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

Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-Agent Reinforcement Learning Framework 混合软件定义网络中的分布式流量工程：多代理强化学习框架

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-04 DOI: 10.1109/TNSM.2024.3454282

Yingya Guo;Bin Lin;Qi Tang;Yulong Ma;Huan Luo;Han Tian;Kai Chen

{"title":"Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-Agent Reinforcement Learning Framework","authors":"Yingya Guo;Bin Lin;Qi Tang;Yulong Ma;Huan Luo;Han Tian;Kai Chen","doi":"10.1109/TNSM.2024.3454282","DOIUrl":"10.1109/TNSM.2024.3454282","url":null,"abstract":"Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in distributed TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal in a hybrid SDN scenario, we construct a reasonable virtual environment to meet different routing constraints brought by legacy routers and SDN switches for training the routing agents. To train the routing agents for determining the local routing policies according to local network observations, we introduce the difference reward assignment mechanism for encouraging agents to cooperatively take optimal routing action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6759-6769"},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187261","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-Agent DRL-Based Energy Harvesting for Freshness of Data in UAV-Assisted Wireless Sensor Networks 基于 DRL 的多代理能量收集，提高无人机辅助无线传感器网络的数据新鲜度

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-04 DOI: 10.1109/TNSM.2024.3454217

Mesfin Leranso Betalo;Supeng Leng;Hayla Nahom Abishu;Abegaz Mohammed Seid;Maged Fakirah;Aiman Erbad;Mohsen Guizani

{"title":"Multi-Agent DRL-Based Energy Harvesting for Freshness of Data in UAV-Assisted Wireless Sensor Networks","authors":"Mesfin Leranso Betalo;Supeng Leng;Hayla Nahom Abishu;Abegaz Mohammed Seid;Maged Fakirah;Aiman Erbad;Mohsen Guizani","doi":"10.1109/TNSM.2024.3454217","DOIUrl":"10.1109/TNSM.2024.3454217","url":null,"abstract":"In sixth-generation (6G) networks, unmanned aerial vehicles (UAVs) are expected to be widely used as aerial base stations (ABS) due to their adaptability, low deployment costs, and ultra-low latency responses. However, UAVs consume large amounts of power to collect data from multiple sensor nodes (SNs). This can limit their flight time and transmission efficiency, resulting in delays and low information freshness. In this paper, we present a multi-access edge computing (MEC)-integrated UAV-assisted wireless sensor network (WSN) with a laser technology-based energy harvesting (EH) system that makes the UAV act as a flying energy charger to address these issues. This work aims to minimize the age of information (AoI) and improve energy efficiency by jointly optimizing the UAV trajectories, EH, task scheduling, and data offloading. The joint optimization problem is formulated as a Markov decision process (MDP) and then transformed into a stochastic game model to handle the complexity and dynamics of the environment. We adopt a multi-agent deep Q-network (MADQN) algorithm to solve the formulated optimization problem. With the MADQN algorithm, UAVs can determine the best data collection and EH decisions to minimize their energy consumption and efficiently collect data from multiple SNs, leading to reduced AoI and improved energy efficiency. Compared to the benchmark algorithms such as deep deterministic policy gradient (DDPG), Dueling DQN, asynchronous advantage actor-critic (A3C) and Greedy, the MADQN algorithm has a lower average AoI and improves energy efficiency by 95.5%, 89.9%, 78.02% and 65.52% respectively.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6527-6541"},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187263","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

QoE-Driven Cross-Layer Bitrate Allocation Approach for MEC-Supported Adaptive Video Streaming 支持 MEC 的自适应视频流的 QoE 驱动型跨层比特率分配方法

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-03 DOI: 10.1109/TNSM.2024.3453992

