IEEE Transactions on Network and Service Management最新文献

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DGS: An Efficient Delay-Guaranteed Scheduling Framework for Wireless Deterministic Networking DGS:无线确定性网络的高效延迟保证调度框架
IF 4.7 2区 计算机科学
IEEE Transactions on Network and Service Management Pub Date : 2024-09-09 DOI: 10.1109/TNSM.2024.3456576
Minghui Chang;Haojun Lv;Yunqi Gao;Bing Hu;Wei Wang;Ze Yang
{"title":"DGS: An Efficient Delay-Guaranteed Scheduling Framework for Wireless Deterministic Networking","authors":"Minghui Chang;Haojun Lv;Yunqi Gao;Bing Hu;Wei Wang;Ze Yang","doi":"10.1109/TNSM.2024.3456576","DOIUrl":"10.1109/TNSM.2024.3456576","url":null,"abstract":"Deterministic Networking (DetNet) aims to provide an end-to-end ultra-reliable data network with ultra-low latency and jitter. However, implementing DetNet in wireless networks, particularly in the air interface, still faces the challenge of guaranteeing bounded delay. This paper proposes a delay-guaranteed three-layer scheduling framework for DetNet, named Deterministic Guarantee Scheduling (DGS). The top layer calculates the amount of new data entering the queue in each scheduling period and timestamps the data to track its arrival time. Based on the remaining waiting time of each flow’s data volume, the middle layer proposes a scheduling algorithm based on urgency, prioritizing the scheduling of data volumes with the shortest remaining queuing time. The lower layer fine-tunes the scheduling results obtained by the middle layer for actual transmission. We implemented the DGS framework on the 5G-air-simulator platform. Simulation results demonstrate that DGS outperforms all other mechanisms by guaranteeing delay for a larger number of deterministic flows and achieving better throughput performance.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6582-6596"},"PeriodicalIF":4.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187255","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
Reliable Task Offloading in Sustainable Edge Computing with Imperfect Channel State Information 不完善信道状态信息下可持续边缘计算中的可靠任务卸载
IF 4.7 2区 计算机科学
IEEE Transactions on Network and Service Management Pub Date : 2024-09-09 DOI: 10.1109/TNSM.2024.3456568
Peng Peng;Wentai Wu;Weiwei Lin;Fan Zhang;Yongheng Liu;Keqin Li
{"title":"Reliable Task Offloading in Sustainable Edge Computing with Imperfect Channel State Information","authors":"Peng Peng;Wentai Wu;Weiwei Lin;Fan Zhang;Yongheng Liu;Keqin Li","doi":"10.1109/TNSM.2024.3456568","DOIUrl":"10.1109/TNSM.2024.3456568","url":null,"abstract":"As a promising paradigm, edge computing enhances service provisioning by offloading tasks to powerful servers at the network edge. Meanwhile, Non-Orthogonal Multiple Access (NOMA) and renewable energy sources are increasingly adopted for spectral efficiency and carbon footprint reduction. However, these new techniques inevitably introduce reliability risks to the edge system generally because of i) imperfect Channel State Information (CSI), which can misguide offloading decisions and cause transmission outages, and ii) unstable renewable energy supply, which complicates device availability. To tackle these issues, we first establish a system model that measures service reliability based on probabilistic principles for the NOMA-based edge system. As a solution, a Reliable Offloading method with Multi-Agent deep reinforcement learning (ROMA) is proposed. In ROMA, we first reformulate the reliability-critical constraint into an long-term optimization problem via Lyapunov optimization. We discretize the hybrid action space and convert the resource allocation on edge servers into a 0-1 knapsack problem. The optimization problem is then formulated as a Partially Observable Markov Decision Process (POMDP) and addressed by multi-agent proximal policy optimization (PPO). Experimental evaluations demonstrate the superiority of ROMA over existing methods in reducing grid energy costs and enhancing system reliability, achieving Pareto-optimal performance under various settings.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6423-6436"},"PeriodicalIF":4.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187254","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
Causal Genetic Network Anomaly Detection Method for Imbalanced Data and Information Redundancy 针对不平衡数据和信息冗余的因果遗传网络异常现象检测方法
IF 4.7 2区 计算机科学
IEEE Transactions on Network and Service Management Pub Date : 2024-09-06 DOI: 10.1109/TNSM.2024.3455768
Zengri Zeng;Xuhui Liu;Ming Dai;Jian Zheng;Xiaoheng Deng;Detian Zeng;Jie Chen
