Last updated: 2025-02-08 04:03 UTC
All documents
Number of pages: 134
Author(s) | Title | Year | Publication | Keywords | ||
---|---|---|---|---|---|---|
Subhrajit Barick, Chetna Singhal | UAV-Assisted MEC Architecture for Collaborative Task Offloading in Urban IoT Environment | 2025 | Early Access | Internet of Things Autonomous aerial vehicles Urban areas Optimization Collaboration Delays Computer architecture Quality of service Computational modeling Topology Mobile edge computing Internet of Things unmanned aerial vehicles task offloading user satisfaction service provider profit | Mobile edge computing (MEC) is a promising technology to meet the increasing demands and computing limitations of complex Internet of Things (IoT) devices. However, implementing MEC in urban environments can be challenging due to factors like high device density, complex infrastructure, and limited network coverage. Network congestion and connectivity issues can adversely affect user satisfaction. Hence, in this article, we use unmanned aerial vehicle (UAV)-assisted collaborative MEC architecture to facilitate task offloading of IoT devices in urban environments. We utilize the combined capabilities of UAVs and ground edge servers (ESs) to maximize user satisfaction and thereby also maximize the service provider’s (SP) profit. We design IoT task-offloading as joint IoT-UAV-ES association and UAV-network topology optimization problem. Due to NP-hard nature, we break the problem into two subproblems: offload strategy optimization and UAV topology optimization. We develop a Three-sided Matching with Size and Cyclic preference (TMSC) based task offloading algorithm to find stable association between IoTs, UAVs, and ESs to achieve system objective. We also propose a K-means based iterative algorithm to decide the minimum number of UAVs and their positions to provide offloading services to maximum IoTs in the system. Finally, we demonstrate the efficacy of the proposed task offloading scheme over benchmark schemes through simulation-based evaluation. The proposed scheme outperforms by 19%, 12%, and 25% on average in terms of percentage of served IoTs, average user satisfaction, and SP profit, respectively, with 25% lesser UAVs, making it an effective solution to support IoT task requirements in urban environments using UAV-assisted MEC architecture. | 10.1109/TNSM.2025.3535094 |
Eiji Oki, Ryotaro Taniguchi, Kazuya Anazawa, Takeru Inoue | Design of Multiple-Plane Twisted and Folded Clos Network Guaranteeing Admissible Blocking Probability | 2025 | Early Access | Optical switches Switching circuits Optical fiber networks Optical packet switching Optical transmitters Optical design Integrated circuit modeling Fabrics Capacity planning Technological innovation Clos network optical circuit switching data center switching capacity blocking probability | Future advancements in data centers are anticipated to incorporate advanced circuit switching technologies, especially optical switching, which achieve high transmission capacity and energy efficiency. Previous studies addressed a Clos-network design problem to guarantee an admissible blocking probability to maximize the switching capacity, which is defined by the number of terminals connected to the network. However, as the number of available N×N switches increases, the switching capacity no longer increases due to the switch port limitation. This paper proposes a design of a multiple-plane twisted-folded (TF) Clos network, named MP-TF, to enhance the switching capacity, which is limited by the original TF-Clos, by guaranteeing an admissible blocking probability. MP-TF consists of identical M TF-Clos planes and pairs of a 1×M selector and an M×1 selector, each pair of which is associated with a transmitter and receiver pair. We formulate a design model of MP-TF as an optimization problem to maximize the switching capacity. We introduce connection admission control in MP-TF, named MP-CAC. We derive the theorem that the MP-TF design model using MP-CAC guarantees the admissible blocking probability. Numerical results observe that MP-TF increases the switching capacity as the number of TF-Clos planes when available N×N switches are sufficient; for example, with seven planes, the switching capacity is 1.97 times larger than that of one plane, given a request active probability of 0.6 and an admissible blocking probability of 0.01. We find that the computation time for MP-TF diminishes with an increase in the number of TF-Clos planes. Designing MP-TF is similar to designing a single TF-Clos plane, differing mainly in the handling of connection admission control. With a larger number of N×N switches, MP-TF enables the design of a smaller TF-Clos plane. We provide the analyses of optical power management and network cost of MP-TF. | 10.1109/TNSM.2025.3539907 |
