TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Jiang Mo, Ke Zhao, Limei Peng, Hsiao-Chun Wu	PDO-SFCM: Prediction-Driven Orchestration for SFC Migration in SAGIN via Fine-Tuned Large Time-Series Model and DRL	2026	Early Access	Modeling Space-air-ground integrated networks Timing	Space-air-ground integrated networks (SAGINs) have emerged as an appealing enabling technology for the next-generation ubiquitous connectivity. By extending terrestrial networks with aerial and space platforms, SAGIN can provide seamless coverage and flexible resource-access across various altitudes. However, dynamic link conditions, intermittent connectivity, and heterogeneous latency constraints would often introduce serious challenges to the service function chain (SFC) migration and orchestration. In this work, we introduce a novel PDO-SFCM (prediction-driven orchestration for SFC migration) approach, which utilizes a fine-tuned large time-series model (LTM) for network status prediction and a deep reinforcement learning (DRL) module for proactive SFC migration in SAGINs. In detail, the fine-tuned LTM predicts multi-horizon estimates of SFC arrivals and per virtual network function (per-VNF) resource demands, which will form the observation space of the DRL agent. The DRL module thus schedules appropriate migration actions on the cost-augmented time-expanded graph (C-eTEG), which can satisfy the feasibility subject to the bandwidth, buffering, and precedence constraints. Extensive simulation results demonstrate that our proposed new PDO-SFCM scheme consistently greatly improves the acceptance rate, reduces the end-to-end delay, and lowers the migration cost in comparison with DRL baselines under different prediction settings. Our proposed new scheme can significantly leverage the SAGIN performance by the devised foundation-level time-series prediction and learning-based orchestration mechanisms.	10.1109/TNSM.2026.3694203
Songshou Dong, Yanqing Yao, Huaxiong Wang, Yining Liu	LCMS: Efficient Lattice-based Conditional Privacy-preserving Multi-receiver Signcryption Scheme for Internet of Vehicles	2026	Early Access	Optical waveguides Optical fibers Broadcasting	Internet of Vehicles (IoV) requires robust security and privacy protection mechanisms to enable trusted traffic information exchange, while also requiring low communication and low computing overhead to meet the real-time requirements of IoV. Existing signcryption schemes suffer from quantum vulnerability, inadequate unlinkability/vehicle anonymity, absence of revocability, poor scalability, inadequate management of malicious entities, and high communication and computational overhead. So we propose an efficient lattice-based conditional privacy-preserving multi-receiver signcryption scheme (LCMS) that systematically addresses these gaps through three core innovations: 1) Privacy preservation is achieved via a pseudonym mechanism integrated with certificateless key generation, which ensures vehicle anonymity and weak unlinkability while preventing malicious key generation center and key escrow; 2) Malicious entity management through dynamic revocability and distributed decryption among roadside units, preventing unilateral message access; and 3) Post-quantum efficiency is achieved by leveraging the Learning With Rounding problem to eliminate expensive Gaussian sampling, combined with ciphertext packing techniques. This reduces time overhead, the size of signcryptexts, and communication overhead, while lowering the overall storage overhead of the scheme through the MP12 trapdoor. Security proofs show LCMS achieves Existential Unforgeability under Adaptive Identity Chosen-Message Attack and Indistinguishability under Adaptive Identity Chosen-Ciphertext Attack in the Random Oracle Model, with rigorously validated resistance against multiple IoV-specific attacks. Experimental results via SageMath implementation demonstrate that our scheme exhibits a smaller signcryptext size and lower signcryption/unsigncryption time compared to existing random lattice-based signcryption schemes. Scalability tests with 300 vehicles and 300 roadside units (RSUs) were completed within 230 seconds. Communication overhead analysis confirms practical feasibility for IEEE 802.11p vehicle communication protocol, and RSU serving capability evaluation under realistic vehicle density (100–200/km2) and speed (40–60 km/h) further validates system practicality. LCMS provides a quantum-resistant, privacy-preserving, and efficient solution for production IoV.	10.1109/TNSM.2026.3688507
