IEEE Transactions on Network and Service Management最新文献

{"title":"Explainable and Energy-Efficient Selective Ensemble Learning in Mobile Edge Computing Systems","authors":"Lei Feng;Chaorui Liao;Yingji Shi;Fanqin Zhou","doi":"10.1109/TNSM.2025.3539830","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3539830","url":null,"abstract":"Explainable ensemble learning combines explainable artificial intelligence (XAI) and ensemble learning (EL) to solve the closed-box problem of EL and provide a clear and transparent explanation of the decision-making process in the model. As a distributed machine learning architecture, EL deploys base learners trained with local data at edge node and infers on target tasks, then combines the inference results of the participating base learners. However, selecting all base learners into EL may result in wasting more computing resources and not obtain better performance. To address this issue, we put forward the definition of confidence level (ConfLevel) on the basis of XAI and verify its effectiveness as the metric of selecting the base learner. Then, we take the joint optimization model of considering high ConfLevel and low computing power to determine the participating base learners for selective ensemble learning (SEL). Due to the non-convex and combinatorial nature of the problem, we propose a node selection and power control algorithm on the premise of Benders’ Decomposition (referred to BD-NSPC) to obtain the global optimal solution efficiently. In addition, simulation results show that BD-NSPC consumes about 30% less energy per EN on average and improves accuracy by 1-2% compared to other SEL algorithms. Besides, compared with federated learning (FL) framework, BD-NSPC reduces the energy consumption by about 25% and the latency by about 28%, achieving comparable accuracy in the edge computing system.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1744-1759"},"PeriodicalIF":4.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860752","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}

{"title":"SINR-Delay Constrained Node Localization in RIS-Assisted Time-Varying IoT Networks Using ML Frameworks","authors":"Vikash Kumar Bhardwaj;Gagan Mundada;Omm Prakash Sahoo;Mahendra Kumar Shukla;Om Jee Pandey","doi":"10.1109/TNSM.2025.3539711","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3539711","url":null,"abstract":"Node localization in time-varying Internet of Things (IoT) networks is an essential problem due to increased delay and poor Signal-to-Interference plus Noise Ratio (SINR) at the Base Station (BS). To improve the received signal strength at the BS, Reconfigurable Intelligent Surface (RIS) has recently been used between transmitter and receiver. Additionally, novel phase prediction methods and optimal weight assignment frameworks have been proposed over RIS and BSs, respectively. Nevertheless, these methods suffer from poor performance due to their heuristic approach, resulting in more time consumption and poor SINR. Motivated by the aforementioned challenges, we propose a novel node localization method over a RIS-assisted time-varying IoT network using Machine Learning (ML) frameworks in this work. Firstly, the method computes the optimal phase configuration over the RIS corresponding to each element using coeff2phaseNN, which has been trained on channel coefficients among the transmitter, receiver, and RIS. Subsequently, the weight of the individual antenna element at the BS is optimized using the proposed VectorSync model. The results confirm that the coeff2phaseNN method demonstrates a reduction of 89.79% in total MSE loss compared to the Artificial Neural Network-RIS (ANN-RIS) method. Additionally, it demonstrates a 71.04% reduction in the absolute RIS phase prediction deviation from the optimal phase compared to the ANN-RIS method. Moreover, the proposed VectorSync method attains a 79.28% and 92.29% reduction in time required for optimal weight assignment compared to the Bartlett and Capon methods, respectively. Finally, the Localization Error(LR) using the proposed method is compared to conventional methods in a time-varying experimental scenario and found to be the minimum, i.e., 6.156%.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1544-1557"},"PeriodicalIF":4.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871090","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}