Luis Velasco, Gianluca Graziadei, Sima Barzegar, Marc Ruiz | Provisioning of Time-Sensitive and non-Time-Sensitive Flows With Assured Performance | 2025 | Early Access | Quality of service Delays Resource management Job shop scheduling Jitter Computer architecture Full-duplex system Digital twins Wireless fidelity Standards Time-Sensitive Networking Network Operation Time-aware scheduling Network Digital Twin | 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. | 10.1109/TNSM.2025.3539697 |
Bing Shi, Zihao Chen | A Stackelberg Game Based Trajectory Planning Strategy for Multi-UAVs-Assisted MEC System | 2025 | Early Access | Autonomous aerial vehicles Trajectory Trajectory planning Games Costs Computational modeling Resource management Planning Benchmark testing Time-frequency analysis Mobile Edge Computing Unmanned Aerial Vehicle Stackelberg Game Deep Reinforcement Learning | 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 Unmanned Aerial Vehicles (UAVs) into MEC networks. In the context of UAV-assisted edge computing, there usually exist multiple UAVs and users, and each UAV may aim to maximize its profit by providing computing services, while users will decide which UAVs to utilize based on their preferences. In this context, how UAVs and users effectively plan their trajectories becomes particularly important as it will affect the profitability of UAVs and the user experience. Since the trajectories of UAVs and users are affected by each other, we model the trajectories of UAVs and users as a Stackelberg game, and then design trajectory planning strategies for users and UAVs based on Independent Proximal Policy Optimization (IPPO) and Proximal Policy Optimization (PPO) respectively, aiming to maximize UAVs’ profits while ensuring user acceptance of UAV 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 UAV profit while guaranteeing users’ service experience. | 10.1109/TNSM.2025.3539671 |
Lei Feng, Chaorui Liao, Yingji Shi, Fanqin Zhou | Explainable and Energy-Efficient Selective Ensemble Learning in Mobile Edge Computing Systems | 2025 | Early Access | Computational modeling Machine learning algorithms Machine learning Ensemble learning Training Predictive models Explainable AI Prediction algorithms Energy consumption Accuracy Edge computing Ensemble learning Ensemble selection Explainable artificial intelligence | Explainable ensemble learning combines explainable artificial intelligence (XAI) and ensemble learning (EL) to solve the black-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. | 10.1109/TNSM.2025.3539830 |
Yu-Fang Chen, Frank Yeong-Sung Lin, Sheng-Yung Hsu, Tzu-Lung Sun, Yennun Huang, Chiu-Han Hsiao | Adaptive Traffic Control: OpenFlow-Based Prioritization Strategies for Achieving High Quality of Service in Software-Defined Networking | 2025 | Early Access | Resource management Quality of service Delays Protocols Heuristic algorithms Dynamic scheduling 6G mobile communication Servers IP networks Optimization Lagrangian Relaxation (LR) Network Management OpenFlow Priority Scheduling Quality of Service (QoS) Resource Allocation Software-Defined Networking (SDN) | This paper tackles key challenges in Software-Defined Networking (SDN) by proposing a novel approach for optimizing resource allocation and dynamic priority assignment using OpenFlows priority field. The proposed Lagrangian relaxation (LR)-based algorithms significantly reduces network delay, achieving performance management with dynamic priority levels while demonstrating adaptability and efficiency in a sliced network. The algorithms’ effectiveness were validated through computational experiments, highlighting the strong potential for QoS management across diverse industries. Compared to the Same Priority baseline, the proposed methods: RPA, AP–1, and AP–2, exhibited notable performance improvements, particularly under strict delay constraints. For future applications, the study recommends expanding the algorithm to handle larger networks, integrating it with artificial intelligence technologies for proactive resource optimization. Additionally, the proposed methods lay a solid foundation for addressing the unique demands of 6G networks, particularly in areas such as base station mobility (Low-Earth Orbit, LEO), ultra-low latency, and multi-path transmission strategies. | 10.1109/TNSM.2025.3540012 |
Kengo Tajiri, Ryoichi Kawahara | Optimization of Data and Model Transfer for Federated Learning to Manage Large-Scale Network | 2025 | Early Access | Servers Training Data models Federated learning Deep learning Bandwidth Costs Convergence Accuracy Numerical models network management federated learning routing optimization | 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. | 10.1109/TNSM.2025.3538156 |