Jing Zhang, Chao Luo, Rui Shao	MTG-GAN: A Masked Temporal Graph Generative Adversarial Network for Cross-Domain System Log Anomaly Detection	2026	Early Access	Anomaly detection Adaptation models Generative adversarial networks	Anomaly detection of system logs is crucial for the service management of large-scale information systems. Nowadays, log anomaly detection faces two main challenges: 1) capturing evolving temporal dependencies between log events to adaptively tackle with emerging anomaly patterns, 2) and maintaining high detection capabilities across varies data distributions. Existing methods rely heavily on domain-specific data features, making it challenging to handle the heterogeneity and temporal dynamics of log data. This limitation restricts the deployment of anomaly detection systems in practical environments. In this article, a novel framework, Masked Temporal Graph Generative Adversarial Network (MTG-GAN), is proposed for both conventional and cross-domain log anomaly detection. The model enhances the detection capability for emerging abnormal patterns in system log data by introducing an adaptive masking mechanism that combines generative adversarial networks with graph contrastive learning. Additionally, MTG-GAN reduces dependency on specific data distribution and improves model generalization by using diffused graph adjacency information deriving from temporal relevance of event sequence, which can be conducive to improve cross-domain detection performance. Experimental results demonstrate that MTG-GAN outperforms existing methods on multiple real-world datasets in both conventional and cross-domain log anomaly detection.	10.1109/TNSM.2026.3654642
Emilio Paolini, Andrea Pinto, Luca Valcarenghi, Flavio Esposito	Programmable In-Network Aggregation for Communication-Aware Federated Learning in 5G RANs	2026	Early Access	Modeling Timing Training	Federated Learning (FL) enables collaborative model training without sharing raw data, making it attractive for privacy-preserving applications at the wireless edge. However, when executed over real 5G networks, FL performance degrades due to uplink congestion, heterogeneous client capabilities, and intermittent connectivity. Most existing approaches attempt to mitigate these issues indirectly by optimizing clients (through adaptive participation, local training, or selection strategies) or by optimizing models (via pruning, quantization, or compression), but they ignore potential network bottlenecks. This paper introduces FLAG, an FL architecture that embeds innetwork aggregation directly into 5G gNodeBs, transforming the network into an active participant in the learning process. In particular, FLAG performs parameter aggregation at line rate within the 5G Service Data Adaptation Protocol layer and incorporates three mechanisms: Partial-Contribution Correction for loss-tolerant averaging, a timer-driven pipeline for real-time scheduling, and a deadline-based grouping strategy to mitigate stragglers. Experiments with realistic wireless emulation show that FLAG achieves up to 5.1× faster time-to-accuracy and maintains accuracy within 0.8% of a loss-free baseline, while reducing gNB-to-server bandwidth by aggregating pergNB rather than per-client. FLAG requires no modifications to clients or the parameter server, demonstrating how 5G-aware system design can make federated learning scalable, efficient, and resilient under real-world wireless conditions.	10.1109/TNSM.2026.3697723
Awaneesh Kumar Yadav, Madhusanka Liyanage, An Braeken	An Improved and Provably Secure EDHOC Protocol Supporting the Extended Canetti–Krawczyk (eCK) Security Model	2026	Early Access	Aerospace and electronic systems Telemetry Central Processing Unit	Transport Layer Security (TLS) is considered to be the most used standard security protocol for the Internet of Things (IoT). However, as TLS was originally designed for computer networks, it is not optimal with respect to efficiency. Therefore, a new protocol called Object Security for Constrained RESTful Environments (OSCORE) has been standardized for securing constrained devices. Currently, the Ephemeral Diffie Hellman Over COSE (EDHOC) protocol, which is a key exchange protocol to define a session key used in OSCORE, is also in the process of being standardized. This paper shows that the four authentication modes of the EDHOC protocol are vulnerable in the extended Canetti–Krawczyk (eCK) security model, which is a common security model used in IoT. In addition, also resistance to Distributed Denial of Service (DDoS) attacks is weak. Taking this into account, we propose two new variants of EDHOC. The first variant, EDHOC2, is able to overcome both issues but has a slightly higher cost for communication, computation, storage, and energy consumption. The second variant, EDHOC3, offers only additional protection in the eCK security model and has, on average, similar, even better performance in one authentication mode, compared to EDHOC. Additionally, the Real-Or-Random (ROR) logic and Scyther validation tool are employed to ensure the security of the designed variants. Furthermore, a prototype implementation is conducted to demonstrate the real-time deployment of the designed versions.	10.1109/TNSM.2026.3690530