Yashar Farzaneh Yeznabad;Markus Helfert;Gabriel-Miro Muntean

{"title":"QoE-Driven Cross-Layer Bitrate Allocation Approach for MEC-Supported Adaptive Video Streaming","authors":"Yashar Farzaneh Yeznabad;Markus Helfert;Gabriel-Miro Muntean","doi":"10.1109/TNSM.2024.3453992","DOIUrl":"10.1109/TNSM.2024.3453992","url":null,"abstract":"The Software-Defined Mobile Network (SDMN), Multi-Access Edge Computing (MEC), Cloud RAN (C-RAN), and Network Slicing are the promising solutions that have been defined for the next generation of the wireless mobile networks in order to fulfill the increasing Quality of Experience (QoE) demand of the mobile users and the Quality of Service (QoS) concerns of high-performance, innovative services. In today’s complex telecommunications network, coupled with continuous traffic growth, and users’ demand for higher speeds, it is vital for mobile operators to allocate their available resources efficiently. This paper focuses on the joint resource allocation problem of delivering adaptive video streams to users located in different slices of a wireless network enabled by MEC, SDMN, and C-RAN technologies. It proposes a novel Cross-Layer QoE-Driven Bitrate Allocation (CLQDBA) algorithm, that aims to improve system utilization by using information from the higher layers regarding traffic patterns and desired video quality of HTTP Adaptive Streaming (HAS) users. The mixed-integer nonlinear program is formulated, taking into account network slice requirements, radio resource limitations, storage and transcoding capacity of MEC servers, and users’ quality of experience. CLQDBA is a low complexity greedy-based algorithm aims to maximize users’ quality of experience (QoE) and minimize the deviation between the achievable throughput at the MAC-layer for users and the value of allocated bit rates for video frames at the application layer. The simulation result shows that compared to the baseline scheme, our introduced algorithm, on average, achieves a 15% higher system utilization, 17% higher video quality, and 13% improvement of Jain’s Fairness index for HAS users.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6857-6874"},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ANDE: Detect the Anonymity Web Traffic With Comprehensive Model ANDE：利用综合模型检测匿名网络流量

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-03 DOI: 10.1109/TNSM.2024.3453917

Yunlong Deng;Tao Peng;Bangchao Wang;Gan Wu

{"title":"ANDE: Detect the Anonymity Web Traffic With Comprehensive Model","authors":"Yunlong Deng;Tao Peng;Bangchao Wang;Gan Wu","doi":"10.1109/TNSM.2024.3453917","DOIUrl":"10.1109/TNSM.2024.3453917","url":null,"abstract":"The escalating growth of network technology and users poses critical challenges to network security. This paper introduces ANDE, a novel framework designed to enhance the classification accuracy of anonymity networks. ANDE incorporates both raw data features and statistical features extracted from network traffic. Raw data features are transformed into images, enabling recognition and classification using robust image domain models. ANDE combines an enhanced Squeeze-and-Excitation (SE) ResNet with Multilayer Perceptrons (MLP), facilitating concurrent learning and classification of both feature types. Extensive experiments on two publicly available datasets demonstrate the superior performance of ANDE compared to traditional machine learning and deep learning methods. The comprehensive evaluation underscores ANDE’s effectiveness in accurately classifying network traffic within anonymity networks. Additionally, this study empirically validates the efficacy of the SE block in augmenting the classification capabilities of the proposed framework, establishing ANDE as a promising solution for network traffic classification in the realm of network security.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6924-6936"},"PeriodicalIF":4.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187269","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

Node-Oriented Slice Reconfiguration Based on Spatial and Temporal Traffic Prediction in Metro Optical Networks 基于城域光网络时空流量预测的节点导向切片重组

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-09-02 DOI: 10.1109/TNSM.2024.3453381