{"title":"Causal Genetic Network Anomaly Detection Method for Imbalanced Data and Information Redundancy","authors":"Zengri Zeng;Xuhui Liu;Ming Dai;Jian Zheng;Xiaoheng Deng;Detian Zeng;Jie Chen","doi":"10.1109/TNSM.2024.3455768","DOIUrl":"10.1109/TNSM.2024.3455768","url":null,"abstract":"The proliferation of Internet-connected devices and the complexity of modern network environments have led to the collection of massive and high-dimensional datasets, resulting in substantial information redundancy and sample imbalance issues. These challenges not only hinder the computational efficiency and generalizability of anomaly detection systems but also compromise their ability to detect rare attack types, posing significant security threats. To address these pressing issues, we propose a novel causal genetic network-based anomaly detection method, the CNSGA, which integrates causal inference and the nondominated sorting genetic algorithm-III (NSGA-III). The CNSGA leverages causal reasoning to exclude irrelevant information, focusing solely on the features that are causally related to the outcome labels. Simultaneously, NSGA-III iteratively eliminates redundant information and prioritizes minority samples, thereby enhancing detection performance. To quantitatively assess the improvements achieved, we introduce two indices: a detection balance index and an optimal feature subset index. These indices, along with the causal effect weights, serve as fitness metrics for iterative optimization. The optimized individuals are then selected for subsequent population generation on the basis of nondominated reference point ordering. The experimental results obtained with four real-world network attack datasets demonstrate that the CNSGA significantly outperforms existing methods in terms of overall precision, the imbalance index, and the optimal feature subset index, with maximum increases exceeding 10%, 0.5, and 50%, respectively. Notably, for the CICDDoS2019 dataset, the CNSGA requires only 16-dimensional features to effectively detect more than 70% of all sample types, including 6 more network attack sample types than the other methods detect. The significance and impact of this work encompass the ability to eliminate redundant information, increase detection rates, balance attack detection systems, and ensure stability and generalizability. The proposed CNSGA framework represents a significant step forward in developing efficient and accurate anomaly detection systems capable of defending against a wide range of cyber threats in complex network environments.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6937-6952"},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187256","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
MobiFi: Mobility-Aware Reactive and Proactive Wireless Resource Management in LiFi-WiFi Networks MobiFi:LiFi-WiFi 网络中的移动感知、反应式和主动式无线资源管理
IF 4.7 2区 计算机科学
IEEE Transactions on Network and Service Management Pub Date : 2024-09-05 DOI: 10.1109/TNSM.2024.3455105
Hansini Vijayaraghavan;Wolfgang Kellerer
{"title":"MobiFi: Mobility-Aware Reactive and Proactive Wireless Resource Management in LiFi-WiFi Networks","authors":"Hansini Vijayaraghavan;Wolfgang Kellerer","doi":"10.1109/TNSM.2024.3455105","DOIUrl":"10.1109/TNSM.2024.3455105","url":null,"abstract":"This paper presents MobiFi, a framework addressing the challenges in managing LiFi-WiFi heterogeneous networks focusing on mobility-aware resource allocation. Our contributions include introducing a centralized framework incorporating reactive and proactive strategies for resource management in mobile LiFi-only and LiFi-WiFi networks. This framework reacts to current network conditions and proactively anticipates the future, considering user positions, line-of-sight blockages, and channel quality. Recognizing the importance of long-term network performance, particularly for use cases such as video streaming, we tackle the challenge of optimal proactive resource allocation by formulating an optimization problem that integrates access point assignment and wireless resource allocation using the alpha-fairness objective over time. Our proactive strategy significantly outperforms the reactive resource allocation, ensuring 7.7% higher average rate and 63.3% higher minimum user rate for a 10-user LiFi-WiFi network. We employ sophisticated techniques, including a Branch and Bound-based Mixed-Integer solver and a low-complexity, Evolutionary Game Theory-based algorithm to achieve this. Lastly, we introduce a novel approach to simulate errors in predictive user position modeling to assess the robustness of our proactive allocation strategy against real-world uncertainties. The contributions of MobiFi advance the field of resource management in mobile LiFi-WiFi networks, enabling efficiency and reliability.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6597-6613"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187257","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
Multi-Agent DRL-Based Two-Timescale Resource Allocation for Network Slicing in V2X Communications 基于 DRL 的多代理双时标资源分配用于 V2X 通信中的网络分片
IF 4.7 2区 计算机科学
IEEE Transactions on Network and Service Management Pub Date : 2024-09-05 DOI: 10.1109/TNSM.2024.3454758
