IEEE Transactions on Network and Service Management最新文献_第3页

DIDA: Distributed In-Network Intelligent Data Plane for Machine Learning Applications DIDA：用于机器学习应用的分布式网络智能数据平面

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-21 DOI: 10.1109/TNSM.2025.3548477

Giulio Sidoretti;Lorenzo Bracciale;Stefano Salsano;Hesham ElBakoury;Pierpaolo Loreti

引用次数: 0

NAGA: A Deterministic Programmable Network With Update Timing Guarantees NAGA：具有更新定时保证的确定性可编程网络

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-20 DOI: 10.1109/TNSM.2025.3553401

Nemanja Ðerić;Amir Varasteh;Andreas Blenk;Wolfgang Kellerer

{"title":"NAGA: A Deterministic Programmable Network With Update Timing Guarantees","authors":"Nemanja Ðerić;Amir Varasteh;Andreas Blenk;Wolfgang Kellerer","doi":"10.1109/TNSM.2025.3553401","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3553401","url":null,"abstract":"There is no system yet that provides predictable data plane and control plane operations in programmable networks. However, both predictable data plane and control plane operations are needed, e.g., in industrial networks. Particularly there, the operation of the network needs to be planned and, hence, relies on network operations that are deterministic and executed in a timely manner. To fill this gap, this paper proposes our system named <monospace>NAGA</monospace>, which provides data plane deterministic guarantees along with consistent and timely network updates in programmable networks. In order to not rely on specialized hardware, <monospace>NAGA</monospace> uses widely-available hardware capabilities such as priority queuing and label-based forwarding. Whereas the real implementation of <monospace>NAGA</monospace> in a P4-based testbed demonstrates that applications receive guaranteed performance in terms of latency and data rate, simulation studies show the ability of <monospace>NAGA</monospace> to be even deployed in large scale scenarios beyond industrial networks, such as wide area and data center networks.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1874-1888"},"PeriodicalIF":4.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860976","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

P4-Secure: In-Band DDoS Detection in Software Defined Networks P4-Secure：软件定义网络带内DDoS检测

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-03-19 DOI: 10.1109/TNSM.2025.3552844

Liam Daly Manocchio;Yaying Chen;Siamak Layeghy;David Gwynne;Marius Portmann

{"title":"P4-Secure: In-Band DDoS Detection in Software Defined Networks","authors":"Liam Daly Manocchio;Yaying Chen;Siamak Layeghy;David Gwynne;Marius Portmann","doi":"10.1109/TNSM.2025.3552844","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3552844","url":null,"abstract":"Efficient detection of Distributed Denial of Service (DDoS) attacks in datacentres and corporate networks is an active research domain. This paper introduces, P4-Secure, an efficient approach for in-band detection of DDoS attacks, without using the controller resources and channel. The pure in-band implementation of DDoS detection, makes it a practical and viable solution for real-world network security applications, including large-scale backbone networks. The proposed DDoS detection uses an axis-aligned classifier based on the packet asymmetry metric, trained through the negative selection approach. The trained axis-aligned classifier was then implemented in the data plane using P4 programming and managed to classify network flows with a configurable false-positive ratio. Through experiments on two independent real-world network datasets (UQ and ISP) and the CAIDA DDoS attack dataset, the robustness of the proposed approach was evaluated across varying network characteristics. The approach demonstrated a notably superior performance in minimising false positives compared to alternative methods, with a rate of only 0.5%. This achievement was coupled with a 90% F1 score, highlighting its effectiveness in addressing DDoS attacks while avoiding unnecessary false alarms. The evaluation on real-world hardware demonstrates that P4-Secure incurs minimal overhead even at high packet rates, such as 8 Mpps, making it highly suitable for datacentres and backbone network security applications.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"2120-2137"},"PeriodicalIF":4.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860793","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

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

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