satisfaction of service-level agreements. We present link-path formulations supported by heuristics to compute solutions in reasonable time. We qualify the advantage in considering mobility for both users and VMs as up to 20% less users not satisfied in their SLA with a little increase of opened facilities. We compare two VM mobility modes, bulk and live migration, as a function of mobile cloud service requirements, determining that a high preference should be given to live migration, while bulk migrations seem to be a feasible alternative on delay-stringent tiny-disk services, such as augmented reality support, and only with further relaxation on network constraints.

International audience; Nowadays, the rapid growth of Cloud computing services is stressing the network communication infrastructure in terms of resiliency and programmability. This evolution reveals missing blocks of the current Internet Protocol architecture, in particular in terms of virtual machine mobility management for addressing and locator-identifier mapping. In this paper, we propose some changes to the Locator/Identifier Separation Protocol (LISP) to cope with this gap. We define novel controlplane functions and evaluate them exhaustively in the worldwide public LISP testbed, involving five LISP sites distant from a few hundred kilometers to many thousands kilometers. Our results show that we can guarantee service downtime upon livevirtual machine migration lower than a second across American, Asian and European LISP sites, and down to 300 ms within Europe, outperforming standard LISP and legacy triangular routing approaches in terms of service downtime, as a function of datacenter-datacenter and client-datacenter distances.

International audience; Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and applications. Recently, mobile data-based research reached important conclusions about various aspects of human mobility patterns. But how accurately do these conclusions reflect the reality? To evaluate the difference between reality and approximation methods, we study in this paper the error between real human trajectory and the one obtained through mobile phone data using different interpolation methods (linear, cubic, nearest interpolations) taking into consideration mobility parameters. Moreover, we evaluate the error between real and estimated load using the proposed interpolation methods. From extensive evaluations based on real cellular network activity data of the state of Massachusetts, we show that, with respect to human trajectories, the linear interpolation offers the best estimation for sedentary people while the cubic one for commuters. Another important experimental finding is that trajectory estimation methods show different error regimes whether used within or outside the ''territory'' of the user defined by the radius of gyration. Regarding the load estimation error, we show that by using linear and cubic interpolation methods, we can find the positions of the most crowded regions (''hotspots'') with a median error lower than 7%.

International audience; With the explosion of broadband Over-The-Top (OTT) services, the Internet is autonomously migrating toward overlay and incrementally deployable content distribution infrastructures. Information-Centric Networking (ICN) technologies are the natural candidates to efficiently distribute popular contents to users. However, the strategic incentives in exploiting ICN, for both users and ISPs, are much less understood to date. In this paper, we highlight strategic incentives for ICN overlay adoption in OTT services, that is, we discus how OTTs shall shape their prices to motivate ICN overlay usages.

International audience; Multipath communications at the Internet scale have been a myth for a long time, with no actual protocol being deployed at large scale. Recently, the Multipath Transmission Control Protocol (MPTCP) extension was standardized and is undergoing rapid adoption in many different use-cases, from mobile to fixed access networks, from data-centers to core networks. Among its major benefits-i.e., reliability thanks to backup path rerouting, through-put increase thanks to link aggregation, and confidentiality being more difficult to intercept a full connection-the latter has attracted lower attention. How effective would be to use MPTCP, or an equivalent multipath transport layer protocol, to exploit multiple Internet-scale paths and decrease the probability of Man-in-the-Middle (MITM) attacks is a question which we try to answer. By analyzing the Autonomous System (AS) level graph, we identify which countries and regions show a higher level of robustness against MITM AS-level attacks, for example due to core cable tapping or route hijacking practices. 1

Wireless mesh networks (WMNs) are emerging as a key solution to provide broadband and mobile wireless connectivity in a flexible and cost-effective way. In suburban areas, a common deployment model relies on orthogonal frequency division multiple access (OFDMA) communications between mesh routers (MRs), with one MR installed at each user premises. In this paper, we investigate a possible user cooperation path to implement strategic resource allocation in OFDMA WMNs, under the assumption that users want to control their interconnections. In this case, a novel strategic situation appears: How much an MR can demand, how much it can obtain, and how this shall depend on the interference with its neighbors. Strategic interference management and resource allocation mechanisms are needed to avoid performance degradation during congestion cases between MRs. In this paper, we model the problem as a bankruptcy game taking into account the interference between MRs. We identify possible solutions from cooperative game theory, namely the Shapley value and the nucleolus, and show through extensive simulations of realistic scenarios that they outperform two state-of-the-art OFDMA allocation schemes, namely, centralized-dynamic frequency planning, and frequency-ALOHA. In particular, the nucleolus solution offers best performance overall in terms of throughput and fairness, at a lower time complexity.

International audience; The emergence of Network Functions Virtualization (NFV) is bringing a set of novel algorithmic challenges in the operation of communication networks. NFV introduces volatility in the management of network functions, which can be dynamically orchestrated, i.e., placed, resized, etc. Virtual Network Functions (VNFs) can belong to VNF chains, where nodes in a chain can serve multiple demands coming from the network edges. In this paper, we formally define the VNF placement and routing (VNF-PR) problem, proposing a versatile linear programming formulation that is able to accommodate specific features and constraints of NFV infrastructures, and that is substantially different from existing virtual network embedding formulations in the state of the art. We also design a math-heuristic able to scale with multiple objectives and large instances. By extensive simulations, we draw conclusions on the trade-off achievable between classical traffic engineering (TE) and NFV infrastructure efficiency goals, evaluating both Internet access and Virtual Private Network (VPN) demands. We do also quantitatively compare the performance of our VNF-PR heuristic with the classical Virtual Network Embedding (VNE) approach proposed for NFV orchestration, showing the computational differences, and how our approach can provide a more stable and closer-to-optimum solution.

