IEEE Transactions on Network and Service Management最新文献_第5页

Quantum Federated Learning for Metaverse: Analysis, Design, and Implementation 面向元宇宙的量子联合学习：分析、设计和实现

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-17 DOI: 10.1109/TNSM.2025.3552307

Dev Gurung;Shiva Raj Pokhrel;Gang Li

引用次数: 0

Optimal Placement of the Virtualized Federated Learning Aggregation Function at the Edge 虚拟联邦学习聚合函数在边缘的最优放置

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-13 DOI: 10.1109/TNSM.2025.3551257

Giuseppe Ruggeri;Marica Amadeo;Claudia Campolo;Antonella Molinaro

{"title":"Optimal Placement of the Virtualized Federated Learning Aggregation Function at the Edge","authors":"Giuseppe Ruggeri;Marica Amadeo;Claudia Campolo;Antonella Molinaro","doi":"10.1109/TNSM.2025.3551257","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3551257","url":null,"abstract":"Federated Learning (FL) enables multiple devices (clients) training a shared machine learning (ML) model on local datasets and then sending the updated models to a central server, whose task is aggregating the locally-computed updates and sharing the learned global model again with the clients in an iterative process. The population of clients may change at each round, whereas the node executing the aggregation function is typically placed at an edge domain and remains static until the end of the overall FL training process. Indeed, the computing capabilities of the edge node hosting the aggregation function and the distance (latency) of such a node from the selected clients can highly affect the convergence rate of the FL training procedure. Moreover, the heterogeneous time-varying capabilities of edge nodes, coupled with the dynamic client population selected at each round, call for the optimal dynamic placement of the aggregation function across the available nodes in an edge domain. In this work, we formulate an optimization problem for the placement of the FL aggregation function, which aims to select at each round the edge node able to minimize the overall per-round training time, encompassing the aggregation time, the local training time at the clients and the time for exchanging the global model and the model updates. A time-efficient greedy heuristics is proposed, which is shown to well approximate the optimal solution and outperform the considered benchmark solutions.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 3","pages":"2580-2594"},"PeriodicalIF":4.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling of Heterogeneous 5G Network Slice for Smart Real-Time Railway Communications 面向铁路智能实时通信的5G异构网络切片建模

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-06 DOI: 10.1109/TNSM.2025.3547762

Sławomir Hanczewski;Maciej Stasiak;Joanna Weissenberg;Michał Weissenberg

引用次数: 0

Enabling Stateful TCP Performance Profiling With Key Event Capturing 启用有状态TCP性能分析与关键事件捕获

IF 5.4 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-05 DOI: 10.1109/TNSM.2025.3564336

Ruopeng Geng;Jianyuan Lu;Chongrong Fang;Shaokai Zhang;Jiangu Zhao;Zhigang Zong;Biao Lyu;Shunmin Zhu;Peng Cheng;Jiming Chen

{"title":"Enabling Stateful TCP Performance Profiling With Key Event Capturing","authors":"Ruopeng Geng;Jianyuan Lu;Chongrong Fang;Shaokai Zhang;Jiangu Zhao;Zhigang Zong;Biao Lyu;Shunmin Zhu;Peng Cheng;Jiming Chen","doi":"10.1109/TNSM.2025.3564336","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3564336","url":null,"abstract":"TCP ensures reliable transmission through its stateful implementation and remains crucial today. TCP performance profiling is essential for tasks like diagnosing network performance problems, optimizing transmission performance, and developing new TCP variants, etc. Existing profiling methods lack enough attention to TCP state transition to provide detailed insights on TCP performance. Thus, we build TcpSight, a tool focusing on TCP state transition throughout connection lifetimes. TcpSight conducts stateful analysis by capturing key events using an efficient per-connection lock-free data management mechanism. Besides, TcpSight enhances profiling by integrating application layer information collected from the TCP stack. With the profiling results, users can identify the culprit of TCP performance degradation, and evaluate the performance of TCP algorithms. We design optional modules and filtering mechanisms to reduce TcpSight’s overhead. Our evaluation presents that TcpSight incurs an additional CPU consumption of about 16.6% (without filtering) and 10.6% (with filtering) when the server’s load is 55.7%, and generates storage consumption about 1.88 KB per connection on average. We also give application cases of TcpSight and the deployment experiences in Alibaba Cloud. TcpSight helps in revealing meaningful findings and insights into exploiting TCP in the production deployment.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 5","pages":"4964-4982"},"PeriodicalIF":5.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation 基于联邦云辅助知识蒸馏的无人机轨迹多任务学习与缓存

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-04 DOI: 10.1109/TNSM.2025.3547743