Li Zhang, Chan Xu, Yuan Huang, Bing Tang, Zijun Peng, Wenhui He, Buqing Cao, Mingdong Tang	Elastic Scaling for Microservices in Cloud-Edge Collaborative Environments: A Workload Prediction-Driven Approach	2026	Early Access		Cloud computing optimizes service quality and resource efficiency via centralized hardware and computational resources. However, the predominantly centralized deployment and operation of cloud data centers increase the physical distance to end-users, leading to degraded service quality. Edge computing addresses this by offloading data processing and analysis tasks directly to devices at the network edge, reducing reliance on backhaul transmission and thus offering a more responsive solution for latency-sensitive applications. Nevertheless, ensuring that applications meet predefined Service Level Agreement (SLA) in resource-constrained edge environments remains challenging. To tackle these issues, this paper investigates elastic scaling strategies in cloud-edge collaborative settings. We propose an attention-enhanced bidirectional LSTM model (A-Bi-LSTM) for microservice workload prediction, and design an adaptive elastic scaling system named XScale. This system incorporates a fall-back scaling mechanism when predictions are unreliable and introduces a proactive load forwarding strategy to enhance overall edge node performance. Experimental results show that, compared to existing elastic scaling methods, XScale reduces SLA violations by 82.3%, increases average resource utilization by 17.4%, decreases average response time by 21.1%, and improves overall edge node performance by 36.3%.	10.1109/TNSM.2026.3696137
Ke Yu, Xiaofeng Tao, Shen Wang, Chaojie Guo	Game-Theoretic Defense of SYN Flood Attacks in B5G Cloud-Edge-Terminal Networks	2026	Early Access		The emergence of beyond-fifth-generation (B5G) networks and the increasing demand for Internet of Things (IoT) requires deploying a cloud-edge-terminal computing network with the Software Defined Network as the controller. However, this network is vulnerable to various threats, notably SYN flood attacks. This paper adopts queuing theory and game theory to explore Mobile Edge Computing (MEC) attack and defense interaction in different IoT businesses. Moreover, we propose a utility model of the packet flow in MEC networks featuring the delay and packet loss rate in the SYN flood attacks. For the attacker and defender’s strategy, we use game theory to model the interaction between strategy and resource allocation. A search algorithm analyzing MEC cell impact on strategy selection is developed, and we investigate the impact of the attacker’s possession of prior knowledge versus lack thereof regarding MEC cell characteristics under SYN flood attacks. The proposed game models are solved, and the results show that under the defender’s strategy, the attacker has no chance to launch SYN flood attacks under the defender’s defense cost of four times MEC computing resources; the cost of defense resources is lower than other related schemes.	10.1109/TNSM.2026.3695918
Deemah H. Tashman, Soumaya Cherkaoui	Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks	2026	Early Access	Reconfigurable intelligent surfaces Reliability Optimization	Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adaptive, energy-aware hybrid reconfigurable intelligent surface (RIS) for underlay multiple-input single-output (MISO) CRNs. Distinct from prior approaches relying on static RIS architectures, our proposed RIS dynamically alternates between passive and active operation modes in real time according to harvested energy availability. We also model our scenario under practical hardware impairments and cascaded fading channels. We formulate and solve a joint transmit beamforming and RIS phase optimization problem via the soft actor-critic (SAC) deep reinforcement learning (DRL) method, leveraging its robustness in continuous and highly dynamic environments. Notably, we conduct the first systematic study of reward poisoning attacks on DRL agents in RIS-enhanced CRNs, and propose a lightweight, real-time defense based on reward clipping and statistical anomaly filtering. Numerical results demonstrate that the SAC-based approach consistently outperforms established DRL base-lines, and that the dynamic hybrid RIS strikes a superior trade-off between throughput and energy consumption compared to fully passive and fully active alternatives. We further show the effectiveness of our defense in maintaining SU performance even under adversarial conditions. Our results advance the practical and secure deployment of RIS-assisted CRNs, and highlight crucial design insights for energy-constrained wireless systems.	10.1109/TNSM.2026.3660728
