TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Francisco Muro, Eduardo Baena, Tomaso De Cola, Sergio Fortes, Raquel Barco	AI-Driven Optimization of Virtual Network Function Allocation in 6G Non-Terrestrial Networks	2026	Early Access	Resource management Optimization Satellites	The integration of 6G technologies into Non-Terrestrial Networks (NTNs) raises a fundamental orchestration problem: how to allocate Virtual Network Functions (VNFs) across satellite and terrestrial domains under tight onboard resource constraints and a continuously changing topology. The virtualized 6G Open Radio Access Network (O-RAN) paradigm makes it possible to run 5G software stacks on Software-Defined Radios (SDRs) based on General Purpose Processors (GPPs), but it also turns VNF placement into a high-dimensional, multi-objective decision that static heuristics and model-based formulations struggle to capture. This paper addresses that gap by introducing an AI-driven VNF allocation framework for 6G-NTN environments built on an O-RAN-based distributed architecture and orchestrated on top of Kubernetes. The VNF allocation problem is formalized for a multi-domain 6G-NTN scenario with constrained satellite resources, and a measurement-based test campaign is designed to characterize the emulated platform in terms of virtual resource utilization and end-to-end performance. The framework couples tree-based machine learning predictors with a gradient-free optimizer to reach the optimal feasible allocation, outperforming two heuristic baselines drawn from the VNF placement literature by reducing the service RTT by up to 39% and delivering up to 3× higher YouTube DL throughput with respect to the best feasible heuristic. Beyond these gains, the proposed framework establishes a measurement-driven, reproducible methodology for VNF allocation in 6GNTN scenarios, demonstrating that AI-driven orchestration can systematically uncover non-obvious resource configurations that purely analytical or static approaches consistently miss.	10.1109/TNSM.2026.3724474
Yuya Miyaoka, Masaki Inoue, Kengo Urata, Shigeaki Harada	Chat-Driven Optimal Management for Virtual Network Services	2026	Early Access	Modeling Large language models Central Processing Unit	This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based networking (IBN) methods depend on statistical language models to interpret user intent, but cannot guarantee the feasibility of generated configurations. To overcome this, we develop a two-stage framework consisting of an Interpreter, which extracts intent from natural language prompts using NLP, and an Optimizer, which computes feasible virtual machine (VM) placement and routing via integer linear programming. In particular, the Interpreter translates user chats into update directions, i.e., whether to increase, decrease, or maintain parameters such as CPU demand and latency bounds, thereby enabling iterative refinement of the network configuration. In this paper, two distinct Interpreter implementations are introduced: a Sentence-BERT model with support vector machine (SVM) classifiers and a large language model (LLM). Experiments in single-user and multi-user settings show that the framework dynamically updates VM placement and routing while preserving feasibility. The LLM-based approach achieves higher accuracy with fewer labeled samples, whereas the Sentence-BERT with SVM classifiers provides significantly lower latency suitable for real-time operation. We also compare our cascade structure method with an end-to-end LLM approach, highlighting our proposed method’s high level of reliability.	10.1109/TNSM.2026.3726950
Ryota Nakamura, Kengo Urata, Shigeaki Harada	A Virtual Network Embedding Method Enhancing Self-consumption of Renewable Energy	2026	Early Access	Timing Joining processes Real-time systems	To achieve carbon neutrality in telecommunications networks, renewable energy (RE) must be utilized more efficiently despite its inherent fluctuations. These fluctuations often cause mismatches between power generation and demand, leading to surplus power in some sites and shortages in others. To address this issue, we formulate the problem of maximizing RE use in the allocation of virtualized ICT workloads, which can be realized through techniques such as VM live migration across sites and time periods. To efficiently solve the formulated problem, we propose a heuristic search method that leverages virtualization to enable VM workload migration across sites and time periods, thereby optimizing resource allocation according to RE availability. The proposed method is designed to increase RE consumption while managing trade-offs with communication quality and equipment efficiency. Simulation results demonstrate that our approach can improve RE utilization by up to 30% while maintaining acceptable performance, even under prediction errors. These findings highlight the potential of virtualization-based control frameworks for building more sustainable and environmentally conscious telecommunications networks.	10.1109/TNSM.2026.3726977