Cheng Ren, Jinsong Gao, Yu Wang, Yaxin Li | A Fastformer Assisted DRL Method on Energy Efficient and Interference Aware Service Provisioning | 2025 | Early Access | Interference Throughput Servers Energy consumption Resource management Adaptation models Transformers Logic gates Upper bound Training Network function virtualization deep reinforcement learning Fastformer virtual network function interference | 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. | 10.1109/TNSM.2025.3538105 |
Xiangshuo Zheng, Wenting Shen, Ye Su, Yuan Gao | DIADD: Secure Deduplication and Efficient Data Integrity Auditing with Data Dynamics for Cloud Storage | 2025 | Early Access | Cloud computing Data integrity Protocols Data privacy Encryption Indexes Costs Servers History Heart rate Cloud storage Data integrity auditing Data deduplication Data dynamics | 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. | 10.1109/TNSM.2025.3535708 |
Zhenyu Fu, Juan Liu, Yuyi Mao, Long Qu, Lingfu Xie, Xijun Wang | Energy-Efficient UAV-Assisted Federated Learning: Trajectory Optimization, Device Scheduling, and Resource Management | 2025 | Early Access | Autonomous aerial vehicles Energy consumption Convergence Accuracy Trajectory Resource management Optimization Scheduling Servers Training Federated learning energy efficiency device scheduling unmanned aerial vehicle (UAV) trajectory optimization | The emergence of intelligent mobile technologies and the widespread adoption of 5G wireless networks have made Federated Learning (FL) a promising method for protecting privacy during distributed model training. However, traditional FL frameworks rely on static aggregators such as base stations, encountering obstacles such as increased energy demands, frequent disconnections, and poor model performance. To address these issues, this paper investigates an innovative Unmanned Aerial Vehicle (UAV)-assisted FL framework, aiming to utilize UAVs as mobile model aggregators to collaborate with devices in training models, while minimizing the total energy consumption of devices and ensuring that FL can achieve the target model accuracy. By adopting the Distributed Approximate NEwton (DANE) method for local optimization, we analyze the convergence of FL and derive device scheduling constraints that aid in convergence. Accordingly, we formulate a problem of minimizing the total energy consumption of devices, integrating a constraint on global model accuracy, and jointly optimizing the UAV trajectory, device scheduling, bandwidth allocation, time slot lengths, as well as the uplink transmission power, CPU frequency, and local convergence accuracy. Then, we decompose this non-convex optimization problem into three subproblems and propose an iterative algorithm based on Block Coordinate Descent (BCD) with convergence guarantee. Simulation results indicate that, compared with various benchmark methods, our proposed UAV-assisted FL framework significantly reduces the total energy consumption of devices and achieves an improved trade-off between energy and convergence accuracy. | 10.1109/TNSM.2025.3531237 |
Van Tong, Cuong Dao, Hai Anh Tran, Duc Tran, Huynh Thi Thanh Binh, Nam-Thang Hoang, Truong X. Tran | Encrypted Traffic Classification Through Deep Domain Adaptation Network With Smooth Characteristic Function | 2025 | Early Access | Cryptography Training Telecommunication traffic Payloads Classification algorithms Virtual private networks Feature extraction Adaptation models Accuracy Home automation Traffic Classification Deep Learning Deep Adaptation Network Multi-kernel Maximum Mean Discrepancy and Smooth Characteristic Function Test | 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. | 10.1109/TNSM.2025.3534791 |
Xiaonan Wang, Yajing Song | Personalized Preference and Social Attribute Based Data Sharing for Information-Centric IoT | 2025 | Early Access | Internet of Things Smart devices Performance evaluation Data models Time factors Delays Backhaul networks Spread spectrum communication Relays Proposals Information-centric Internet of Things personalized preference social attribute data sharing in-network caching | With the rapid increase in the number of smart devices connected to the Internet of Things (IoT), network traffic has imposed serious overload on backhaul networks and led to network congestion. Data sharing among IoT devices through multi-hop communication between smart devices is expected to ease increasing pressure of backhaul traffic. In this paper, we propose a personalized preference and social attribute based data sharing framework for information-centric IoT, aiming to improve success rates of data sharing among IoT devices and reduce data sharing delays. This framework proposes personalized preferences and social attributes to reduce data response time and avoid data delivery failures caused by obsolete FIB and broken reverse paths. The experiment results justify the advantages of the proposed framework in terms of data sharing success rates and delays. | 10.1109/TNSM.2025.3529291 |