Guangquan Xu, Tuoyu Chen, Guohua Xin, Wei Yu, Hongpeng Bai, Zhigang Li	UFG-AFLNET: A Greybox Fuzzing Framework with Fine-Grained State Modeling and Gradient-Guided Mutation for Network Protocols	2026	Early Access		Greybox fuzzing has become an effective technique for uncovering vulnerabilities in network protocol implementations. However, existing approaches still face several significant challenges: (1) state modeling is overly coarse-grained, failing to accurately capture subtle state transitions during protocol execution, (2) focusing solely on the first mutation point that reaches the target state, overlooking other regions that may equally impact the target state, (3) neglecting the non-uniform contribution of different message regions to path coverage. To address these issues, we propose UFG-AFLNET, a unified greybox fuzzing framework. UFG-AFLNET significantly enhances fuzzing efficiency and vulnerability discovery through fine-grained state machine modeling, gradient-guided sequence selection, and lightweight dynamic taint inference. Specifically, UFG-AFLNET introduces a state clustering learner that uses the Single-Pass clustering algorithm to extend response state machines into fine-grained path state machines, thereby enabling more precise state differentiation. Additionally, to select the most critical mutation points, we employ a recurrent neural network to compute the sensitivity gradients between target path states and message regions. Finally, we use a message sequence mutator supported by dynamic taint inference to assign weights to each byte and prioritize mutations on those most likely to expose new execution paths. Experiments on five widely used protocol implementations show that UFG-AFLNET significantly outperforms baseline fuzzers in path coverage, the number of vulnerabilities discovered and so on. These results demonstrate the potential of UFG-AFLNET in advancing the field of network protocol security testing.	10.1109/TNSM.2026.3695923
Shuyun Luo, Dongmiao Ying, Zhiyi Luo, Weiqiang Xu	Edge-based Approximate Caching for Fast and Scalable Text-to-Image Diffusion Models	2026	Early Access		Text-to-image generation applications based on diffusion models face substantial challenges in computational efficiency and latency, particularly in time-sensitive scenarios, due to the inherently iterative denoising process. Although approximate caching techniques can reduce denoising iterations by reusing intermediate states of diffusion models, existing approaches fail to adequately capture user request behaviors. It is observed that users tend to issue a large number of prompt requests within short time intervals (bursty patterns), and that prompts from the same user often exhibit high similarity over short time periods (temporal locality). In this work, we define and formalize the Intermediate State Selection (ISS) problem to minimize denoising iterations. We further prove the NP-hardness of the ISS problem via a polynomial-time reduction from the Dominating Set problem. We then exploit both characteristics of prompt requests and present EdgeDiffusion, a novel edge-cloud cooperative framework in which the cloud retains image generation, while prompts caching and intermediate state selection are offloaded to edge servers. Specifically, we design an ISS algorithm that optimizes state reuse by leveraging temporal locality and an adaptive caching strategy tailored to bursty patterns. Experimental results on real-world datasets demonstrate that EdgeDiffusion achieves 18.3%-77.3% computational savings over baseline strategies (NIRVANA, qLRU-AC, LRU and LFU), while maintaining 98% quality of images.	10.1109/TNSM.2026.3697787
Dinghao Zeng, Fagui Liu, Runbin Chen, Jingwei Tan, Dishi Xu, Qingbo Wu, C.L. Philip Chen	CoreScaler: A Resource-Efficient Hybrid Scaling Framework for Dynamic Workloads in Cloud	2026	Early Access	Resource management Central Processing Unit Memory	Containerized microservices face significant challenges in balancing service quality and resource efficiency under dynamic workloads. Existing approaches suffer from horizontal scaling’s cold start latency, vertical scaling’s resource ceilings, and hybrid methods’ limited adaptability. We present CoreScaler, a resource-efficient hybrid scaling framework based on analysis of CPU usage patterns revealing substantial consumption differences between working mode and waiting mode instances. This insight drives our dual-mode instance management model that distinguishes between working instances actively handling requests and waiting instances maintaining hot standby with minimal resource allocation. CoreScaler employs a master-subordinate distributed architecture where the master node performs capacity planning using multi-confidence interval predictions and contextual multi-armed bandit optimization, while subordinate nodes execute mode-aware CPU quota adjustments. Comprehensive evaluation on a Kubernetes cluster with a typical microservice system under four representative production work-loads demonstrates that CoreScaler maintains SLO compliance while reducing CPU and memory allocation by 22.53% and 30.83% respectively compared to state-of-the-art solutions. The framework achieves substantially higher resource utilization than single-dimension scaling approaches, validating the effectiveness of coordinated hybrid scaling for dynamic cloud environments.	10.1109/TNSM.2026.3692955