Bowen Bao;Hui Yang;Qiuyan Yao;Jie Zhang;Bijoy Chand Chatterjee;Eiji Oki

{"title":"Node-Oriented Slice Reconfiguration Based on Spatial and Temporal Traffic Prediction in Metro Optical Networks","authors":"Bowen Bao;Hui Yang;Qiuyan Yao;Jie Zhang;Bijoy Chand Chatterjee;Eiji Oki","doi":"10.1109/TNSM.2024.3453381","DOIUrl":"10.1109/TNSM.2024.3453381","url":null,"abstract":"Given the spring-up of diverse new applications with different requirements in metro optical networks, network slicing provides a virtual end-to-end resource connection with customized service provision. To improve the quality-of-service (QoS) of slices with long-term operation in networks, it is beneficial to reconfigure the slice adaptively, referring to the future traffic state. Considering the busy-hour Internet traffic with daily human mobility, the tidal pattern of traffic flow occurs in metro optical networks, expressing both temporal and spatial features. To achieve high QoS of slices, this paper proposes a node-oriented slice reconfiguration (NoSR) scheme to reduce the penalty of slices, where a gradient-based priority strategy is designed to reduce the penalties of slices overall penalties in reconfiguration. Besides, given that a precise traffic prediction model is essential for efficient slice reconfiguration with future traffic state, this paper presents the model combining the graph convolutional network (GCN) and gated recurrent unit (GRU) to extract the traffic features in space and time dimensions. Simulation results show that the presented GCN-GRU traffic prediction model achieves a high forecasting accuracy, and the proposed NoSR scheme efficiently reduces the penalty of slices to guarantee a high QoS in metro optical networks.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6731-6743"},"PeriodicalIF":4.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187120","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

Energy-Efficient and Latency-Aware Data Routing in Small-World Internet of Drone Networks 小型世界无人机互联网络中的高能效和延迟感知数据路由选择

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-08-30 DOI: 10.1109/TNSM.2024.3452414

Sreenivasa Reddy Yeduri;Sindhusha Jeeru;Om Jee Pandey;Linga Reddy Cenkeramaddi

{"title":"Energy-Efficient and Latency-Aware Data Routing in Small-World Internet of Drone Networks","authors":"Sreenivasa Reddy Yeduri;Sindhusha Jeeru;Om Jee Pandey;Linga Reddy Cenkeramaddi","doi":"10.1109/TNSM.2024.3452414","DOIUrl":"10.1109/TNSM.2024.3452414","url":null,"abstract":"Recently, drones have attracted considerable attention for sensing hostile areas. Multiple drones are deployed to communicate and coordinate sensing and data transfer in the Internet of Drones (IoD) network. Traditionally, multi-hop routing is employed for communication over long distances to increase the network’s lifetime. However, multi-hop routing over large-scale networks leads to energy imbalance and higher data latency. Motivated by this, in this paper, a novel framework of energy-efficient and latency-aware data routing is proposed for Small-World (SW)-IoD networks. We started with an optimization problem formulation in terms of network delay, energy consumption, and reliability. Then, the formulated mixed integer problem is solved by introducing the Small-World Characters (SWC) into the conventional IoD network to form the SW-IoD network. Here, the proposed framework introduces SWC by removing a few existing edges with the least edge weight from the traditional network and introducing the same number of long-range edges with the highest edge weight. We present the simulation results corresponding to packet delivery ratio, network lifetime, and network delay for the performance comparison of the proposed framework with state-of-the-art approaches such as the conventional SWC method, LEACH, Modified LEACH, Canonical Particle Multi-Swarm (PMS) method, and conventional shortest path routing algorithm. We also analyze the effect of the location of the ground control station, the velocity of the drones, and the different heights of layers on the performance of the proposed framework. Through experiments, the superiority of the proposed method is proven to be better when compared to other methods. Finally, the performance evaluation of the proposed model is tested on a network simulator (NS3).","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6555-6565"},"PeriodicalIF":4.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187265","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

Edge Computing and Few-Shot Learning Featured Intelligent Framework in Digital Twin Empowered Mobile Networks 数字孪生移动网络中的边缘计算和快速学习特色智能框架

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-08-30 DOI: 10.1109/TNSM.2024.3450993