{"title":"New Offloading Method of Computing Task Based on Gray Wolf Hunting Optimization Mechanism for the IOV","authors":"Jie Zhang;De-Gan Zhang;Meng Qiao;E. Hong-Lin;Ting Zhang;Ping Zhang","doi":"10.1109/TNSM.2025.3539865","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3539865","url":null,"abstract":"Task offloading, as an effective solution, provides low latency and sufficient computing resources for mobile users in the network. However, how to reasonably offload to reduce system overhead is a challenging issue today. This article takes user terminals, edge servers, and idle vehicles with resources as the network structure, and is inspired by the highly social nature of the gray wolf pack. It proposes a new offloading method of edge computing task based on hunting optimizing mechanism of gray wolf for the Internet of Vehicle (IOV). Firstly, an adaptive weight factor is proposed to balance the weight ratio of delay and energy consumption in the system cost under the constraints of delay and energy consumption. With delay and computing resources of vehicles and servers as constraints, a multi constraint minimization system cost problem is proposed. Secondly, the hunting process of the gray Wolf optimization algorithm is used to find the optimal solution of the unloading scheme, The Levy flight strategy was added to enhance the global search ability of the algorithm, and a dynamic weight strategy was introduced to improve the convergence performance of the algorithm. Finally, the improved gray Wolf optimization algorithm was used to solve the optimal unloading plan and minimum cost. The simulation results show that compared with traditional gray Wolf optimization algorithm offloading schemes, the proposed scheme in this paper requires lower system costs.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 3","pages":"2264-2277"},"PeriodicalIF":4.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232147","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}

{"title":"Provisioning of Time-Sensitive and Non-Time-Sensitive Flows With Assured Performance","authors":"Luis Velasco;Gianluca Graziadei;Sima Barzegar;Marc Ruiz","doi":"10.1109/TNSM.2025.3539697","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3539697","url":null,"abstract":"Time-Sensitive Networking (TSN) standards provide scheduling and traffic shaping mechanisms to ensure the coexistence of Time-Sensitive (TS) and non-TS traffic classes on the same network infrastructure. Nonetheless, much effort is still needed on the operation of such TSN capable network infrastructure to ensure that the required performance of the different flows, defined in terms of key performance indicators, can be met once the flows are deployed in the network. In this paper, we focus on such aspects and propose a solution involving network-wide scheduling for TS flows, as well as performance estimation for non-TS flows. Specifically, a control plane architecture especially designed for provisioning TS and non-TS flows is proposed. The architecture integrates: i) a TS Flow Scheduler Planner for defining the scheduling of requested TS flows along a path so as to meet their required performance; and ii) a Network Digital Twin to estimate the performance of requested and already established non-TS flows. Differently from standardized time-aware schedulers, per-TS flow queues are assumed so as to guarantee minimal jitter. Efficient algorithms are proposed so the provisioning of flows can be carried out with high accuracy and short time. Simulation results for heterogeneous scenarios demonstrate the feasibility and efficiency of the proposed control plane architecture, as well as point out the limitations of current time-synchronization mechanisms when high-speed interfaces are considered.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1484-1499"},"PeriodicalIF":4.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10877871","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871104","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}

{"title":"A Stackelberg Game-Based Trajectory Planning Strategy for Multi-AAVs-Assisted MEC System","authors":"Bing Shi;Zihao Chen","doi":"10.1109/TNSM.2025.3539671","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3539671","url":null,"abstract":"Nowadays, Mobile Edge Computing (MEC) has been widely deployed to enhance the computational capabilities of mobile devices. However, the geographic location of MEC servers is usually fixed. In order to provide flexible edge computing services, some works have considered integrating Autonomous aerial vehicles (AAVs) into MEC networks. In the context of AAV-assisted edge computing, there usually exist multiple AAVs and users, and each AAV may aim to maximize its profit by providing computing services, while users will decide which AAVs to utilize based on their preferences. In this context, how AAVs and users effectively plan their trajectories becomes particularly important as it will affect the profitability of AAVs and the user experience. Since the trajectories of AAVs and users are affected by each other, we model the trajectories of AAVs and users as a Stackelberg game, and then design trajectory planning strategies for users and AAVs based on Independent Proximal Policy Optimization (IPPO) and Proximal Policy Optimization (PPO) respectively, aiming to maximize AAVs’ profits while ensuring user acceptance of AAV services. Finally, we evaluate the proposed trajectory planning strategy against three typical benchmark strategies using synthetic and realistic datasets. The experimental results demonstrate that our strategy can outperform benchmark strategies in terms of AAV profit while guaranteeing users’ service experience.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1716-1726"},"PeriodicalIF":4.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860961","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}