Liang Liu, Wenbin Zhai, Feng Wang, Youwei Ding, Wanying Lu, Weizhi Meng	Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks	2026	Early Access	Labeling Modeling Internet of Things	The expansive attack surfaces and device heterogeneity of Internet of Things (IoT) networks pose significant challenges for anomaly detection. While Federated Learning (FL) enables privacy-preserving detection, existing FL methods typically assume fully labeled client data, which is unrealistic for practical IoT deployments. Resource constraints and network heterogeneity further complicate the trade-off among training efficiency, detection accuracy, and communication overhead. To address these challenges, we propose FedS3A, a Federated Semi-Supervised and Semi-Asynchronous learning framework for IoT anomaly detection. FedS3A operates in a practical disjoint semi-supervised setting where the server holds limited labeled data and clients possess extensive unlabeled data. We apply pseudo-labeling with a dynamically decaying weight to balance server-side supervised training and client-side unsupervised learning. To improve round efficiency, we introduce a semi-asynchronous model update and staleness-tolerant distribution scheme that scales client contributions to the global model based on local model staleness and participation frequency. We also adopt a group-based aggregation function to mitigate the impact of non-IID client data, and utilize sparse difference transmission to reduce communication overhead. We evaluate FedS3A on the CIC-IDS2017 and Edge-IIoTset datasets, and the results demonstrate that FedS3A consistently outperforms representative FL approaches in detection performance and round efficiency. FedS3A achieves over 98% accuracy even under non-IID settings while reducing communication costs by approximately 50%.	10.1109/TNSM.2026.3727228
Pingping Dong, Liying Chen, Xuan Yao, Kai Wang, Lianming Zhang, Jiawei Huang	Fumer: Proactive Time-Shifting for Synchronized Periodic Traffic in Distributed Training	2026	Early Access	Training Timing Modeling	The growth of distributed training models, with parameters now reaching the billion-scale, has shifted the system bottleneck from computation to communication. While Remote Direct Memory Access (RDMA) is widely deployed to improve network performance by circumventing the kernel mechanism, the synchronization-computation cycles under the synchronous parallel mode introduce a highly synchronized and periodic “on-off” bursty traffic pattern, which poses significant challenges to data center networking. Consequently, distributed training suffers from two critical bottlenecks: instantaneous congestion during communication and persistent link idleness during computation. These issues lead to severe bandwidth contention and resource underutilization, ultimately hindering overall training efficiency. To address these challenges, this paper proposes Fumer, a proactive periodic traffic optimization framework that shifts the congestion control paradigm from reactive rate adjustment to proactive time-shifting. Specifically, Fumer leverages In-band Network Telemetry (INT) and Fast Fourier Transform (FFT) with signal-wave separation to decompose interleaved traffic signals, aiming to overcome the lack of periodic awareness. Furthermore, Fumer employs an off-peak transmission optimization algorithm to calculate optimal time-shift values, thereby tackling synchronized congestion and link idleness. By executing proactive off-peak scheduling, Fumer shifts overlapping communication windows into idle periods to smooth traffic peaks in the time domain. Experimental results show that Fumer boosts average path throughput across all workloads to 86.3 Gbps, improving upon DCQCN (42.6 Gbps) by 102.6% and RECC by 22.1%. Furthermore, it reduces the average and 99.9th-percentile iteration times by up to 25.0%-45.4% and 25.5%-49.3%, respectively, demonstrating its efficacy and robustness across diverse large-scale training workloads.	10.1109/TNSM.2026.3728016
