The advent of 6G networks necessitates fundamental advancements in Radio Access Network (RAN) slicing to accommodate unprecedented service demands with highly dynamic traffic patterns. Existing AI-driven approaches for RAN slicing face critical limitations: they typically require model retraining when slice configurations change, lack scalability for concurrent slice management, or overlook the significant computational overhead from frequent resource reconfigurations. This paper addresses these significant challenges by introducing a novel RAN Partially Observable Stochastic Game (RAN-POSG) framework that enables parallel decision-making while effectively incorporating global resource constraints. Firstly, we propose an Adaptive Retention Framework (ARF) that strategically balances stability and adaptability through an innovative Conditional Accumulative Reward mechanism, significantly reducing reconfiguration overhead while maintaining high Service Level Agreement (SLA) compliance. Our proposed Statistic-Field Deep Truncated Monte Carlo (SF-DTMC) algorithm leverages multi-agent reinforcement learning where agents make independent yet coordinated decisions, ensuring both flexibility with varying slice counts and scalability under large-scale networks. Lastly, a custom-built high-fidelity RAN slicing simulator based on NS-3 simulator with PettingZoo interfaces is developed for comprehensive evaluations across multiple test scenarios. The simulation results demonstrate the significant advantages of the proposed framework, including superior user satisfaction, resource utilization, and lower resource reconfiguration overhead compared to state-of-the-art methods, representing a crucial advancement toward Native-AI in 6G networks.

Network Function Virtualization (NFV) often suffers from unexpected failures in some mission-critical applications due to the reliability-agnostic deployment of Virtual Network Functions (VNFs), thus achieving suboptimal service performance. To cope with this issue, we propose a Reliability-aware VNF service Provisioning (RVFP) approach for NFV-based networks via Multi-Agent Deep Reinforcement Learning (MADRL), where each VNF is hosted and backed up in appropriate hardware with reliability guarantees. Specifically, a novel MADRL-based framework for VNF placement is designed with two alternating optimization objectives, namely maximizing reliability and minimizing VNF instance failure probability, to incorporate diverse objective landscapes and encourage broader exploration within the solution space. Furthermore, two agent allocation strategies are developed to alternately arrange the tasks of collaborative agents for reliability-aware placement of VNFs in a parallel and cooperative manner. Besides, the newly-designed prioritized experience sampling, built on reward and sampling frequency, is used to better exploit valuable experiences for faster model training. Extensive simulation results obtained from various scenarios show that RVFP can significantly improve placement reliability and service acceptance ratios for VNF deployment compared with the state-of-the-art approaches.

A large number of distributed applications necessitate accurate network distance, for example, in the form of delay or latency, to ensure the Quality of Service (QoS). Due to high network measurement overhead and severe traffic congestion, network distance prediction has been introduced, instead of direct network measurements, to infer the unknown network distance with the partial measurements. However, most existing efforts neglect to fully capitalize on the potential latent factors, such as spatial correlations, long-existing temporal results and multi-rule exploration fusion, to achieve better accuracies with quicker convergence. To fill this gap, in this paper, we propose an Accurate Prediction of Network Distance (APND) solution via Federated Deep Reinforcement Learning (FDRL), which has four novel features distinguishing from the previous work. Firstly, a local feature-based matrix with low rank is established in each network cluster, referring to a set of neighbor nodes, to represent the potential spatial correlations among reachable node-pairs. Secondly, the parallel FDRL-based matrix factorization with multi-rule exploration fusion is introduced into APND and executed in all local clusters to minimize prediction errors and accelerate learning convergence. Thirdly, the long-existing learning experience is designed for local model training via Deep Reinforcement Learning (DRL) with rapid convergence. Fourthly, following the real-world routing paths, the cross-domain network nodes are simultaneously classified into adjacent clusters, built on the spatial correlations among them, and their coordinates will be further refined with error-based and average-based policies. Extensive experiments built on available real-world datasets illustrate that APND can accurately predict network distance compared with state-of-the-art approaches at the moderate computing cost.

In the era of big data, network data increase rapidly in a distributed manner, giving birth to the network big data. Network big data with the extra features such as distributed and decentralized data collection and storage, distributed and parallel data processing, more complex and evolving relationships among data, and heterogeneous data representation, pose opportunities together with challenges to the traditional network analysis algorithms. A hopeful solution is combining machine learning techniques with network big data analysis. In this paper, we proposed a novel time series clustering method which effectively combines machine learning techniques with network big data analysis for fault diagnosis task based on network logs. Verification experimental results on classic HDFS dataset demonstrate the outstanding performance of the proposed method.

