International Journal of Network Management最新文献

Software-defined networking (SDN) enables flexible, programmable networks, but in large deployments, poor controller placement can raise latency and energy use. This paper presents a practical in-band SDN model and an optimization method that jointly accounts for control-plane delay and device power consumption to place controllers and configure switches. We formulate the problem as a binary integer programming (BIP) that decides controller locations, which switches remain active, and port bit-rates and routes. Experiments on a large WAN topology (Janos-US) show the approach can cut total network energy by up to 15% while keeping control-plane delays within required bounds, offering network operators a straightforward way to trade responsiveness for energy savings.

Cryptocurrencies represent a significantly utilized class of digital assets, encompassing a diverse array of tokens and coins available for trading purposes. In this study, the integration of machine learning algorithms with an arbitrage trading strategy across cryptocurrency exchanges is explored. The objective is to scrutinize prominent cryptocurrency pairs characterized by high volatility, vulnerability to speculation, regulatory gaps, liquidity constraints, and heavy-tail distribution, with the intention of training the model to predict the potential for arbitrage. To differentiate from competitors who await rare arbitrage opportunities, a novel approach is introduced to enhance arbitrage profitability. The primary innovation of this study lies in demonstrating the capability to predict profitable arbitrage opportunities in discrete intervals in advance, by incorporating sophisticated confidence level metrics to initiate arbitrage trades only when the model's predictions demonstrate substantial certainty. The findings indicate that the profitability of the entire strategy exceeds 100% within a 1-week timeframe.

The Internet of Things (IoT) requires sophisticated security due to heterogeneity and resource constraints. Current anomaly detection (AD) approaches address none of these challenges. Local AD models can account for device heterogeneity. However, existing approaches cannot run on constrained devices. This paper implements decentralized local AD models. Each model processes data from only one device. Simplifying the prediction task results in lightweight AD models. They provide an opportunity to address the resource constraints of devices. With less need for processing power, IoT devices can perform AD on their own. The novel approach improves the optimization metrics of detection performance, latency, bandwidth usage, privacy, and model complexity. Further optimization using model aggregation speeds up the creation of AD models. The evaluation uses the publicly available UNSW-NB15 dataset. It shows that models can be simplified to run on IoT devices. Measurements with a local model on a Raspberry PI show only a slight increase in training and processing time compared with central remote processing on a significantly more powerful desktop PC. While the accuracy remains > 98%, the F1 score increases from 0.64 to 0.89 in the decentralized approach. The time for the creation of models is reduced by more than 90%.

The growing demands for network capacity and the increasing complexities of modern network environments pose significant challenges for effective network management and operations. In response, network operators and administrators are moving beyond traditional manual and rule-based methods, adopting advanced artificial intelligence (AI)-driven paradigms (e.g., self-driving networks, autonomous networks, network automation). Recently, large language models (LLMs) have emerged as a promising AI technology with the potential to revolutionize network management and operations through natural language interaction. In this paper, we provide a comprehensive survey of LLM-based approaches in network management and compare those approaches with existing methods. We identify key advantages of LLM-based approaches, such as their ability to interpret intent and automate complex tasks, as well as limitations, which include hallucinations and domain adaptation challenges. Based on these insights, we outline open technical challenges and propose future research directions to guide the development of LLM-based network management.

The demands of today's 5G mobile network, especially low latency and high bandwidth, are a big challenge for the 5G Core (5GC) provider. The most critical user data packet handler in the 5GC network function (NF) is the user plane function (UPF), which is responsible for moving data from the user equipment to the destination data network, and vice versa. Existing work mainly focuses on implementing UPF using the key technologies of high-speed data processing. In this paper, with a mobile core provider called free5GC for a stand-alone (SA) 5G network, we share our experience with the implementation of UPF by using a programmable hardware appliance, which can offer more Tbps compared with the implementation of software UPF that can offer only a few hundred Gbps. For that, we demonstrate how to build up a more flexible architecture of UPF by using the software-defined networking (SDN) concept due to the opacity of protocol specification. We split the UPF control signal implementation into a software application and user data packet processing into a programmable hardware appliance. We also show how to integrate a number of current UPF data plane free5GC implementations, such as Data Plane Development Kit (DPDK), Linux kernel module, and SmartNIC. Furthermore, we analyze and make use of microservices to support the specific features of the UPF data plane that cannot be implemented in a programmable hardware appliance. We tested our free5GC mobile network and the new UPF design architecture that can run on a real programmable hardware appliance from Accton CSP-7551. The evaluation results show that our programmable user plane can reach the line rate.