Yali Yuan, Yu Huang, Xingjian Zeng, Hantao Mei, Guang Cheng	M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery	2026	Early Access	Labeling Modeling Electronic mail	The growing complexity of encrypted network traffic presents dual challenges for modern network management: accurate multiclass classification of known applications and reliable discovery of unknown traffic patterns. Although deep learning models show promise in controlled environments, their real-world deployment is hindered by data scarcity, concept drift, and operational constraints. This paper proposes M3S-UPD, a novel Multi-Stage Self-Supervised learning framework for encrypted traffic classification and unknown pattern discovery that synergistically integrates semi-supervised learning with representation analysis. Our approach provides a unified framework for known-class classification and unknown pattern discovery through a four-phase iterative process: 1) probabilistic embedding generation, 2) clustering-based structure discovery, 3) distribution-aligned outlier identification, and 4) confidence-aware model updating. Key innovations include a self-supervised mechanism for unknown pattern discovery that requires neither synthetic samples nor prior knowledge, and a continuous learning framework designed for reliable model updating. Experimental results show that M3S-UPD not only outperforms existing methods on the few-shot encrypted traffic classification task, but also simultaneously achieves competitive performance on the zero-shot unknown pattern discovery task. The code is available at https://github.com/fatmo666/M3S-UPD/.	10.1109/TNSM.2026.3729337
Cong T. Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Hoang-Anh Pham	Generative AI Service Provision in Heterogeneous Edge Networks: A Dynamic Two-Stage Optimization Approach	2026	Early Access	Modeling Timing Resource management	Generative Artificial Intelligence (GAI) has been attracting a massive and rapidly expanding user base worldwide in recent years, resulting in enormous demand for inference requests that cannot be handled efficiently by centralized cloud-based architectures. Edge computing presents a promising approach to mitigate these challenges by leveraging the power of numerous edge devices to better provide GAI services to the users. In this work, we develop a novel two-stage approach to dynamically allocate GAI models and assign user requests to the best edge nodes. In the first stage, we model a joint optimization problem to minimize the expected processing time and decide the optimal model allocation based on predicted user demands. In the second stage, we develop an efficient online approach to assign requests to edge nodes when they arrive, as well as to reallocate GAI models when necessary. Moreover, to address the complexity of the optimization problems in this stage, we leverage Lyapunov optimization framework and Benders decomposition methods to efficiently solve the problems, thereby enabling the proposed approach to quickly adapt to the dynamics of the system. Extensive simulations are conducted to evaluate the performance of the proposed approach and investigate the impacts of important parameters. Simulation results show that the proposed approach can reduce the total processing time by up to 43% with very short running time.	10.1109/TNSM.2026.3730014
Hussein Fawaz, Jacopo Talpini, Marco Savi, Silvia Giordano, Omran Ayoub	Detecting Zero-Day Attacks via Reconstruction of Feature Influence and Model Uncertainty	2026	Early Access	Modeling Uncertainty Training	In practical Network Intrusion Detection System (NIDS) deployments, detecting anomalies is only the first step, while determining the exact nature of those anomalies is equally important. Commonly, anomalous traffic is forwarded to a supervised multiclass classifier trained to identify known attack categories. While effective for known threats, this step presents a significant limitation, as zero-day attacks can be misclassified as known attacks. Therefore, there is a need for approaches that go beyond standard classification and can reliably recognize when an input does not conform to any learned attack pattern, i.e., zero-day attacks. To tackle this problem, we propose a novel detection strategy that leverages per-instance feature importance scores from an explainable Artificial Intelligence (XAI) framework and prediction uncertainty estimates derived from