International Journal of Network Management最新文献

Although many studies have conducted the traffic scheduling of time-sensitive networks, most focus on small-scale static scheduling for specific scenarios, which cannot cope with dynamic and rapid scheduling of time-triggered (TT) flows generated in scalable scenarios in the Industrial Internet of Things. In this paper, we propose a Scalable TT flow scheduling method based on Dynamic Online Grouping in industrial time-sensitive networks (SDOG). To achieve that, we establish an undirected weighted flow graph based on the conflict index between TT flows and divide available time into equally spaced time windows. We dynamically group the TT flows within each window locally. When the number of flows to be scheduled doubles, we can achieve scalable and efficient solutions to efficiently schedule dynamic TT flows, avoiding unnecessary conflicts during data communication. In addition, a topology pruning strategy is adopted to prune the network topology of each group, reducing unnecessary link variables and further effectively shortening the scheduling time. Experimental results validated our expected performance and demonstrated that our proposed SDOG scheduling method has advantages in terms of overall traffic schedulability and average time for scheduling individual traffic.

Network slicing, which enables efficient resource management to meet specific service requirements, provides a scalable solution for optimizing music transmission and live performance in mobile networks beyond 5G and into 6G. The research focuses on optimizing live performances as well as music transmission. Since AI-driven resource management improves performance quality and real-time music streaming in dynamic 6G network situations, these factors are interconnected. This approach allows multiple tenants, such as event organizers and music producers, to share infrastructure while customizing communication and quality standards for real-time music services. To ensure optimal resource allocation, including high bandwidth, low latency, and consistent service quality, network slices are dynamically configured by the infrastructure provider. Although the implementation of network slicing in the core network has been well studied, applying it within the radio access network (RAN) presents challenges, especially given the unpredictability of wireless channels and the strict quality of service (QoS) demands for live music streaming. For 6G networks, the article suggests a tenant-driven RAN slicing method improved by artificial intelligence (AI) to maximize music performance and transmission. A hybrid AI framework integrates a deep recurrent neural network (DRNN) for continuous monitoring and prediction of network conditions with a deep Q-network (DQN) augmented by prioritized experience replay (PER) for real-time resource adaptation. The DRNN forecasts network states to guide high-level resource allocation, whereas DQN with PER dynamically manages routing and bandwidth based on past critical network states, enabling rapid responses to fluctuating performance demands. Comparative results indicate that the suggested approach outperforms conventional techniques, achieving a latency of 25 ms, an audio quality of 4.6, and a bandwidth utilization of 90%. Simulation results in live music and enhanced mobile broadband (eMBB) environments demonstrate the proposed approach's effectiveness in minimizing latency, enhancing audio quality, and stabilizing transmission, surpassing traditional network allocation techniques.

The information society has led to a shift in traditional English education methods, with the evolution of technology, particularly internet and communication network technologies, reshaping the teaching landscape. This facilitated innovative instructional approaches and enhanced the learning experience. This research introduces a novel virtual learn net architecture (VLNA) within the 6G network layers, which processes the performance of the virtual reality-based English education system (VR-EES) model to provide a seamless, personalized learning experience for online learners. This architecture is structured into several layers: The user equipment (UE) layer connects VR headsets to the network with ultrareliable, low-latency links; the radio access network (RAN) layer, employing massive MIMO and beam forming, enhances connection speed, capacity, and coverage. Edge computing handles latency-sensitive tasks like speech recognition and adaptive content delivery, reducing the load on the core network. The core network layer (CLN) manages network slices for specific learning tasks such as real-time interaction, high-definition multimedia, and computation-intensive processes, with control plane and user plane separation (CUPS) optimizing network management and security through end-to-end encryption. Software-defined networking (SDN) and network function virtualization (NFV) provide centralized, dynamic control, allowing real-time resource allocation based on demand. Cloud-edge integration supports Artificial intelligence (AI)-driven adaptive learning, optimizing educational content delivery based on individual progress. The study results demonstrate that stimulation of VLNA achieved significant improvements in latency reduction, bandwidth utilization, throughput, packet loss rate, jitter, user engagement, learning efficiency, and user satisfaction. The integration of edge computing and network slicing led to a significant reduction in latency, while the enhanced throughput enabled seamless VR experiences. In this study, latency reduction, bandwidth utilization, and user satisfaction emerge as the most significant factors, with user satisfaction standing out as the top performer due to its substantial impact on enhancing the overall learning experience. The packet loss rate is maintained to a certain level, ensuring reliable data transmission. The VR-EES model's experimental results also enhanced visual learning, multimedia quality, user pleasure, learning effectiveness, and user engagement.

In this study, we propose and evaluate a model that utilizes both log data and state data to detect abnormal conditions in network switches. Building upon our previous research and drawing inspiration from TF-IDF used in natural language processing to measure word importance, we propose a statistical method, Log-TF-IDF, to quantify the rarity of each log pattern in the log data. Furthermore, based on this Log-TF-IDF, we introduce the AB Score, which quantifies how abnormal the current log pattern is. Our findings indicate that the AB Score is notably higher and more volatile in abnormal conditions. We confirm that anomaly detection is feasible through the AB Score, which has the advantage of being computationally efficient due to its statistical basis. We combined the metrics generated during the AB Score calculation with resource data collected with NETCONF and developed a machine-learning model to detect abnormal conditions in network switches. We confirm that this model can detect abnormal conditions with an F1 score of 0.86 on our collected dataset, confirming its viability for detecting abnormal states in network equipment.

Multitopology routing (MTR) provides an attractive alternative to segment routing (SR) for traffic engineering when network devices cannot be upgraded. However, due to a high overhead in terms of link state messages exchanged by topologies and the need to frequently update link weights to follow evolving network conditions, MTR is often limited to a small number of topologies and the satisfaction of loose QoS constraints. To overcome these limitations, we propose virtual MTR (vMTR), an MTR extension where demands are routed over virtual topologies that are silent; that is, they do not exchange LSA messages and that are continuously derived from a very limited set of real topologies, optimizing each QoS parameter. In this context, we present a polynomial and exact algorithm for vMTR and, as a benchmark, a local search algorithm for MTR. We show that vMTR helps to reduce drastically the number of real topologies and that it is more robust to QoS changes. In the case where SR can actually be rolled-out, we also show that vMTR allows to drastically reduce SR overhead.

Many studies have used machine learning techniques for bitrate control to improve the quality of experience (QoE) of video streaming applications. However, most of these studies have focused on HTTP adaptive streaming with one-to-one connections. This research examines video conferencing applications that involve real-time, multiparty, and full-duplex communication among participants. In conventional video conferencing systems, a rule-based algorithm is typically employed to estimate the available bandwidth of each participant, and the outcomes are then used to control the video delivery rate to the participant. This paper proposes Muno, a bandwidth prediction framework based on deep reinforcement learning (DRL) for multiparty video conferencing systems. Muno aims to enhance the overall QoE by using DRL to improve bandwidth estimation for each connection. The experimental results indicate that Muno achieves a significantly higher video streaming rate, video resolution, and framerate while lowering delay in highly dynamic networks when compared to the state-of-the-art rule-based algorithms and roughly equivalent streaming rate and delay in stable networks. Moreover, Muno can generalize well to different network conditions which were not included in the training set. We also implemented a high-performance and scalable version of Muno for in-campus deployment.