Yang Gao, Jun Tao, Zuyan Wang, Yifan Xu | Analytical Scheduling for Selfishness Detection in OppNets Based on Differential Game | 2025 | Early Access | Routing Costs Differential games Nash equilibrium Scheduling Degradation Analytical models Routing protocols Relays Numerical models Opportunistic Network Selfishness Detection Differential Game Pontryagin’s Maximum Principle | Selfishness detection offers an effective way to mitigate the routing performance degradation caused by selfish behaviors in Opportunistic Networks but leads to extra network traffic and computational burden. Most existing efforts focus on designing the selfishness detection scheme by exploiting the behavioral records of nodes. In this paper, we investigate the scheduling strategy of selfishness detection during the message lifespan with the game theory. Specifically, the Long-term Selfishness Detection Game (LSDG) is proposed based on the differential game and the payoff in the integral form. LSDG formulates the selfishness detection and the node’s selfishness with the Ordinary Differential Equations (ODEs). Then, we prove the existence of the Nash equilibrium in LSDG and deduce the necessary conditions of the equilibrium strategy based on Pontryagin’s maximum principle. The recursion-based algorithm is designed in this paper to compute the numerical solution of the equilibrium strategy via Euler’s method. Both the soundness of our modeling approach and solution properties are verified by extensive experiments. The simulations also show that the obtained solution can achieve the Nash equilibrium, where neither the source node nor relay nodes can benefit more by solely changing their own strategies. | 10.1109/TNSM.2025.3535082 |
Theodoros Giannakas, Dimitrios Tsilimantos, Apostolos Destounis, Thrasyvoulos Spyropoulos | Fast Edge Resource Scaling with Distributed DNN | 2025 | Early Access | Resource management Artificial neural networks Costs Uncertainty Computer architecture Training Service level agreements Image edge detection Data models 5G mobile communication Distributed inference slicing machine learning methodologies resource provisioning | 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. | 10.1109/TNSM.2025.3532365 |
Abdul Basit, Muddasir Rahim, Tri Nhu Do, Nadir Adam, Georges Kaddoum | DRL-Based Maximization of the Sum Cross-Layer Achievable Rate for Networks Under Jamming | 2025 | Early Access | Jamming Wireless networks Schedules Interference Intercell interference Uplink Vectors Time division multiple access Residual neural networks Reliability Deep reinforcement learning medium access control jamming attacks residual neural network | In quasi-static wireless networks characterized by infrequent changes in the transmission schedules of user equipment (UE), malicious jammers can easily deteriorate network performance. Accordingly, a key challenge in these networks is managing channel access amidst jammers and under dynamic channel conditions. In this context, we propose a robust learning-based mechanism for channel access in multi-cell quasi-static networks under jamming. The network comprises multiple legitimate UEs, including predefined UEs (pUEs) with stochastic predefined schedules and an intelligent UE (iUE) with an undefined transmission schedule, all transmitting over a shared, time-varying uplink channel. Jammers transmit unwanted packets to disturb the pUEs’ and the iUE’s communication. The iUE’s learning process is based on the deep reinforcement learning (DRL) framework, utilizing a residual network (ResNet)-based deep Q-Network (DQN). To coexist in the network and maximize the network’s sum cross-layer achievable rate (SCLAR), the iUE must learn the unknown network dynamics while concurrently adapting to dynamic channel conditions. Our simulation results reveal that, with properly defined state space, action space, and rewards in DRL, the iUE can effectively coexist in the network, maximizing channel utilization and the network’s SCLAR by judiciously selecting transmission time slots and thus avoiding collisions and jamming. | 10.1109/TNSM.2025.3534028 |