an ensemble classifier. To evaluate our approach, we conduct extensive experiments using a leave-one-attack-out strategy across three benchmark datasets, CICIoT2023, NF–TON–IoT, and CIC–DDoS2019, and test performance under two underlying classifiers, namely XG-Boost and Random Forest, demonstrating the model-agnostic nature of our method. Experimental results show that our approach achieves best-case AUROC gains approaching 40% and F1-score improvements of up to 73%, while maintaining positive or near-neutral worst-case performance across datasets, highlighting the effectiveness and robustness of jointly modeling explanation-driven reconstruction error and predictive uncertainty for reliable zero-day threat identification.	10.1109/TNSM.2026.3731401
Ren-Hung Hwang, Jiao-Chuan Huang, Yuan-Cheng Lai, Ying-Dar Lin	Reinforcement Learning Meets LLM Honeypots: A MITRE Engage–Aligned Approach	2026	Early Access	Large language models Modeling Training	The growing sophistication of cyberattacks, accelerated by large language models (LLMs), highlights the limitations of traditional honeypots, which often lack realism, require heavy maintenance, and rely on static deception strategies. Recent LLM-based honeypots generate fluent, context-aware responses but cannot adapt to evolving attacker behavior, limiting long-term effectiveness. This work presents an adaptive honeypot that integrates reinforcement learning (RL) with LLM-generated deception, aligning state, reward, and action spaces with the MITRE ATT&CK and MITRE Engage frameworks. A finetuned LLM infers attacker tactics, techniques, and procedures (TTPs) from live command sequences, providing semantically rich states for the RL agent, which then selects context-sensitive actions from Engage’s Affect strategies to guide adversaries toward deeper and higher-value engagement. Evaluated on Linux and Windows testbeds, the system achieved a 23% increase in cumulative engagement reward on Windows over a non-RL baseline (p < 0.001). Ablation over five random seeds shows that replacing the learned policy with random action selection over the same action space collapses attack depth from 9.52 to 4.25 on Linux (p < 0.001), confirming that the learned policy, not the action space alone, drives engagement. Intent analysis accuracy improved by 55 percentage points relative to a rule-based baseline (Wazuh), and LLM-generated responses fell within 10 percentage points of a real system, a substantially smaller gap than Cowrie, an ordering confirmed by an independent cross-family judge. These results demonstrate that RL-driven adaptation, combined with LLM realism and standardized engagement frameworks, enables honeypots that sustain realistic, intelligence-rich interactions and enhance threat analysis without compromising system safety.	10.1109/TNSM.2026.3731455
Stephen Jasina, Loqman Salamatian, Joshua Mathews, Scott Anderson, Paul Barford, Mark Crovella, Walter Willinger	Matisse: Visualizing Measured Internet Latencies as Manifolds	2026	Early Access	Manifolds Internet Measurement	Manifolds are complex topological spaces that can be used to represent datasets of real-world measurements. Visualizing such manifolds can help with illustrating their topological characteristics (e.g., curvature) and providing insights into important properties of the underlying data (e.g., anomalies in the measurements). In this paper, we describe a new methodology and system for generating and visualizing manifolds that are inferred from actual Internet latency measurements between different cities and are projected over a 2D Euclidean space (e.g., a geographic map). Our method leverages a series of graphs that capture critical information contained in the data, including well-defined locations (for vertices) and Ricci curvature information (for edges). Our visualization approach then generates a curved surface (manifold) in which (a) geographical locations of vertices are maintained and (b) the Ricci curvature values of the graph edges determine the curvature properties of the manifold. The resulting manifold highlights areas of critical connectivity and defines an instance of “Internet delay space” where latency measurements manifest as geodesics. We describe details of our method and its implementation in a tool, which we call Matisse, for generating, visualizing and manipulating manifolds projected onto a base map. We illustrate Matisse with three case studies: a simple example to demonstrate key concepts, and visualizations of the US and Europe public Internet to show Matisse’s utility.	10.1109/TNSM.2026.3730274