Gerald Tietaa Maale;Noble Arden Elorm Kuadey;Yeasin Arafat;Kwantwi Thomas;Guolin Sun;Guisong Liu

{"title":"Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation","authors":"Gerald Tietaa Maale;Noble Arden Elorm Kuadey;Yeasin Arafat;Kwantwi Thomas;Guolin Sun;Guisong Liu","doi":"10.1109/TNSM.2025.3547743","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3547743","url":null,"abstract":"The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 3","pages":"2516-2533"},"PeriodicalIF":4.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Enhanced Reconfiguration for Deterministic Transmission in Time-Sensitive Networks 时间敏感网络中确定性传输的增强重构

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-04 DOI: 10.1109/TNSM.2025.3547896

Mengjie Guo;Guochu Shou;Yaqiong Liu;Yihong Hu

{"title":"An Enhanced Reconfiguration for Deterministic Transmission in Time-Sensitive Networks","authors":"Mengjie Guo;Guochu Shou;Yaqiong Liu;Yihong Hu","doi":"10.1109/TNSM.2025.3547896","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3547896","url":null,"abstract":"Time-aware shaper (TAS) is key to enabling deterministic guarantees in time-sensitive networks (TSN), but it requires precise configuration for specific traffic scenarios. Dynamic traffic scenarios are increasingly commonplace with the rise of emerging applications, necessitating TAS reconfiguration to adapt to the changes in traffic. However, existing mechanisms primarily reconfigure TAS by generating a new gate control list (GCL) and transitioning to it, which may lead to temporary violations of bounds on delay or jitter, providing no persistently deterministic guarantees. In this paper, we propose a novel TAS reconfiguration mechanism with the virtual GCL (VGCL) to satisfy the demands of dynamic traffic while guaranteeing deterministic transmission. It implements TAS reconfiguration for dynamic traffic by embedding different VGCLs into the GCL, avoiding the need for the GCL transition. Thus, the reconfiguration problem is modeled as an embedding problem by using the VGCL and we develop algorithms to solve it. Experimental results demonstrate that our mechanism can well reconfigure TAS for dynamic traffic without the GCL transition, and increase the reconfiguration success rate in various scenarios compared with the existing approaches.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 3","pages":"2546-2563"},"PeriodicalIF":4.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stackelberg Game-Based Pricing and Offloading for the DVFS-Enabled MEC Systems 支持dvfs的MEC系统的基于Stackelberg游戏的定价和卸载

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-03 DOI: 10.1109/TNSM.2025.3547568

Jing Mei;Cuibin Zeng;Zhao Tong;Zhibang Yang;Keqin Li

{"title":"Stackelberg Game-Based Pricing and Offloading for the DVFS-Enabled MEC Systems","authors":"Jing Mei;Cuibin Zeng;Zhao Tong;Zhibang Yang;Keqin Li","doi":"10.1109/TNSM.2025.3547568","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3547568","url":null,"abstract":"Due to the limited computing resources of both mobile devices (MDs) and the mobile edge computing (MEC) server, devising reasonable strategies for MD task offloading, MEC server resource pricing, and resource allocation is crucial. In this paper, a scenario is considered, comprising multiple MDs and a single MEC server. Each MD has a divisible task in each time slot, allowing for partial offloading and the option to discard parts of the task. The MEC server contains multiple computing units with the same computing power, and its computing resources can be dynamically adjusted through dynamic voltage and frequency scaling (DVFS) according to the size of tasks offloaded by MDs. At any given time slice, a Stackelberg game is formulated based on the strategies of the MDs and the strategy of the MEC server. An iterative evolution algorithm is employed to explore the optimal strategies for MDs and the MEC server. Simulation results demonstrate that both parties can reach an equilibrium state through the game, and these experiments confirm that the algorithm effectively enhances system efficiency.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 3","pages":"2502-2515"},"PeriodicalIF":4.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incremental Semi-Supervised Learning for Data Streams Classification in Internet of Things 基于增量半监督学习的物联网数据流分类

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-02-27 DOI: 10.1109/TNSM.2025.3546649

