1,539 research outputs found

    End-to-end provisioning in multi-domain/multi-layer networks

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    The last decade has seen many advances in high-speed networking technologies. At the Layer 1 fiber-optic level, dense wavelength division multiplexing (DWDM) has seen fast growth in long-haul backbone/metro sectors. At the Layer 1.5 level, revamped next-generation SONET/SDH (NGS) has gained strong traction in the metro space, as a highly flexible sub-rate\u27 aggregation and grooming solution. Meanwhile, ubiquitous Ethernet (Layer 2) and IP (Layer 3) technologies have also seen the introduction of new quality of service (QoS) paradigms via the differentiated services (Diff-Serv) and integrated services (Intserv) frameworks. In recent years, various control provisioning standards have also been developed to provision these new networks, e.g., via efforts within the IETF, ITU-T, and OIF organizations. As these networks technologies gain traction, there is an increasing need to internetwork multiple domains operating at different technology layers, e.g., IP, Ethernet, SONET, DWDM. However, most existing studies have only looked at single domain networks or multiple domains operating at the same technology layer. As a result, there is now a growing level of interest in developing expanded control solutions for multi-domain/multi-layer networks, i.e., IP-SONET-DWDM. Now given the increase in the number of inter-connected domains, it is difficult for a single entity to maintain complete \u27global\u27 information across all domains. Hence, related solutions must pursue a distributed approach to handling multi-domain/multi-layer problem. Namely, key provisions are needed in the area of inter- domain routing, path computation, and signaling. The work in this thesis addresses these very challenges. Namely, a hierarchical routing framework is first developed to incorporate the multiple link types/granularities encountered in different network domains. Commensurate topology abstraction algorithms and update strategies are then introduced to help condense domain level state and propagate global views. Finally, distributed path computation and signaling setup schemes are developed to leverage the condensed global state information and make intelligent connection routing decisions. The work leverages heavily from graph theory concepts and also addresses the inherent distributed grooming dimension of multi-layer networks. The performance of the proposed framework and algorithms is studied using discrete event simulation techniques. Specifically, a range of multi-domain/multi-layer network topologies are designed and tested. Findings show that the propagation of inter-domain tunneled link state has a huge impact on connection blocking performance, lowering inter-domain connection blocking rates by a notable amount. More importantly, these gains are achieved without any notable increase in inter-domain routing loads. Furthermore, the results also show that topology abstraction is most beneficial at lower network load settings, and when used in conjunction with load-balancing routing.\u2

    Precomputation schemes for QoS routing

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    Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges

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    In the last decade, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges

    C-RAN CoMP Methods for MPR Receivers

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    The growth in mobile network traffic due to the increase in MTC (Machine Type Communication) applications, brings along a series of new challenges in traffic routing and management. The goals are to have effective resolution times (less delay), low energy consuption (given that wide sensor networks which are included in the MTC category, are built to last years with respect to their battery consuption) and extremely reliable communication (low Packet Error Rates), following the fifth generation (5G) mobile network demands. In order to deal with this type of dense traffic, several uplink strategies can be devised, where diversity variables like space (several Base Stations deployed), time (number of retransmissions of a given packet per user) and power spreading (power value diversity at the receiver, introducing the concept of SIC and Power-NOMA) have to be handled carefully to fulfill the requirements demanded in Ultra-Reliable Low-Latency Communication (URLLC). This thesis, besides being restricted in terms of transmission power and processing of a User Equipment (UE), works on top of an Iterative Block Decision Feedback Equalization Reciever that allows Multi Packet Reception to deal with the diversity types mentioned earlier. The results of this thesis explore the possibility of fragmenting the processing capabilities in an integrated cloud network (C-RAN) environment through an SINR estimation at the receiver to better understand how and where we can break and distribute our processing needs in order to handle near Base Station users and cell-edge users, the latters being the hardest to deal with in dense networks like the ones deployed in a MTC environment
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