Elie Inaty, Charbel Maroun, Ghattas Akkad, Ali Mansour, Martin Maier	A 6G Driven Multiclass Power Efficient Dynamic Bandwidth Allocation (MPE-DBA) Scheme for Passive Optical Network (PON)	2026	Early Access	Costing Costs Algorithms	The latest ITU IMT-2030 recommendations for sixth generation (6G) networks have imposed strict specifications on communication systems. Some of these requirements include increased throughput, ultra-low delay and jitter, differentiated services, and energy efficiency. Current dynamic bandwidth allocation (DBA) schemes for passive optical networks (PON) may meet some of these requirements, yet they fail to fulfill other recommendations, especially energy efficiency. Therefore, we propose a new PON architecture, whose objective is to offer flexibility in meeting the IMT-2030 recommendations. It uses a multiclass power efficient dynamic bandwidth allocation (MPE-DBA) scheme that helps achieving both differentiated services and sustainability in terms of energy and cost. For computational efficiency, we propose a two stages Mamdani fuzzy inference system (FIS). The inputs of the first FIS are the latency and cost of the 6G traffic, whereas the latency and cost of the non-6G (N6G) traffic are the inputs of the second FIS stage. Both FISs use the variation in the number of channels as output. The proposed algorithm achieves less than 100 μs delay, less than 10μs jitter and high aggregate throughput for the 6G packets. In addition, it reduces the power consumption by three times and the cost of traffic transmission by four times as compared to the state-of-the art solution.	10.1109/TNSM.2026.3694150
Lizhuang Tan, Nguyen Van Tu, Xinhang Wang, Peiying Zhang, James Won-Ki Hong	SDNIE: A Software-Defined Approach to High-Performance Network Impairment Emulation using Programmable Switches	2026	Early Access	Emulation Central Processing Unit Testing	Network testing is critical for evaluating the performance, reliability, and security of modern computer networks. A key challenge is creating an accurate, cost-effective, and high-performance network emulation environment. Network Impairment Emulators (NIEs) emulate real-world network conditions such as bandwidth constraints, latency, and packet loss, but existing CPU- and FPGA-based solutions suffer from limited performance, high costs, and poor flexibility. This paper proposes Software-Defined Network Impairment Emulation (SDNIE), a novel framework that leverages programmable switches for scalable, cost-efficient network impairment emulation. SDNIE introduces three key techniques: (1) intent-driven network impairment configuration, automating impairment modeling; (2) serial-parallel combined execution, optimizing performance; and (3) CPU-Tofino collaborative deployment, offloading complex computations. Experimental results show that SDNIE matches commercial emulators in performance while significantly reducing costs. This work demonstrates the potential of programmable switches in network testing, offering a scalable, cost-effective, and high-performance alternative for next-generation network impairment emulation.	10.1109/TNSM.2026.3694388
Arad Kotzer, Tom Azoulay, Yoad Abels, Aviv Yaish, Ori Rottenstreich	SoK: DeFi Lending and Yield Aggregation Protocol Taxonomy, Empirical Measurements, and Security Challenges	2026	Early Access	Filtering Application specific integrated circuits Filters	Decentralized Finance (DeFi) lending protocols implement programmable credit markets without intermediaries. This paper systematizes the DeFi lending ecosystem, spanning collateralized lending (including over- and under- collateralized designs, and zero-liquidation loans), uncollateralized primitives (e.g., flashloans), and yield aggregation protocols which allocate capital across underlying lending platforms. Beyond a taxonomy of mechanisms and comparing protocols, we provide empirical on-chain measurements of lending activity and user behavior, using Compound V2 and AAVE V2 as case studies, and connect empirical observations to protocol design choices (e.g., interestrate models and liquidation incentives). We then characterize vulnerabilities that arise due to notable designs, focusing on interestrate setting mechanisms and time-measurement approaches. Finally, we outline open questions at the intersection of mechanism design, empirical measurement and security for future research.	10.1109/TNSM.2026.3682174