Jun Jiang;Bin Wang;Quan Tang;Guoxiang Zhong;Xuhao Tang;Joel J. P. C. Rodrigues

{"title":"Incremental Semi-Supervised Learning for Data Streams Classification in Internet of Things","authors":"Jun Jiang;Bin Wang;Quan Tang;Guoxiang Zhong;Xuhao Tang;Joel J. P. C. Rodrigues","doi":"10.1109/TNSM.2025.3546649","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3546649","url":null,"abstract":"Data stream classification is widely used in Internet of Things (IoT) scenarios such as health monitoring, anomaly detection and online diagnosis. Due to the continuous data stream changing dynamically over time, it is impossible to classify all the data simultaneously. Moreover, labeling each sample in practical data stream applications is time-and resource-consuming. The realistic situation is that only a few instances in a data stream are labeled. Therefore, classifying data streams with limited labels has become challenging in IoT scenarios. In this paper, we propose an incremental dynamic weighted semi-supervised method for classifying IoT data streams. Considering the dynamics and continuity in data streams, we use a chunk-based approach to learn the features in the data stream and assign weights to the classifier dynamically. Moreover, we deploy incremental learning methods to continuously learn from the sampled labeled data stream to update the classifier model, which can take advantage of newly incoming labeled data to improve learning performance. Experimental evaluations on seven IoT datasets show that the proposed method outperforms semi-supervised methods in accuracy, precision, and geometric mean (Gmean) by 10% and 5% over supervised methods, respectively.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 3","pages":"2489-2501"},"PeriodicalIF":4.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adapting to the Evolution: Enhancing Intrusion Detection Through Machine Learning in the QUIC Protocol Era 适应演变：在 QUIC 协议时代通过机器学习加强入侵检测

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-02-21 DOI: 10.1109/TNSM.2025.3540753

Adam Kadi;Lyes Khoukhi;Jouni Viinikka;Pierre-Edouard Fabre

{"title":"Adapting to the Evolution: Enhancing Intrusion Detection Through Machine Learning in the QUIC Protocol Era","authors":"Adam Kadi;Lyes Khoukhi;Jouni Viinikka;Pierre-Edouard Fabre","doi":"10.1109/TNSM.2025.3540753","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3540753","url":null,"abstract":"The advent of the QUIC protocol may herald a significant shift in the composition of online traffic in the years to come. The transport layer encryption of the QUIC protocol is one of its main evolutions, especially for metadata that was previously transmitted over TCP traffic without encryption. This new protocol has the potential to require significant alterations in future Internet traffic analysis methods and impact network intrusion detection. On the other side, Machine learning has been used in several research projects to identify network intrusions, with positive outcomes. However, we must take into account new evolution of network traffic. In this paper, we propose a new approach that employs supervised machine learning algorithms to identify flows generated by bots interacting with a Web server during a DDoS attack, focusing on the challenges posed by the QUIC protocol and its implications for effective intrusion detection and cybersecurity. Our contribution in this work is divided into three main parts: 1) A guided process with model architecture for emulating and collecting traffic that depict a range of situations our system may encounter; 2) an analysis module that consists on the creation of two labeled datasets, where observations represent the traffic flows detected in PCAP files. We studied the relevance of different features for these datasets, contributing to a thorough understanding of the quality of the data used; 3) a real world experimention for evaluating the effectiveness of several supervised machine learning algorithms on our datasets. This experimentation allows us to determine which algorithm provides the best prediction results.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1929-1944"},"PeriodicalIF":4.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Class Incremental Website Fingerprinting Attack Based on Dynamic Expansion Architecture 基于动态扩展架构的类增量式网站指纹攻击

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-02-20 DOI: 10.1109/TNSM.2025.3538895

Yali Yuan;Yangyang Du;Guang Cheng

{"title":"Class Incremental Website Fingerprinting Attack Based on Dynamic Expansion Architecture","authors":"Yali Yuan;Yangyang Du;Guang Cheng","doi":"10.1109/TNSM.2025.3538895","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3538895","url":null,"abstract":"Encrypted traffic on anonymizing networks is still at risk of being exposed to the Website Fingerprinting (WF) attack. This attack can seriously threaten the online privacy of users of anonymity networks such as Tor. While deep-learning-based WF attacks achieve high accuracy in controlled experimental settings, they cannot continuously learn after deployment. In real-world environments, new websites are constantly emerging, requiring attackers to expand their monitoring scope continuously. This necessitates attack models capable of continuous learning and expanding classification capabilities. In this paper, we explore how attackers can leverage incremental class learning techniques to continuously learn new classes while retaining the ability to distinguish old ones. This approach mitigates the catastrophic forgetting problem in dynamic, open-world scenarios. We introduce a new WF attack, Class Incremental Fingerprinting (CIF), which employs a scalable architecture enabling Class Incremental Learning (CIL) with limited resources. We evaluate this attack in various scenarios, such as learning 100, 200, and 500 monitored website classes across 5 and 10 incremental tasks, achieving an average accuracy of 97.8% and above. Additionally, we assess the CIF attack’s effectiveness in open-world multi-classification scenarios and test it in few-shot settings using the proposed data augmentation method, Mixtam, achieving an average task accuracy of 87.6% and above with only 30 samples per class.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1955-1971"},"PeriodicalIF":4.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0