Soonbeom Kwon, Yusu Noh, Youngwoo Jang, Illyoung Choi, Byungchul Tak, In-geol Chun, Young-Kyoon Suh	Scalable and Robust Resource Provisioning via Adaptive Task Scheduling for Edge Devices	2026	Early Access	Schedules Scheduling Cloning	Edge devices, such as wearables, drones, and CCTV systems, are vital for real-time data collection in urban intelligence. However, their limited computational and storage capacities pose significant challenges. While offloading to public clouds offers scalability, it often incurs high latency and operational costs. Conversely, centralizing workloads on edge servers may result in the underutilization of high-performance edge devices. To address these limitations, we introduce ERPF, a Kubernetes-based Edge Resource Provisioning Framework that augments the capabilities of heterogeneous edge environments. ERPF orchestrates dynamic volume provisioning, GPU-aware resource allocation, execution context migration, and adaptive task distribution to improve system flexibility and efficiency. Building on this, we propose a novel adaptive task scheduling technique, termed eATS, composed of three key mechanisms: (i) Partition Smoothing Scheme for stable task granularity control, (ii) Resilient Edge Reintegration for failure detection and task reassignment, and (iii) Competitive Task Cloning for speculative execution with fastest-result commitment. The proposed eATS scheme reduces task execution time by up to 27.6%, lowers partition size variability by 8.7×, and improves scheduling robustness across heterogeneous edge devices over the baseline.	10.1109/TNSM.2026.3694238
Vishnu Prakash, Shrinivas Petale, Reshma Rastogi, Suresh Subramaniam, Bijoy Chand Chatterjee	DRLF: Deep Reinforcement Learning-Based Fragmentation-Aware Routing and Spectrum Allocation in Elastic Optical Networks	2026	Early Access	Resource management Elastic optical networks Training	Fragmentation poses a significant challenge in elastic optical networks (EONs) and diminishes resource utilization. While various research efforts have attempted to address the fragmentation issue, they often rely on rule-based strategies. Although these strategies encode valuable knowledge, they may not fully capture the dynamic and multifaceted behaviors of EONs. This limitation impedes adaptive service provisioning to mitigate fragmentation. To enhance network performance, this paper proposes a deep reinforcement learning-based fragmentationaware routing and spectrum allocation, named DRLF, which employs deep neural networks (DNNs) to learn fragmentation-aware routing and spectrum allocation (RSA) within the intricate EON state spaces. Through the utilization of the deep Q-network (DQN) algorithm, DNN parameters are updated to facilitate episode-based training of DRLF. The RSA training process is segmented into episodes, each comprising a fixed number of lightpath requests, with a primary focus on optimizing network resource utilization while minimizing fragmentation. Unlike previous approaches, DRLF implements a continuous reward policy tied to path fragmentation, where rewards are inversely correlated with fragmentation levels. Additionally, the agent receives a bonus reward for successfully achieving the blocking probability under the threshold value in each episode. DRLF incorporates fragmentation as heuristic information in the reward function, and the action space is designed to train the agent to find a suitable spectrum allocation so that the fragmentation increase can be minimized. Consequently, the DRLF agent is trained to prioritize paths and spectrum slots with lower fragmentation levels, thereby accommodating more lightpath requests in future scenarios. Numerical evaluations demonstrate that DRLF surpasses existing DRL-based approaches, such as DeepRMSA and HeuDRL, as well as heuristic rule-based allocation strategies, particularly in terms of blocking performance.	10.1109/TNSM.2026.3731113