{"title":"A Fastformer Assisted DRL Method on Energy Efficient and Interference Aware Service Provisioning","authors":"Cheng Ren;Jinsong Gao;Yu Wang;Yaxin Li","doi":"10.1109/TNSM.2025.3538105","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3538105","url":null,"abstract":"Network function virtualization (NFV) empowered by virtualization technology can achieve flexible virtual network function (VNF) placement. To improve resource utilization and energy efficiency, different VNFs tend to be co-located on common servers, which inevitably intrigues VNF performance degradation induced by hardware resource competition. The problem of energy-efficient and interference-aware service function chain (SFC) provisioning is considered in this paper and envisioned to yield minimum activated servers and maximum average throughput. It is formulated as a mixed integer linear programming (MILP) model to achieve optimal solutions. Then, a gale-shapley based offline approximation algorithm is designed through bipartite matching, to yield an SFC allocation decision in one go with proved competitive ratio. In online scenario, Transformer and its efficient model Fastformer, combined with Graph Attention Network (GAT) respectively, are introduced into deep reinforcement learning (DRL) structure for the first time to quickly and accurately abstract features of substrate network and SFC. A DRL-based Fastformer-assisted energy efficient and interference aware SFC provisioning (DRL-EI) algorithm is proposed with an elaborately designed reward function to balance energy consumption and VNF interference. Simulations indicate the gap between DRL-EI and MILP is marginal. DRL-EI outperforms state-of-art work in terms of energy consumption, VNF normalized throughput and acceptance rate.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1801-1811"},"PeriodicalIF":4.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860980","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}

{"title":"Optimization of Data and Model Transfer for Federated Learning to Manage Large-Scale Network","authors":"Kengo Tajiri;Ryoichi Kawahara","doi":"10.1109/TNSM.2025.3538156","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3538156","url":null,"abstract":"Recently, deep learning has been introduced to automate network management to reduce human costs. However, the amount of log data obtained from the large-scale network is huge, and conventional centralized deep learning faces communication and computation costs. This paper aims to reduce communication and computation costs by training deep learning models using federated learning on data generated in the network and to deploy deep learning models as soon as possible. In this scheme, data generated at each point in the network are transferred to servers in the network, and deep learning models are trained by federated learning among the servers. In this paper, we first reveal that the training time depends on the transfer routes and the destinations of data and model parameters. Then, we introduce a simultaneous optimization method for (1) to which servers each point transfers the data through which routes and (2) through which routes the servers transfer the parameters to others. In the experiments, we numerically and experimentally compared the proposed method and naive methods in complicated wired network environments. We show that the proposed method reduced the total training time by 34% to 79% compared with the naive methods.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"958-973"},"PeriodicalIF":4.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860844","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}

{"title":"DIADD: Secure Deduplication and Efficient Data Integrity Auditing With Data Dynamics for Cloud Storage","authors":"Xiangshuo Zheng;Wenting Shen;Ye Su;Yuan Gao","doi":"10.1109/TNSM.2025.3535708","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3535708","url":null,"abstract":"Data integrity auditing with data deduplication allows the cloud to store only one copy of the identical file while ensuring the integrity of outsourced data. To facilitate flexible updates of outsourced data, data integrity auditing schemes supporting data dynamics and deduplication have been proposed. However, existing schemes either impose significant computation and communication burden to achieve data dynamics while ensuring data integrity and deduplication, or incur substantial computation overhead during the phases of authenticator generation and auditing. To address the above problems, in this paper, we construct a secure deduplication and efficient data integrity auditing scheme with data dynamics for cloud storage (DIADD). We design a lightweight authenticator structure to produce data authenticators for data integrity auditing, which can achieve authenticator deduplication and greatly reduce the computation overhead in the authenticator generation phase. Additionally, the time-consuming operations can be eliminated in the auditing phase. To enhance the efficiency of data dynamics, we employ the multi-set hash function technology to produce the file tags. This allows data owners to compute a new file tag without needing to recover the entire original file when performing dynamic operations. Furthermore, security analysis and experimental results demonstrate that DIADD is both secure and efficient.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"299-316"},"PeriodicalIF":4.7,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621903","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}

