314 research outputs found

    Simulation and experimental evaluation of a flexible time triggered ethernet architecture applied in satellite nano/micro launchers

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    The success of small satellites has led to the study of new technologies for the realization of Nano and Micro Launch Vehicle (NMLV) in order to make competitive launch costs. The paper has the objective to define and experimentally investigate the performance of a communication system for NMLV interconnecting the End Systems as On-Board Computer (OBC), telemetry apparatus, Navigation Unit...we propose a low cost Ethernet-based solution able to provide the devices with high interconnection bandwidth. To guarantee hard delays to the Guide, Navigation and Control applications we propose some architectural changes of the traditional Ethernet network with the introduction of a layer implemented in the End Systems and allow for the lack of any contention on the network links. We show how the proposed solution has comparable performance to the one of TTEthernet standard that is a very expensive solution. An experimental test-bed equipped with Ethernet switches and Hercules boards by Texas Instruments is also provided to prove the feasibility of the proposed solution

    evaluation of power saving and feasibility study of migrations solutions in a virtual router network

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    The power consumption of the network equipment has increased significantly and some strategies to contain the power used in the IP network are needed. Among the green networking strategies, the virtualization class and in particular the deployment of migrating virtual routers can lead to a high energy saving. It consists in migrating virtual routers in fewer physical nodes when the traffic decreases allowing for a power consumption saving. In this paper we formulate the problem of minimizing the power consumption as a Mixed Integer Linear Programming (MILP) problem. Due to the hard complexity of the introduced MILP problem, we propose a heuristic for the migration of virtual routers among physical devices in order to turn off as many nodes as possible and save power according to the compliance with network node and link capacity constraints. We show that 50% of nodes may be turned off in the case of a real provider network when traffic percentage reduction of 80% occurs. Finally we also perform a feasibility study by means of an experimental test-bed to evaluate migration time of a routing plane based on QUAGGA routing software

    Proposal and investigation of a distributed learning strategy in Orbital Edge Computing-endowed satellite networks for Earth Observation applications

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    One of the key enabling solutions to in-orbit extract information from Earth Observation images is given by deep learning techniques. However, the accuracy of these algorithms is strictly related to the availability of large datasets of satellite images for training purposes. Limitations on the available transmission bandwidth in the orbital context may prevent the possibility to downlink all acquired images to a node where centralized training happens. Instead, Federated Learning (FL) could be fruitfully leveraged in this scenario, since it provides for each satellite to train a local model only with its own dataset, and then to share its trained model with a central server, which receives models trained by the different satellites and aggregates them into a new global model being eventually shared with all the satellites, and this repeats until convergence is reached. However, because communication with a node acting as a central parameter server may be still limited by short visibility time, the described process may need a long time because of limited communication windows, negatively impacting the time needed to reach model convergence. For this reason, we propose a communication strategy to support a completely distributed learning technique to train a deep learning model in-orbit, by leveraging the fact that satellites may form a network thanks to the potential availability of Inter-Satellite Links (ISLs) within and between orbital planes. Our proposal is different from a FL approach since we provide for each satellite to receive all the information needed to calculate an updated global model by itself, without leaning on a central parameter server. Numerical results show that distributed learning outperforms FL in number of learning rounds completed in the unit time, allowing for reaching validation accuracy convergence in a shorter time, as it has been verified on a land coverage classification task based on the EuroSAT dataset

    SOA-Based Optical Packet Switching Architectures

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    The service evolution and the rapid increase in traffic levels fuel the interest toward switching paradigms enabling the fast allocation of Wavelength Division Multiplexing WDM channels in an on demand fashion with fine granularities (microsecond scales). For this reason, in the last years, different optical switching paradigms have been proposed: optical-packet switching (OPS), optical-burst switching (OBS), wavelength-routed OBS, etc. Among the various all-optical switching paradigms, OPS attracts increasing attention. Owing to the high switching rate, Semiconductor Optical Amplifier (SOA) is a key technology to realize Optical Packet Switches. We propose some Optical Packet Switch (OPS) architectures and illustrate their realization in SOA technology. The effectiveness of the technology in reducing the power consumption is also analyzed. The chapter is organized in three sections. The main blocks (Switching Fabric, Wavelength Conversion stage, Synchronization stage) of an OPS are illustrated in Section 2 where we also show some examples of realizing wavelength converters and synchronizers in SOA technology. Section 3 introduces SOA-based single-stage and multi-stage switching fabrics. Finally the SOA-based OPS power consumption is investigated in Section 4

    Reconfiguration of optical-NFV network architectures based on cloud resource allocation and QoS degradation cost-aware prediction techniques

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    The high time required for the deployment of cloud resources in Network Function Virtualization network architectures has led to the proposal and investigation of algorithms for predicting trafc or the necessary processing and memory resources. However, it is well known that whatever approach is taken, a prediction error is inevitable. Two types of prediction errors can occur that have a different impact on the increase in network operational costs. In case the predicted values are higher than the real ones, the resource allocation algorithms will allocate more resources than necessary with the consequent introduction of an over-provisioning cost. Conversely, when the predicted values are lower than the real values, the allocation of fewer resources will lead to a degradation of QoS and the introduction of an under-provisioning cost. When over-provisioning and under-provisioning costs are different, most of the prediction algorithms proposed in the literature are not adequate because they are based on minimizing the mean square error or symmetric cost functions. For this reason we propose and investigate a forecasting methodology in which it is introduced an asymmetric cost function capable of weighing the costs of over-provisioning and under-provisioning differently. We have applied the proposed forecasting methodology for resource allocation in a Network Function Virtualization architectures where the Network Function Virtualization Infrastructure Point-of-Presences are interconnected by an elastic optical network.We have veried a cost savings of 40% compared to solutions that provide a minimization of the mean square error