Ashely Li, Jeffrey Chang, Steven S. W. Lee	Modeling and Optimization Algorithm for Capacity Planning in Hose Model VPN Networks	2026	Early Access	Joining processes Modeling Algorithms	Hose-based VPNs offer greater bandwidth flexibility, as they allow traffic to and from a hose endpoint to be arbitrarily distributed across other endpoints. Existing studies on hose-based VPNs have primarily focused on VPN provisioning algorithms, while optimal capacity planning for hose-based VPN networks remains largely unexplored. Given budget constraints and forecasts of future bandwidth demands at VPN endpoints, the capacity planning problem requires the joint optimization of routing decisions and link capacity allocation. Although the problem can be formulated as a nonlinear programming model, its nonconvex nature makes direct solution computationally challenging. To address this issue, we reformulate the problem as a sequence of linear programming problems and develop a solution framework based on a water-filling algorithm. For any defined budget and relative tolerance, the proposed algorithm yields a near-optimal solution where the network expansion cost stays within the allowed margin. Numerical results demonstrate that the proposed approach efficiently solves the hose-based VPN capacity planning problem within practical computation time.	10.1109/TNSM.2026.3694390
Yan Wang, Xingwei Wang, Hao Lu, Bo Yi, Min Huang, Yue Kou	Intelligent Cross-Domain Data Orchestration in Computing Power Networks: An Attention-Enhanced Multi-Agent Reinforcement Learning Approach	2026	Early Access	Modeling Delays Optimization	Computing Power Networks (CPNs) integrate heterogeneous resources across the cloud–edge–end continuum to support wide-area distributed computational services, but the geographical separation of computation and data makes cross-domain data access a major bottleneck. Intelligent cross-domain data orchestration in CPNs is difficult because replica selection and end-to-end path planning must be jointly optimized under Service Level Agreement (SLA) and resource constraints, while each domain observes only partial congestion and resource information. This paper presents AE-MAAC, an attention-enhanced multi-agent reinforcement learning framework that formulates cross-domain data orchestration as a Multi-Agent Markov Decision Process (MMDP) with a hierarchical composite action space and constraint-aware masking under a centralized-training–decentralized-execution paradigm. An attention-based state representation captures heterogeneous cross-domain topology and resource information, an attention-enhanced centralized critic strengthens inter-domain credit assignment in large-scale settings, and parallel dual-policy actors together with a parallel experience ensemble and prioritized sampling improve training stability in large constrained action spaces. Extensive simulations across three CPN scales show that AE-MAAC achieves the highest average episode reward. In the representative 5×10 network, it reaches an average episode reward of 431.7 with a 94.2% request success rate and a 259.8 ms average end-to-end delay, while yielding a lower multi-objective cost than state-of-the-art RL baselines.	10.1109/TNSM.2026.3696950
Xiaomao Zhou, Zihao Shao, Qingmin Jia, Renchao Xie	ProxyLLM: Augmenting LLMs with Proxy Models for Tool Utilization in Network Service Generation	2026	Early Access	Tools Modeling Large language models	This paper introduces ProxyLLM, a novel framework designed to enhance the tool utilization capabilities of Large Language Models (LLMs) by leveraging an ensemble of smaller, specialized proxy models. Specifically, instead of invoking tools directly, ProxyLLM delegates tasks to these proxy models, each of which is responsible for a distinct domain and equipped with a curated set of relevant tools. Meanwhile, ProxyLLM employs a two-step knowledge transfer mechanism, utilizing data generated by the LLM for knowledge distillation and LLM-guided Deep Reinforcement Learning (DRL) to enhance the decision-making abilities of the proxy models. During the data-driven knowledge distillation process, the introduction of rationales ensures that proxy models maintain a comprehensive understanding of tasks, thereby improving the learning effectiveness. In the DRL learning process, LLM guidance is separately integrated into both the actor and critic learning phases. This ensures consistency in strategy and uniformity in evaluating the action space, which enhances both the efficiency and effectiveness of the learning process. Extensive experiments, including real-world applications such as network service generation in a Computing Power Network (CPN) system, demonstrate that ProxyLLM significantly outperforms existing methods in terms of task accuracy and tool invocation efficiency. The proposed framework offers a promising solution for constructing generalizable, large-scale intelligent agents capable of effectively leveraging diverse tools to solve complex, cross-domain problems.	10.1109/TNSM.2026.3695074