Binbin Lu;Yuan Wu;Liping Qian;Sheng Zhou;Haixia Zhang;Rongxing Lu
{"title":"Multi-Agent DRL-Based Two-Timescale Resource Allocation for Network Slicing in V2X Communications","authors":"Binbin Lu;Yuan Wu;Liping Qian;Sheng Zhou;Haixia Zhang;Rongxing Lu","doi":"10.1109/TNSM.2024.3454758","DOIUrl":"10.1109/TNSM.2024.3454758","url":null,"abstract":"Network slicing has been envisioned to play a crucial role in supporting various vehicular applications with diverse performance requirements in dynamic Vehicle-to-Everything (V2X) communications systems. However, time-varying Service Level Agreements (SLAs) of slices and fast-changing network topologies in V2X scenarios may introduce new challenges for enabling efficient inter-slice resource provisioning to guarantee the Quality of Service (QoS) while avoiding both resource over-provisioning and under-provisioning. Moreover, the conventional centralized resource allocation schemes requiring global slice information may degrade the data privacy provided by dedicated resource provisioning. To address these challenges, in this paper, we propose a two-timescale resource management mechanism for providing diverse V2X slices with customized resources. In the long timescale, we propose a Proximal Policy Optimization-based multi-agent deep reinforcement learning algorithm for dynamically allocating bandwidth resources to different slices for guaranteeing their SLAs. Under the coordination of agents, each agent only observes its partial state space rather than the global information to adjust the resource requests, which can enhance the privacy protection. Moreover, an expert demonstration mechanism is proposed to guide the action policy for reducing the invalid action exploration and accelerating the convergence of agents. In the short-term time slot, with our proposed Cross Entropy and Successive Convex Approximation algorithm, each slice allocates its available physical resource blocks and optimizes its transmit power to meet the QoS. Simulation results show our proposed two-timescale resource allocation scheme for network slicing can achieve maximum 8.4% performance gains in terms of spectral efficiency while guaranteeing the QoS requirements of users compared to the baseline approaches.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6744-6758"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187259","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
A Deep Learning System for Detecting IoT Web Attacks With a Joint Embedded Prediction Architecture (JEPA) 利用联合嵌入式预测架构 (JEPA) 检测物联网网络攻击的深度学习系统
IF 4.7 2区 计算机科学
IEEE Transactions on Network and Service Management Pub Date : 2024-09-05 DOI: 10.1109/TNSM.2024.3454777
Yufei An;F. Richard Yu;Ying He;Jianqiang Li;Jianyong Chen;Victor C. M. Leung
{"title":"A Deep Learning System for Detecting IoT Web Attacks With a Joint Embedded Prediction Architecture (JEPA)","authors":"Yufei An;F. Richard Yu;Ying He;Jianqiang Li;Jianyong Chen;Victor C. M. Leung","doi":"10.1109/TNSM.2024.3454777","DOIUrl":"10.1109/TNSM.2024.3454777","url":null,"abstract":"The advancement of Internet of Things (IoT) technology has significantly transformed the dynamic between humans and devices, as well as device-to-device interactions. This paradigm shift has led to profound changes in human lifestyles and production processes. Through the interconnectedness of numerous sensors and controllers via networks, the IoT facilitates the seamless integration of humans with diverse devices, leading to substantial economic advantages. Nevertheless, the burgeoning IoT industry and the rapid proliferation of various IoT devices have also introduced a multitude of security vulnerabilities. Cyber attackers frequently exploit cyber attacks to compromise IoT devices, jeopardizing user privacy and property security, thereby posing a grave menace to the overall security of the IoT ecosystem. In this paper, we propose a novel IoT Web attack detection system based on a joint embedded prediction architecture (JEPA), which effectively alleviates the security issues faced by IoT. It can obtain high-level semantic features in IoT traffic data through non-generative self-supervised learning. These features can more effectively distinguish normal data from attack data and help improve the overall detection performance of the system. Moreover, we propose a feature interaction module based on a dual-branch network, which effectively fuses low-level features and high-level features, and comprehensively aggregates global features and local features. Simulation results on multiple datasets show that our proposed system has better detection performance and robustness.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6885-6898"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187260","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
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
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