Tianhao Ouyang, Haipeng Yao, Wenji He, Tianle Mai, Fu Wang, F. Richard Yu | Self-Adaptive Dynamic In-Band Network Telemetry Orchestration for Balancing Accuracy and Stability | 2025 | Early Access | Telemetry Switches Stability analysis Accuracy Optimization Bandwidth Real-time systems Data collection Stochastic processes Monitoring In-band network telemetry (INT) INT orchestration Network stability Lyapunov optimization Surrogate Lagrangian relaxation | In-band network telemetry (INT) is an emerging network measurement technique that offers real-time and fine-grained visualization capabilities for networks. However, the utilization of INT for network measurement introduces additional overheads to the network. The process of data collection consumes extra bandwidth resources, and adjustments to the data collection scheme can impact network stability. Additionally, the INT orchestration scheme requires adaptation to dynamics in the network to improve measurement accuracy. Therefore, striking a balance between accuracy and stability becomes a critical problem. In this paper, our focus lies in the trade-off between measurement accuracy and network stability. We consider the long-term orchestration of multiple telemetry tasks, rationally deploying distinct telemetry tasks to different application flows. To address the challenge, we propose a self-adaptive Dynamic INT Orchestration scheme, D-INTO. Specifically, we formulate a stochastic optimization problem for dynamic INT orchestration. Then we employ Lyapunov optimization to decouple the stochastic optimization problem and use surrogate Lagrangian relaxation to construct a polynomial-time approximation algorithm. Theoretical analysis and experimental results demonstrate that our proposed D-INTO outperforms existing schemes in terms of adaptability to the network dynamics. | 10.1109/TNSM.2025.3530432 |
XiaoBo Fan | Path Selection via Mutual Coherence Optimization in Network Monitoring | 2025 | Early Access | Monitoring Routing Tomography Probes Inference algorithms Coherence Costs Vectors Topology Network topology Network monitoring path selection matrix design mutual coherence | Periodically monitoring the state of internal links is important for network diagnosis. One of the major problems in tomography-based network monitoring is to select which paths to measure. In this paper, we propose a new path selection scheme by means of optimizing the mutual coherence of the routing matrix. The proposed scheme exploits the sparse characteristic of link status and follows the matrix design methods in sparse signal theory. By picking the paths with the minimum average mutual coherence, we can recover a sparse vector more accurately. The effectiveness of the proposed algorithms is analyzed theoretically. We conduct simulation experiments of delay estimation on both synthetic and real topologies. The results demonstrate that our scheme can select the most useful paths for network tomography with lowest cost in an acceptable time. | 10.1109/TNSM.2025.3532343 |
Tamás Lévai, Balázs Vass, Gábor Rétvári | Programmable Real-Time Scheduling of Disaggregated Network Functions: A Theoretical Model | 2025 | Early Access | Real-time systems Software Switches Delays Hardware Optimal scheduling Pipelines Software algorithms Costs Telecommunications dataflow graph software switch SDN NFV | Novel telecommunication systems build on a cloudified architecture running softwarized network services as disaggregated virtual network functions (VNFs) on commercial off-the-shelf (COTS) hardware to improve costs and flexibility. Given the stringent processing deadlines of modern applications, these systems are critically dependent on a closed-loop control algorithm to orchestrate the execution of the disaggregated components. At the moment, however, the formal model for implementing such real-time control loops is mostly missing. In this paper, we introduce a new real-time VNF execution environment that runs entirely on COTS hardware. First, we define a comprehensive formal model that enables us to reason about packet processing delays across disaggregated VNF processing chains analytically. Then we integrate the model into a gradient-optimization control algorithm to provide optimal scheduling for real-time infocommunication services in a programmable way. We present experimental evidence that our model gives a proper delay estimation on a real software switch. We evaluate our control algorithm on multiple representative use cases using a software switch simulator. Our results show the algorithm drives the system to a real-time capable state in just a few control periods even in case of complex services. | 10.1109/TNSM.2025.3531989 |