Miguel Catalan-Cid, Joan Josep Aleixendri, Jorge Pueyo, Pau Tomas, Daniel Camps-Mur	COSMO: O-RAN-Based Service Management and Orchestration for Cross-Technology Multi-Tenant Radio Access Networks	2026	Early Access	Radio access networks Regional area networks 5G mobile communication	The evolution toward 6G networks envisions a heterogeneous Radio Access Network (RAN) comprising diverse access technologies, such as private 5G, public 4G/5G, and Wi-Fi, managed by multiple stakeholders. While considerable research effort has been devoted to O-RAN-based frameworks enabling rApp and xApp implementation and validation, few works provide integrated support for cross-technology RAN orchestration, end-to-end multi-tenancy, and a unified subset of SMO functionalities, including Non-RT RIC components. This paper introduces COSMO, a novel RAN Service Management and Orchestration platform designed to support heterogeneous 3GPP (5G NR, LTE) and non-3GPP (Wi-Fi) access networks. COSMO enables cross-technology multi-tenancy, defined as the capability to allow multiple tenants to dynamically share heterogeneous RAN resources with explicit resource allocation guarantees based on Service Level Agreements (SLAs). This is achieved through management primitives that support flexible and on-demand resource allocation. Additionally, the platform includes a cross-technology Non-Real-Time RAN Intelligent Controller (Non-RT RIC) that enables the development of intelligent rApps for closed-loop control and network orchestration. Beyond its architectural design, COSMO improves resource utilization and operational flexibility through unified orchestration of heterogeneous multi-tenant RAN resources. Through prototyping and benchmarking, we demonstrate the effectiveness of COSMO in resource allocation, SLA enforcement, and scalability. In our prototype, the SLA-based rApp reduces SLA violation from approximately 21% to below 10% under dynamic traffic conditions in a heterogeneous RAN deployment including 5G, 4G, and Wi-Fi access networks. Our results confirm that COSMO offers an efficient solution for managing and orchestrating future multi-tenant cross-technology RAN environments.	10.1109/TNSM.2026.3695361
Min Yuan, Qiangsheng Hu, Yuqing Zhu, Hongbing Wang	Prioritizing Critical Tasks in Microservice Clouds: A Dependency-Aware Container Scheduling Framework via Grouping and Degradation	2026	Early Access	Containers Optimization Algorithms	Containerized deployment serves as a pivotal technology for enhancing service performance and resource efficiency in large-scale distributed systems. However, guaranteeing the execution quality of critical tasks in resource-constrained scenarios remains a pressing challenge. This paper proposes a critical-task-prioritized container resource scheduling framework. Firstly, a dependency quantification model based on the Genetic Algorithm is designed to calculate the dependency strength between containers. Secondly, a container grouping strategy based on the Seagull Optimization Algorithm is proposed. Upon determining the optimal container group size, containers are grouped based on their dependency strengths. Finally, a service degradation mechanism for non-critical tasks is introduced. By predicting criticality rankings based on historical data, resources are released from non-critical task groups and prioritized for allocation to critical task groups, thereby ensuring the Quality of Service (QoS) of core business operations. Extensive experiments utilizing real-world datasets demonstrate that the proposed framework outperforms traditional algorithms, effectively increasing the successful request volume of critical tasks and reducing response latency.	10.1109/TNSM.2026.3697106
Mariusz Głąbowski, Sławomir Hanczewski, Damian Kmiecik, Maciej Stasiak, Joanna Weissenberg	Modeling of multi-service queueing systems with traffic overflow	2026	Early Access	Modeling Probability Servers	This article proposes an analytical model of a multi-service hierarchical system with multi-service overflow traffic. To model the primary and secondary resources of this system, the state-dependent queue service discipline was used. In order to model the secondary resources with the dedicated queue for overflow traffic, the Hayward’s approach was generalized and applied. To evaluate its accuracy, the results of analytical modeling were compared with the data obtained during the simulation experiments carried out in the study. Both the data presented in the article and the results obtained by the present authors in numerous comparative studies clearly indicate that the proposed model makes it possible to evaluate the values of the blocking probability with the accuracy that provides its reliable practical application at the stage of network dimensioning. The overflow mechanism has particular significance in networks with limited resources, such as mobile networks.	10.1109/TNSM.2026.3696894