In traffic-based tracking scenes, acknowledging category diversity and visual field motion is pivotal. This leads tracking models to prioritize multi-category feature learning, motion analysis, and extended time series modeling rather than singularly focusing on attribute acquisition and background modeling. With this goal in mind, we construct a tracking model by integrating re-identification branches into a lightweight detection model. Simultaneously, we establish an inter-frame spatio-temporal attention mechanism using the Feature Transfer Module. This mechanism aligns the current frame features with the corresponding local features of the previous frame, which can bolster feature representation and downstream task performance. Experimental results on the traffic dataset validate the efficacy of the proposed approach in addressing challenges such as occlusion and deformation within traffic scenes. The proposed method attains a significant 31.5 MOTA and 35.7 AP on the BDD100K dataset with random data selection.

Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network management due to virtualized infrastructure and stringent quality-of-service requirements. Digital twin (DT) technology paves a way for achieving cost-efficient and performance-optimal management, through creating a virtual representation of slicing-enabled networks digitally to simulate its behaviors and predict the time-varying performance. In this article, a scalable DT of network slicing is developed, aiming to capture the intertwined relationships among slices and monitor the end-to-end (E2E) metrics of slices under diverse network environments. The proposed DT exploits the novel graph neural network model that can learn insights directly from slicing-enabled networks represented by non-Euclidean graph structures. Experimental results show that the DT can accurately mirror the network behaviour and predict E2E latency under various topologies and unseen environments.

With the explosive growth of multimedia applications, heterogeneous cellular networks (HetNets) are widely deployed to meet the increasingly impressing demands on communication capacity. However, supporting the massive real-time traffic generated by always-on multimedia applications in HetNets causes enormous energy consumptions. The tradeoff between energy consumptions and profits of service providers while maintaining satisfied quality-of-service (QoS) has become a significant objective. To this end, this paper proposes a novel framework of power rationing in HetNets to achieve maximal profit and guaranteed service performance. A dynamic power rationing strategy that employs Tullock contest is developed to model the power control of multiple cells into a game and solve the contradiction of profits and energy consumption as the tradeoff between QoS and cost. Two principal challenges in gaming, incomplete information, and the curse of dimensionality are resolved by the designed virtual repeated game that adopts the Monte-Carlo method and particle swarm optimization (PSO) to obtain the Nash equilibrium. The equilibrium of power rationing balances the energy consumptions and profits of cells, which ensures the optimal solution for the multimedia service providers and HetNets operators. The experimental results demonstrate that the developed model can serve as an efficient tool for power rationing in multimedia HetNets.

Network function virtualization (NFV) has been considered as a promising technology for future Internet to increase the network flexibility, accelerate the service innovation, and reduce the Capital Expenditures and Operational Expenditures costs through migrating network functions from dedicated network devices to commodity hardware. Recent studies reveal that although this migration of network function brings the network operation unprecedented flexibility and controllability, NFV-based architecture suffers from serious performance degradation compared with traditional service provisioning on dedicated devices. In order to achieve a comprehensive understanding of the service provisioning capability of NFV, this paper proposes a novel analytical model based on Stochastic Network Calculus (SNC) to quantitatively investigate the end-to-end performance bound of the NFV networks. To capture the dynamic and on-demand NFV features, both the non-bursty traffic, e.g., the Poisson process, and the bursty traffic, e.g., the Markov Modulated Poisson Process, are jointly considered in the developed model to characterize the arriving traffic. To address the challenges of resource competition and end-to-end NFV chaining, the property of convolution associativity and leftover service technologies of SNC are exploited to calculate the available resources of the Virtual Network Function nodes in the presence of multiple competing traffic and transfer the complex NFV chain into an equivalent system for performance derivation and analysis. Both the numerical analysis and extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model. Results demonstrate that the analytical performance metrics match well with those obtained from the simulation experiments and numerical analysis. In addition, the developed model is used as a practical and cost-effective tool to investigate the strategies of the service chain design and resource allocations in the NFV networks.