Tien Van Do, Nam H. Do, Csaba Rotter, T.V. Lakshman, Csaba Biro, T. Bérczes | Properties of Horizontal Pod Autoscaling Algorithms and Application for Scaling Cloud-Native Network Functions | 2025 | Early Access | Measurement Cloud computing Clustering algorithms Prediction algorithms Containers Heuristic algorithms Software algorithms Servers Q-learning Surveys Network Functions Virtualisation Resource Management Kubernetes Horizontal Pod Autoscaling Algorithm metrics | With the growing adoption of network function virtualization, telco core network elements and network functions will increasingly be designed and deployed as cloud-native application instances. To ensure the efficient use of virtualised resources and meet diverse requirements for quality of services a resource scaling algorithm is used to scale the the number of application instances up or down depending on variations in offered traffic from customers. Most of the observed performance metrics for a service are a function of the current customer traffic and the current number of application instances providing the service. The ubiquitous use of Kubernetes, the popular open-source framework for deployment and management of cloud-native functions, has resulted in variants of the Kubernetes Horizontal Pod Autoscaling (HPA) algorithm being widely used to change the number of application instances providing network functions as traffic demands vary. This change is done by determining whether a selected performance metric of interest is outside a range set by two input parameters (the desired metric value and the tolerance parameter). In this paper, we invesitigate the characteristics of the HPA algorithms and prove that there are only a finite number of intervals for its tolerance parametere. Further any choice of the tolerance parameter from each interval leads to similar computational decisions on the recommended number of application instances. As a consequence, the number of parameter setting choices is finite due to the rule that the desired metric value can only be an integer in specific ranges. Additionally, we investigate the use of HPA for scaling application instances that provide session-based services and establish lower and the upper bounds for performance of the HPA scaling algorithms in this scenario. Our contributions can help operators find appropriate parameter settings efficiently -administrators of Kubernetes clusters only need to select parameters from a limited and finite number of choices (instead of infinite) for scaling cloud-native applications. | 10.1109/TNSM.2025.3532121 |
Lihui Zhang, Gang Sun, Rulin Liu, Wei Quan, Hongfang Yu, Dusit Niyato | Priority-Dominated Traffic Scheduling Enabled ATS in Time-Sensitive Networking | 2025 | Early Access | Delays Job shop scheduling Scalability Heuristic algorithms Dynamic scheduling Costs Quality of service Synchronization Scheduling algorithms Network topology Time-Sensitive Networking Traffic Scheduling Asynchronous Traffic Shaping QoS High Applicability | Time-Sensitive Networking (TSN) employs shaping mechanisms such as Time-Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF), which depend heavily on precise time synchronization and complex Gate Control Lists (GCL) configurations, limiting their effectiveness in large-scale mixed traffic networks like those in vehicular systems. In response, IEEE 802.1Qcr protocol introduces the Asynchronous Traffic Shaping (ATS) mechanism, based on Urgency-Based Schedulers (UBS), to asynchronously address diverse traffic needs and ensure low and predictable latency. Nonetheless, no traffic scheduling algorithm exists that can be directly applied to ATS shapers in generic large-scale traffic scenarios to solve for fixed end-to-end (E2E) delay constraints and the number of priority queues. In this paper, we propose an urgency-based fast flow scheduling algorithm (UBFS) to address the issue. UBFS leverages domain-specific optimizing strategies with a focus on traffic delay urgency inspired by greedy algorithm for priority allocation across hops and flows, complemented by preprocessing for scenario solvability and dynamic verification to ensure scheduling feasibility. We benchmark UBFS against the method with both scalability and solution quality in typical network topology and demonstrate that UBFS achieves more rapid scheduling within seconds across linear, ring, and star topologies. Notably, UBFS significantly outperforms the baseline algorithm in scheduling efficiency in mixed and large-scale traffic environments, scheduling a larger number of flows. UBFS also reduces time costs by 2-10 times in delay-sensitive environments and by more than 10 times in large-scale scenarios, effectively balancing time efficiency, performance and scalability, thereby enhancing its applicability in real-world industrial settings. | 10.1109/TNSM.2025.3532080 |