Lazaros Liatsas, Godfrey M. Kibalya, Angelos Antonopoulos	Counterfactual Autoscaling for Resource-Efficient Service Orchestration in the Cloud–Edge Continuum	2026	Early Access	Resource management Quality of service Clouds	Cloud–edge computing enables scalable and resilient deployment of microservice-based applications, however achieving resource efficiency while ensuring stringent Quality of Service (QoS) remains challenging. The strong interdependencies among microservices and non-linear latency effects near resource saturation render conventional workload-driven autoscaling ineffective in complex distributed environments. This paper introduces CARSO (Counterfactual Autoscaling and Resource-efficient Service Orchestration), a proactive and interpretable framework that integrates eXplainable Artificial Intelligence (XAI) into the autoscaling process. CARSO employs counterfactual reasoning to derive minimal resource adjustments that proactively prevent QoS violations. The framework includes two core components: i) a Counterfactual Vertical Autoscaling (CVA) scheme that anticipates and mitigates performance degradation and ii) a Latency-Aware Resource Orchestration (LARO) policy that coordinates scaling and placement actions to balance resource efficiency and end-to-end latency across the cloud–edge continuum. Extensive experiments demonstrate that CARSO outperforms state-of-the-art proactive autoscaling frameworks in both QoS compliance and overall resource utilization.	10.1109/TNSM.2026.3731114
Minhyeok Jang, Jalel Ben-Othman, Hyunchae Chun, Sungrae Cho, Hyunbum Kim	Multi-Agent Network Management with Dynamic Entropy-Driven Logistic Trust Aggregation	2026	Early Access	Entropy Modeling Management	Autonomous network management increasingly fuses multiple heterogeneous detectors—such as the intrusion detectors that monitor different traffic planes for 6G and IoT security—through adaptive trust-weighted consensus. When trust is updated online, however, such systems face a fundamental stability-agility trade-off: they are either calm but slow to react to novel threats, or fast but erratic under routine noise. We identify and formalize the resulting failure modes of trust collapse and blind conformity, and propose DELTA (Dynamic Entropy-driven Logistic Trust Aggregation), a self-regulating trust-management framework. DELTA couples a Fixed-Share Redistribution regularizer, which guarantees a minimum trust quota for every detector, with an entropy-amplified logistic controller whose learning rate is driven by the current leader’s error rate and amplified by the ensemble’s structural entropy; this keeps the system quiescent under normal traffic yet triggers a rapid, bounded re-calibration the moment the trusted detector begins to fail. We prove that DELTA enforces a strictly positive diversity floor—making trust collapse provably impossible—and derive bounds on its transition latency and stationary volatility. Across an extensive evaluation—including robustness to delayed, missing, and adversarial feedback, comparison against expert-advice, Bayesian, and change-point baselines with confidence intervals, and validation on the real UNSW-NB15 intrusion dataset—DELTA recovers from zero-day regime shifts where naive baselines collapse below chance, while remaining an order of magnitude more stable than aggressive adaptive methods, all at O(N) computational and communication cost.	10.1109/TNSM.2026.3731203
Mubashir Murshed, Glaucio H. S. Carvalho, Robson E. De Grande	Holistic Intelligent Traffic Steering Management in Multi-RAT Vehicular Networks	2026	Early Access	Radio access technologies Rats Vehicles	Multiple Radio Access Technology (multi-RAT) environments provide a promising foundation for service-aware communication in intelligent transportation systems (ITS) and smart cities. However, traffic steering (TS) in highly mobile and ultra-dense vehicular networks remains challenging due to dynamic network conditions, heterogeneous RAT capabilities, varying vehicle requirements, packet loss, latency, and frequent ping-pong RAT switching. In this context, we propose Holistic Intelligent Traffic Steering (HITS), a proactive bi-level TS management framework for multi-RAT vehicular networks. HITS integrates centralized network-wide guidance with local vehicleside decision-making. At the central level, a Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model captures holistic spatio-temporal network dynamics and evaluates RAT optimality. At the local level, a State-Action-Reward- State-Action (SARSA) reinforcement learning agent performs adaptive, vehicle-specific RAT selection using local observations and central-level optimality guidance. Results show that HITS achieves up to 6.5% higher average throughput, reduces packet loss ratio by more than 30.2%, lowers latency by nearly 12.2%, and reduces the ping-pong RAT switching rate by over 24% compared with baseline and state-of-the-art (SoTA) TS approaches.	10.1109/TNSM.2026.3729840