    Proposal and Investigation of a Lite Time Sensitive Networking solution for the support of Real Time Services in Space Launcher Networks

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    Most launcher networks are based on proprietary buses such as MIL-STD-1553B whose low bandwidth limits the introduction of new services of suitable characteristics. Ethernet technology, because of its low cost and high performance, has been considered an excellent candidate for its use in launcher networks. The real time Ethernet solutions based on the Time Sensitive Networking (TSN) standards seem the most suitable because of its multi-vendor product characteristics. In this paper we propose a real time Ethernet solution for aerospace applications in which negligible jitter services has to be guaranteed. The proposed solution is based on the following TSN standards: IEEE 802.1AS/ASrev as synchronization protocol and 802.1Qbv-2015 for deterministic traffic scheduling. To improve both the bandwidth effective and the frame delay the solution is also based on a change in the management of the Priority Code Point field in IEEE 802.1Q standard. The optimal scheduling problem is formulated so as to minimize the makespan, defined as the time needed to deliver all of the messages of an elementary cycle. The problem has been resolved with the CPLEX solver and the proposed solution has been evaluated in terms of both delay and bandwidth effective by comparing its performance with the TTEthernet, FTTEthernet benchmark solutions. The obtained results in a real traffic scenario characterized by the set of messages of the VEGA launcher show how the proposed solution allows for the same performance of TTEthernet, i.e., the solution of proprietary and real-time Ethernet with better performance

    Proposal and investigation of an artificial intelligence (Ai)-based cloud resource allocation algorithm in network function virtualization architectures

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    The high time needed to reconfigure cloud resources in Network Function Virtualization network environments has led to the proposal of solutions in which a prediction based-resource allocation is performed. All of them are based on traffic or needed resource prediction with the minimization of symmetric loss functions like Mean Squared Error. When inevitable prediction errors are made, the prediction methodologies are not able to differently weigh positive and negative prediction errors that could impact the total network cost. In fact if the predicted traffic is higher than the real one then an over allocation cost, referred to as over-provisioning cost, will be paid by the network operator; conversely, in the opposite case, Quality of Service degradation cost, referred to as under-provisioning cost, will be due to compensate the users because of the resource under allocation. In this paper we propose and investigate a resource allocation strategy based on a Long Short Term Memory algorithm in which the training operation is based on the minimization of an asymmetric cost function that differently weighs the positive and negative prediction errors and the corresponding over-provisioning and under-provisioning costs. In a typical traffic and network scenario, the proposed solution allows for a cost saving by 30% with respect to the case of solution with symmetric cost function

    Dynamic in-network classification for service function chaining ready SDN networks

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    Service Function Chaining (SFC) paradigm consists in steering traffic flows through an ordered set of Service Functions (SFs) so that to realize complex end to end services. SFC architecture introduces all the logical functions that need to be developed in order to provide the required service. The SFC overlay infrastructure can be built on top of many different underlay network technologies. The high flexibility and centrally controlled feature of Software Defined Networking (SDN), make SDN networks to be a perfect underlay to build the SFC architecture. Due to Ternary Content Address Memory (TCAM) limited size, SDN switches have a limitation in the number of flow rules that can be hosted. This constraint is particularly penalizing in case of the SFC classifier function, since it requires to manage a high number of different flows. The limitation imposed by the TCAM size on the SFC classifier can be a bottleneck for the number of SFC requests that the SDN-based SFC architecture can handle. In this paper we define the Dynamic Chain Request Classification Offloading (D-CRCO) problem, as the one of maximizing the number of accepted SFC requests, having the possibility of: i) implement the SFC classifier also in a node that is internal to the SDN-based SFC domain, and ii) install classification rules in a reactive fashion. Furthermore, we propose the Dynamic Nearest Node (DNN) heuristic to solve the D-CRCO problem. Performance evaluation shows that by using DNN heuristic it is possible to triple the number of accepted requests, with respect to existing solutions

    Comparative analysis of power consumption in asynchronous wavelength modular optical switching fabrics

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    Next-generation optical routers will be designed to support the flexibility required by Future Internet services and, at the same time, to overcome the power consumption bottleneck which appears to limit throughput scalability in today routers. A model to evaluate average power consumption in asynchronous optical switching fabrics is here presented to compare these architectures with other synchronous and asynchronous solutions. The combination of wavelength modular switching fabrics with low spatial complexity and asynchronous operation is demonstrated to be the most power-efficient solution among those considered which employ wavelength converters, through presentation and discussion of a thorough set of numerical results. © 2011 Elsevier B.V. All rights reserved

    Optical delay control of large-spectral-bandwidth laser pulses

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    In this letter we report the first experimental observation of temporal delay control of large-spectral-bandwidth multimode laser pulses by means of electromagnetically induced transparency (EIT). We achieved controllable retardation with limited temporal distortion of optical pulses with an input spectral bandwidth of 3.3 GHz. The experimental results compare favorably with theoretical predictions.Comment: Submitted to Optics Letters (January 2009
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