{"title":"Encrypted Traffic Classification Through Deep Domain Adaptation Network With Smooth Characteristic Function","authors":"Van Tong;Cuong Dao;Hai-Anh Tran;Duc Tran;Huynh Thi Thanh Binh;Thang Hoang-Nam;Truong X. Tran","doi":"10.1109/TNSM.2025.3534791","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3534791","url":null,"abstract":"Encrypted network traffic classification has become a critical task with the widespread adoption of protocols such as HTTPS and QUIC. Deep learning-based methods have proven to be effective in identifying traffic patterns, even within encrypted data streams. However, these methods face significant challenges when confronted with new applications that were not part of the original training set. To address this issue, knowledge transfer from existing models is often employed to accommodate novel applications. As the complexity of network traffic increases, particularly at higher protocol layers, the transferability of learned features diminishes due to domain discrepancies. Recent studies have explored Deep Adaptation Networks (DAN) as a solution, which extends deep convolutional neural networks to better adapt to target domains by mitigating these discrepancies. Despite its potential, the computational complexity of discrepancy metrics, such as Maximum Mean Discrepancy, limits DAN’s scalability, especially when applied to large datasets. In this paper, we propose a novel DAN architecture that incorporates Smooth Characteristic Functions (SCFs), specifically SCF-unNorm (Unnormalized SCF) and SCF-pInverse (Pseudo-inverse SCF). These functions are designed to enhance feature transferability in task-specific layers, effectively addressing the limitations posed by domain discrepancies and computational complexity. The proposed mechanism provides a means to efficiently handle situations with limited labeled data or entirely unlabeled data for new applications. The aim is to limit the target error by incorporating a domain discrepancy between the source and target distributions along with the source error. Two statistics classes, SCF-unNorm and SCF-pInverse, are used to minimize this domain discrepancy in traffic classification. The experimental results demonstrate that our proposed mechanism outperforms existing benchmarks in terms of accuracy, enabling real-time traffic classification in network systems. Specifically, we achieve up to 99% accuracy with an execution time of only three milliseconds in the considered scenarios.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"331-343"},"PeriodicalIF":4.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619081","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}

{"title":"Fast Edge Resource Scaling With Distributed DNN","authors":"Theodoros Giannakas;Dimitrios Tsilimantos;Apostolos Destounis;Thrasyvoulos Spyropoulos","doi":"10.1109/TNSM.2025.3532365","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3532365","url":null,"abstract":"Network slicing has been proposed as a paradigm for 5G+ networks. The operators slice physical resources from the edge all the way to the datacenter, and are responsible to micro-manage the allocation of these resources among tenants bound by predefined Service Level Agreements (SLAs). A key task, for which recent works have advocated the use of Deep Neural Networks (DNNs), is tracking the tenant demand and scaling its resources. Nevertheless, for the edge resources (e.g., RAN), a question arises on whether operators can: (a) scale them fast enough (often in the order of ms) and (b) afford to transmit huge amounts of data towards a remote cloud where such a DNN model might operate. We propose a Distributed DNN (DDNN) architecture for a class of such problems: a small subset of the DNN layers at the edge attempt to act as fast, standalone resource allocator; this is complemented by a mechanism to intelligently offload a percentage of (harder) decisions to additional DNN layers running at a remote cloud. To implement the offloading, we propose: (i) a Bayes-inspired method, using dropout during inference, to estimate the confidence in the local prediction; (ii) a learnable function which automatically classifies samples as “remote” (to be offloaded) or “local”. Using the public Milano dataset, we investigate how such a DDNN should be trained and operated to address (a) and (b). In some cases, our offloading methods are near-optimal, resolving up to 50% of decisions locally with little or no penalty on the allocation cost.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"557-571"},"PeriodicalIF":4.7,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621898","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}