25 research outputs found

    Loss-free architectures in optical burst switched networks for a reliable and dynamic optical layer

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    For the last three decades, the optical fiber has been a quite systematic response to dimensioning issues in the Internet. Originally restricted to long haul networks, the optical network has gradually descended the network hierarchy to discard the bottlenecks. In the 90's, metropolitan networks became optical. Today, optical fibers are deployed in access networks and reach the users. In a near future, besides wireless access and local area networks, all networks in the network hierarchy may be made of fibers, in order to support current services (HDTV) and the emergence of new applications (3D-TV newly commercialized in USA). The deployment of such greedy applications will initiate an upward upgrade. The first step may be the Metropolitan Area Networks (MANs), not only because of the traffic growth, but also because of the variety of served applications, each with a specific traffic profile. The current optical layer is of mitigated efficiency, dealing with unforeseen events. The lack of reactivity is mainly due to the slow switching devices: any on-line decision of the optical layer is delayed by the configuration of the. devices. When the optical network has been extended in the MANs, a lot of efforts has been deployed to improve the reactivity of the optical layer. The Optical Circuit Switching paradigm (OCS) has been improved but it ultimately relies on off-line configuration of the optical devices. Optical Burst Switching (OBS) can be viewed as a highly flexible evolution of OCS, that operates five order of magnitude faster. Within this 'architecture, the loss-free guaranty can be abandoned in order to improve the reactivity of the optical layer. Indeed, reliability and reactivity appear as antagonists properties and getting closer to either of them mitigates the other. This thesis aims at proposing a solution to achieve reliable transmission over a dynamic optical layer. Focusing on OBS networks, our objective is to solve the contention issue without mitigating the reactivity. After the consideration of contention avoidance mechanisms with routing constraints similar as in OCS networks, we investigate the reactive solutions that intend to solve the contentions. None of the available contention resolution scheme can ensure the 100% efficiency that leads to loss-free transmission. An attractive solution is the recourse to electrical buffering, but it is notoriously disregarded because (1) it may highly impact the delays and (2) loss can occur due to buffer overflows. The efficiency of translucent architectures thus highly depends on the buffer availability, that can be improved by reducing the time spent in the buffers and the contention rate. We show that traffic grooming can highly reduce the emission delay, and consequently the buffer occupancy. In a first architecture, traffic grooming is enabled by a translucent core node architecture, capable to re-aggregate incoming bursts. The re-aggregation is mandatory to "de-groom" the bursts in the core network (i.e., to demultiplex the content of a burst). On the one hand, the re-aggregation highly reduces the loss probability, but on the other hand, it absorbs the benefits of traffic grooming. Finally, dynamic access to re-aggregation for contention resolution, despite the significant reduction of the contention rate, dramatically impacts the end-to-end delay and the memory requirement. We thus propose a second architecture, called CAROBS, that exploits traffic grooming in the optical domain. This framework is fully dynamic and can be used jointly with our translucent architecture that performs re-aggregation. As the (de)grooming operations do not involve re-aggregation, the translucent module can be restricted to contention resolution. As a result, the volume of data submitted to re-aggregation is drastically reduced and loss-free transmission can be reached with the same reactivity, end-to-end delay and memory requirement as a native OBS networ

    Online learning on the programmable dataplane

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    This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms

    Proceedings of the 2021 Symposium on Information Theory and Signal Processing in the Benelux, May 20-21, TU Eindhoven

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    Identification through Finger Bone Structure Biometrics

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    Finger Vein Verification with a Convolutional Auto-encoder

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    19th SC@RUG 2022 proceedings 2021-2022

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    19th SC@RUG 2022 proceedings 2021-2022

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    19th SC@RUG 2022 proceedings 2021-2022

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    19th SC@RUG 2022 proceedings 2021-2022

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