Shuang Zheng, Xing Zhang, Michael Sheng, Haixu Wang, Wenbo Wang	Beam Hopping Low Earth Orbit Satellite Resource Allocation for Differentiated Services and Robustness Analysis under Model Attacks	2026	Early Access	Beams Satellites Resource management	Beam hopping (BH)-enabled Low Earth Orbit (LEO) satellites play a pivotal role in next-generation communication networks, providing global coverage, improving spectrum efficiency, and supporting flexible adaptation to heterogeneous service demands. To fully exploit these capabilities, artificial intelligence (AI) techniques are increasingly employed for dynamic resource allocation and power management. However, limited onboard resources and potential adversarial perturbations pose challenges to both efficiency and robustness. To address these issues, we leverage digital twin technology to accurately capture the spatio-temporal dynamics of user–satellite visibility, providing precise state information for decision-making. Building on this, we formulate a joint optimization framework for BH scheduling and power allocation as a Markov Decision Process and propose the BRIDGE—BH with Reinforcement learning incorporating Integrated Dirichlet and Gumbel-TopK Exploration—which integrates a quality of service (QoS)-driven subchannel scheduling mechanism to ensure efficient and differentiated resource allocation. The model’s robustness is systematically evaluated under three classical adversarial attacks. Simulation results demonstrate that our approach achieves superior energy efficiency, service throughput, and fairness, while the robustness analysis shows stable performance under the considered bounded adversarial perturbations.	10.1109/TNSM.2026.3710750
Mustafa Türk, Müge Sayıt, Ali C. Begen, Andreas J. Kassler	FROG: Fast Response to Optimization Goals for HTTP Adaptive Streaming over SDN	2026	Early Access	Optimization Videos Servers	In modern video streaming systems, clients typically make independent bitrate decisions without full knowledge of network conditions, often leading to inefficient resource usage and unstable quality. Network-assisted adaptive streaming is rapidly gaining importance in Network and Service Management (NSM), as operators strive to deliver consistently high Quality of Experience (QoE) under dynamic traffic conditions. This paper introduces FROG, a novel, real-time Software-Defined Networking (SDN)-assisted framework designed to coordinate multi-server and multipath HTTP Adaptive Streaming (HAS) using standardized metrics carriage: Common Media Client Data (CMCD) and Common Media Server Data (CMSD). FROG employs an innovative two-stage optimization workflow in which an initial Linear Programming (LP) model rapidly determines feasible bandwidth bounds, server selection, and path capacities, thereby transforming the remaining optimization into a sequential layer-selection process for coordinated quality selection and flow allocation. This decomposition enables sub-second optimization at the scale of thousands of users, while buffer-aware client feedback is integrated to proactively prevent stalls and maintain system stability. Experiments on an emulated SDN testbed demonstrate that FROG achieves QoE comparable to that of the optimal MILP solution on tractable instances while virtually eliminating video stalls, and significantly reduces quality oscillations, outperforming state-of-the-art network-assisted approaches by up to 2.32× under playback-driven evaluation scenarios. Scalability experiments with up to 4,000 clients further demonstrate sub-second optimization runtimes, confirming the practicality of FROG for large-scale deployments.	10.1109/TNSM.2026.3729597
Guiyan Liu, Ji Li, Kaixin Qin, Songtao Guo, Liang Liu, Li Yin	AdpVDLTS: Adaptive Spatio-Temporal VNF Placement and Load Balanced Traffic Scheduling in Edge Computing Networks	2026	Early Access	Loading Modeling Timing	The rise of network function virtualization (NFV) technology has enabled virtual network functions (VNF) and service function chains (SFCs) to develop into standard paradigms for service delivery. The uncertain traffic brought about by edge computing has made it a key issue to figure out how to deploy VNFs for network load balancing. However, traditional methods are limited to SFC embedding solutions and resource management and pay less attention to traffic. To address the above issue, this paper takes into account the spatio-temporal characteristics of network traffic and the traffic scheduling after VNF deployment to