In networking and computing, resource allocation is typically addressed using classical sharing protocols as, for instance, the proportional division rule, the max-min fair allocation, or other solutions inspired by cooperative game theory. In this paper, we argue that, under awareness about the available resource and other users' demands, in a cooperative setting such classical resource allocation approaches, as well as associated notions of fairness, show important limitations. We identify in the individual satisfaction rate the key aspect of the challenge of defining a new notion of fairness and, consequently, a resource allocation algorithm more appropriate for the cooperative context. We generalize the concept of user satisfaction considering the set of admissible solutions for bankruptcy games. We adapt the Jain's fairness index to include the new user satisfaction rate. Accordingly, we propose a new allocation rule we call ‘Mood Value’. For each user it equalizes our novel game-theoretic definition of user satisfaction with respect to a distribution of the resource. We test the mood value and the new fairness index through extensive simulations showing how they better support the fairness analysis.

International audience; In this paper, we propose data clustering techniques to predict temporal characteristics of data consumption behavior of different mobile applications via wireless communications. While most of the research on mobile data analytics focuses on the analysis of call data records and mobility traces, our analysis concentrates on mobile application usages, to characterize them and predict their behavior. We exploit mobile application usage logs provided by a Wi-Fi local area network service provider to characterize temporal behavior of mobile applications. More specifically, we generate daily profiles of " what " types of mobile applications users access and " when " users access them. From these profiles, we create usage classes of mobile applications via aggregation of similar profiles depending on data consumption rate, using three clustering techniques that we compare. Furthermore, we show that we can utilize these classes to analyze and predict future usages of each mobile application through progressive comparison using distance and similarity comparison techniques. Finally, we also detect and exploit outlying behavior in application usage profiles and discuss methods to efficiently predict them.

—Recent natural disasters have revealed that emer- gency networks presently cannot disseminate the necessary dis- aster information, making it difficult to deploy and coordinate relief operations. These disasters have reinforced the knowledge that telecommunication networks constitute a critical infrastruc- ture of our society, and the urgency in establishing protection mechanisms against disaster-based disruptions. Hence, it is important to have emergency networks able to maintain sustainable communication in disaster areas. Moreover, the network architecture should be designed so that network connectivity is maintained among nodes outside of the impacted area, while ensuring that services for costumers not in the affected area suffer minimal impact. As a first step towards achieving disaster resilience, the RE- CODIS project was formed, and its Working Group 1 members conducted a comprehensive literature survey on “strategies for communication networks to protect against large-scale natural disasters,” which is summarized in this article. We would like to thank the participants of WG1 (Large- scale natural disasters) of COST Action CA15127 who indi- rectly collaborated in this task: Michał Aibin, P´eter Babar- czi, Vitoria Bueno Delgado, Marco Casazza, Anna Fogertun, David Hay, Bjarne E. Helvik, Rita Gir˜ao-Silva, R´o˙za Go´scie´n, Yuming Jiang, Peter Kieseberg, Ioannis Krikidis, Konstantinos Manousakis, Maria do Carmo Medeiros, Cemalettin Ozturk, Jo˜ao Patr´ıcio, Maria Potop-Butucaru, Luis Quesada, Sarah Ruepp, Dorabella Santos, Noor Shirazi, Krzysztof Walkowiak, and Zhongliang Zhao. This article is based upon work from COST Action CA15127 (“Resilient communication services protecting end- user applications from disaster-based failures – RECODIS”) supported by info:eu-repo/semantics/publishedVersion

In-network caching is an important solution for content offloading from content service providers. However despite a rather high maturation in the definition of caching techniques, minor attention has been given to the strategic interaction among the multiple content providers. Situations involving multiple content providers (CPs) and one Internet Service Provider (ISP) having to give them access to its caches are prone to high cache contention, in particular at the appealing topology cross-points. While available cache contention situations from the literature were solved by considering each storage as one autonomous and self managed cache, we propose in this paper to address this contention situation by segmenting the storage on a per-content provider basis (e.g., each CP receives a portion of the storage space depending on its storage demand). We propose a resource allocation and pricing framework to support the network cache provider in the cache allocation to multiple CPs, for situations where CPs have heterogeneous sets of files and untruthful demands need to be avoided. As cache imputations to CPs need to be fair and robust against overclaiming, we evaluate common proportional and max-min fairness (PF, MMF) allocation rules, as well as two coalitional game rules, the Nucleolus and the Shapley value. When comparing our cache allocation algorithm for the different allocation rules with the naive least-recently-used-based cache allocation approach, we find that the latter provides proportional fairness. Moreover, the game-theoretic rules outperform in terms of content access latency the naive cache allocation approach as well as PF and MMF approaches, while sitting in between PF and MMF in terms of fairness. Furthermore, we show that our pricing scheme encourages the CPs to declare their truthful demands by maximizing their utilities for real declarations.