Yirui Wu;Hao Cao;Yong Lai;Liang Zhao;Xiaoheng Deng;Shaohua Wan

{"title":"Edge Computing and Few-Shot Learning Featured Intelligent Framework in Digital Twin Empowered Mobile Networks","authors":"Yirui Wu;Hao Cao;Yong Lai;Liang Zhao;Xiaoheng Deng;Shaohua Wan","doi":"10.1109/TNSM.2024.3450993","DOIUrl":"10.1109/TNSM.2024.3450993","url":null,"abstract":"Digital twins (DT) and mobile networks have evolved forms of intelligence in Internet of Things (IoT). In this work, we consider a Digital Twin Mobile Network (DTMN) scenario with few multimedia samples. Facing challenges of knowledge extraction with few samples, stable interaction with dynamic changes of multimedia data, time and privacy saving in low-resource mobile network, we propose an edge computing and few-shot learning featured intelligent framework. Considering time-sensitive property of transmission and privacy risks of directly uploads in mobile network, we deploy edge computing to locally run networks for analysis, thus saving time to offload computing request and enhancing privacy by encrypting original data. Inspired by remarkable relationship representation of graphs, we build Graph Neural Network (GNN) in cloud to map physical mobile systems to virtual entities with DT, thus performing semantic inferences in cloud with few samples uploaded by edges. Occasionally, node features in GNN could converge to similar, non-discriminative embeddings, causing catastrophic unstable phenomena. An iterative reweight and drop structure (IRDS) is thus constructed in cloud, which nonetheless contributes stability with respect to edge uncertainty. As part of IRDS, a drop Edge&Node scheme is proposed to randomly remove certain nodes and edges, which not only enhances distinguished capability of graph neighbor patterns, but also offers data encryption with random strategy. We show one implementation case of image classification in social network, where experiments on public datasets show that our framework is effective with user-friendly advantages and significant intelligence.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6505-6514"},"PeriodicalIF":4.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187267","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

OptCDU: Optimizing the Computing Data Unit Size for COIN OptCDU：优化 COIN 计算数据单元大小

IF 5.3 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-08-30 DOI: 10.1109/tnsm.2024.3452485

Huanzhuo Wu, Jia He, Jiakang Weng, Giang T. Nguyen, Martin Reisslein, Frank H. P. Fitzek

引用次数: 0

GreenShield: Optimizing Firewall Configuration for Sustainable Networks 绿盾为可持续网络优化防火墙配置

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-08-30 DOI: 10.1109/TNSM.2024.3452150

Daniele Bringhenti;Fulvio Valenza

{"title":"GreenShield: Optimizing Firewall Configuration for Sustainable Networks","authors":"Daniele Bringhenti;Fulvio Valenza","doi":"10.1109/TNSM.2024.3452150","DOIUrl":"10.1109/TNSM.2024.3452150","url":null,"abstract":"Sustainability is an increasingly critical design feature for modern computer networks. However, green objectives related to energy savings are affected by the application of approximate cybersecurity management techniques. In particular, their impact is evident in distributed firewall configuration, where traditional manual approaches create redundant architectures, leading to avoidable power consumption. This issue has not been addressed by the approaches proposed in literature to automate firewall configuration so far, because their optimization is not focused on network sustainability. Therefore, this paper presents GreenShield as a possible solution that combines security and green-oriented optimization for firewall configuration. Specifically, GreenShield minimizes the power consumption related to firewalls activated in the network while ensuring that the security requested by the network administrator is guaranteed, and the one due to traffic processing by making firewalls to block undesired traffic as near as possible to the sources. The framework implementing GreenShield has undergone experimental tests to assess the provided optimization and its scalability performance.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6909-6923"},"PeriodicalIF":4.7,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10660559","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multipartite Entanglement Distribution in the Quantum Internet: Knowing When to Stop! 量子互联网中的多方纠缠分发：知止！

IF 5.3 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-08-30 DOI: 10.1109/tnsm.2024.3452326

Angela Sara Cacciapuoti, Jessica Illiano, Michele Viscardi, Marcello Caleffi

引用次数: 0