solve the load balanced VNF deployment problem. We formalize the problem into an NP-hard nonlinear integer programming problem, which will be solved with the proposed algorithm adaptive VNF deployment and load balanced traffic scheduling (AdpVDLTS). AdpVDLTS divides network time into large and small time slots to operate on traffic and VNFs simultaneously, and achieves load balancing through traffic prediction and collaboration with VNF deployment. Compared with the excellent existing algorithms, AdpVDLTS can maintain more stable load balancing, higher throughput, lower deployment cost, and lower latency. In addition, the effectiveness of the traffic prediction algorithm is proved by ablation experiments.	10.1109/TNSM.2026.3729261
Rita Ingabire, Antonio Bazco-Nogueras, Vincenzo Mancuso, Luis M. Contreras, Jesús Folgueira	Explainable AI to Understand the Latency Behavior of Public Cloud Service Platforms	2026	Early Access	Clouds Distance measurement Forecasting	Cloud platforms have become a core component of the Internet because most services and products rely on them to host their backends. Estimating and understanding the latency experienced when accessing those cloud platforms is a challenge of growing importance that has not been sufficiently studied. To address this relevant matter, we conducted a three-month measurement campaign, collecting traceroute data every 30 min across 256 source–destination probe pairs. Our specific goal is to analyze whether the current network is able to provide adequate performance for emerging applications and services. We use this dataset to evaluate the performance of forecasting algorithms when predicting cloud latency from both temporal and spatial perspectives, and we leverage post-hoc explainability methods to identify the drivers affecting latency. Several prior studies provide public cloud-latency datasets, but these datasets are generally analyzed in isolation. To close this gap and provide a cross-dataset comparative analysis of cloud-latency measurements, we analyzed the related publicly available datasets and applied a common forecasting and explainability workflow to compare their findings. Our analysis reveals that operators do not require complex methods to predict latency and that distance and a few other simple features are sufficient to achieve operationally accurate predictions. We find latency to be remarkably stable from the user’s perspective, both over the duration of the campaign and across hours of the day, which contrasts with previous findings, and we show that the specific path traversed has a significant impact on latency.	10.1109/TNSM.2026.3730351
Yuqiang Wen, K. L. Eddie Law	Selected Group Broadcasting: Modeling and Applications	2026	Early Access	Modeling Broadcasting Timing	Modern decentralized applications frequently employ one-to-many messaging over dynamic recipient groups, a paradigm we term Selected Group Broadcasting (SGB). Though one-to-one traffic models (e.g., M/M/1 queue) are well studied, traffic characterization for one-to-many connectivity remains underdeveloped. In this paper, we investigate the delay distribution of one-to-many traffic over an SGB in a wide-area communication system. Depending on the underlying network input, the SGB problem naturally decomposes into two distinct paradigms: SGBs (for Sequential inputs) and SGBp (for Parallel inputs). Asymptotic analysis reveals that SGBs and SGBp are governed by fundamentally different stochastic dynamics, necessitating dedicated modeling approaches for each. This paper focuses exclusively on the SGBs paradigm, providing a rigorous analytical treatment and establishing an operatortheoretic performance framework. For first-order statistics of SGBs, we derive exact closed-form solutions under exponential inter-ACK interval (ACK Interval) inputs. By exploiting the rational Laplace-domain structure of the forward operator, we extend the model to precisely compute output distributions for any non-negative distribution input. Furthermore, we introduce an equivalent SGBs analysis scheme via the inverse operator, enabling the transformation of continuous commit-time distributions into physically interpretable ACK Interval profiles. For second-order statistics, we establish a formalized functional hypothesis under exponential ACK Intervals, validated via simulation. We verify the derived probability density functions with simulated delays; the results are confirmed via Kolmogorov-Smirnov tests.	10.1109/TNSM.2026.3729017