3,399 research outputs found

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    CN2F: A Cloud-Native Cellular Network Framework

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    Upcoming 5G and Beyond 5G (B5G) cellular networks aim to improve the efficiency and flexibility of mobile networks by incorporating various technologies, such as Software Defined Networking (SDN), Network Function Virtualization (NFV), and Network Slicing (NS). In this paper, we share our findings, accompanied by a comprehensive online codebase, about the best practice of using different open-source projects in order to realize a flexible testbed for academia and industrial Research and Development (R&D) activities on the future generation of cellular networks. In particular, a Cloud-Native Cellular Network Framework (CN2F) is presented which uses OpenAirInterface's codebase to generate cellular Virtual Network Functions (VNFs) and deploys Kubernetes to disperse and manage them among some worker nodes. Moreover, CN2F leverages ONOS and Mininet to emulate the effect of the IP transport networks in the fronthaul and backhaul of real cellular networks. In this paper, we also showcase two use cases of CN2F to demonstrate the importance of Edge Computing (EC) and the capability of Radio Access Network (RAN) slicing

    A survey of trends and motivations regarding Communication Service Providers' metro area network implementations

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    Relevance of research on telecommunications networks is predicated upon the implementations which it explicitly claims or implicitly subsumes. This paper supports researchers through a survey of Communications Service Providers current implementations within the metro area, and trends that are expected to shape the next-generation metro area network. The survey is composed of a quantitative component, complemented by a qualitative component carried out among field experts. Among the several findings, it has been found that service providers with large subscriber base sizes, are less agile in their response to technological change than those with smaller subscriber base sizes: thus, copper media are still an important component in the set of access network technologies. On the other hand, service providers with large subscriber base sizes are strongly committed to deploying distributed access architectures, notably using remote access nodes like remote OLT and remote MAC-PHY. This study also shows that the extent of remote node deployment for multi-access edge computing is about the same as remote node deployment for distributed access architectures, indicating that these two aspects of metro area networks are likely to be co-deployed.Comment: 84 page

    20th SC@RUG 2023 proceedings 2022-2023

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    QoS-aware architectures, technologies, and middleware for the cloud continuum

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    The recent trend of moving Cloud Computing capabilities to the Edge of the network is reshaping how applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of diverse application domains and next-generation applications. Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, are expected to serve a wide range of applications with heterogeneous QoS requirements and call for QoS management systems to guarantee/control performance indicators, even in the presence of real-world factors such as limited bandwidth and concurrent virtual resource utilization. The present dissertation proposes a comprehensive QoS-aware architecture that addresses the challenges of integrating cloud infrastructure with edge nodes in IoT applications. The architecture provides end-to-end QoS support by incorporating several components for managing physical and virtual resources. The proposed architecture features: i) a multilevel middleware for resolving the convergence between Operational Technology (OT) and Information Technology (IT), ii) an end-to-end QoS management approach compliant with the Time-Sensitive Networking (TSN) standard, iii) new approaches for virtualized network environments, such as running TSN-based applications under Ultra-low Latency (ULL) constraints in virtual and 5G environments, and iv) an accelerated and deterministic container overlay network architecture. Additionally, the QoS-aware architecture includes two novel middlewares: i) a middleware that transparently integrates multiple acceleration technologies in heterogeneous Edge contexts and ii) a QoS-aware middleware for Serverless platforms that leverages coordination of various QoS mechanisms and virtualized Function-as-a-Service (FaaS) invocation stack to manage end-to-end QoS metrics. Finally, all architecture components were tested and evaluated by leveraging realistic testbeds, demonstrating the efficacy of the proposed solutions

    Pandemic Protagonists: Viral (Re)Actions in Pandemic and Corona Fictions

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    During the first mandatory lockdowns of the Covid-19 pandemic, citizens worldwide turned to "pandemic fictions" or started to produce their own »Corona Fictions« across different media. These accounts of (previously) experienced or imagined health crises feature a great variety of protagonists and their (re)actions in response to the exceptional circumstances. The contributors to this volume take a closer look at different pandemic protagonists in fictional narratives relating to the Covid-19 pandemic as well as in existing pandemic fictions. Thereby they provide new insights into pandemic narratives from a cultural, literary, and media studies perspective from antiquity to today
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