Content caching is a promising approach in edge computing to cope with the explosive growth of mobile data on 5G networks, where contents are typically placed on local caches for fast and repetitive data access. Due to the capacity limit of caches, it is essential to predict the popularity of files and cache those popular ones. However, the fluctuated popularity of files makes the prediction a highly challenging task. To tackle this challenge, many recent works propose learning based approaches which gather the users' data centrally for training, but they bring a significant issue: users may not trust the central server and thus hesitate to upload their private data. In order to address this issue, we propose a Federated learning based Proactive Content Caching (FPCC) scheme, which does not require to gather users' data centrally for training. The FPCC is based on a hierarchical architecture in which the server aggregates the users' updates using federated averaging, and each user performs training on its local data using hybrid filtering on stacked autoencoders. The experimental results demonstrate that, without gathering user's private data, our scheme still outperforms other learning-based caching algorithms such as m-epsilon-greedy and Thompson sampling in terms of cache efficiency.

Information-centric networking (ICN) is an emerging paradigm that decouples content from the host to achieve fast and cost-efficient communication and content distribution in the future Internet. A key feature of ICN is the deployment of ubiquitous in-network caching to speed up service delivery and improve network resource utilisation. ICN caching has been widely studied in terms of caching strategies and caching performance. However, the economic aspect of ICN has received marginal consideration so far, although it is vital to understand the potential cost- efficiency of ICN before its wide deployment in service provider network. To address this issue, we propose a cost-aware caching scheme to study the Quality-of-Service (QoS) and cost of ICN and investigate the inner association between them. Two new models are designed to characterise the cost and QoS of ICN with arbitrary topology under heterogeneous bursty content requests. A multi- objective evolution algorithm is adopted to find the optimal cache resource allocation. Numerical results show the effectiveness of the proposed scheme in achieving cost- efficiency and QoS guarantee in ICN caching.

Software-Defined Networking (SDN) is an emerging architecture for the next-generation Internet, providing unprecedented network programmability to handle the explosive growth of big data driven by the popularisation of smart mobile devices and the pervasiveness of content-rich multimedia applications. In order to quantitatively investigate the performance characteristics of SDN networks, several research efforts from both simulation experiments and analytical modelling have been reported in the current literature. Among those studies, analytical modelling has demonstrated its superiority in terms of cost-effectiveness in the evaluation of large-scale networks. However, for analytical tractability and simplification, existing analytical models are derived based on the unrealistic assumptions that the network traffic follows the Poisson process, which is suitable to model nonbursty text data, and the data plane of SDN is modelled by one simplified Single-Server Single-Queue (SSSQ) system. Recent measurement studies have shown that, due to the features of heavy volume and high velocity, the multimedia big data generated by real-world multimedia applications reveals the bursty and correlated nature in the network transmission. With the aim of capturing such features of realistic traffic patterns and obtaining a comprehensive and deeper understanding of the performance behaviour of SDN networks, this article presents a new analytical model to investigate the performance of SDN in the presence of the bursty and correlated arrivals modelled by the Markov Modulated Poisson Process (MMPP). The Quality-of-Service performance metrics in terms of the average latency and average network throughput of the SDN networks are derived based on the developed analytical model. To consider a realistic multiqueue system of forwarding elements, a Priority-Queue (PQ) system is adopted to model the SDN data plane. To address the challenging problem of obtaining the key performance metrics, for example, queue-length distribution of a PQ system with a given service capacity, a versatile methodology extending the Empty Buffer Approximation (EBA) method is proposed to facilitate the decomposition of such a PQ system to two SSSQ systems. The validity of the proposed model is demonstrated through extensive simulation experiments. To illustrate its application, the developed model is then utilised to study the strategy of the network configuration and resource allocation in SDN networks.

Carrier Aggregation (CA) has emerged as a promising technique for Long-Term Evolution Advanced (LTE-A) wireless communications to satisfy the ever-increasing bandwidth requirements. However, this technical envisagement puts forward new challenges on radio resource allocation such as serious unbalanced loads among different Component Carriers (CCs). To alleviate this problem, a novel QoS-aware resource allocation scheme, termed as Cross-CC User Migration (CUM) scheme, is proposed in this paper to support real-time services, taking into consideration the system throughput, user fairness and QoS constraints. The experiment results show that the proposed scheme outperforms the well-known Two-Level scheduling scheme in terms of packet loss probability, average queue length and throughput per user.

The rapid development in wireless technologies and multimedia services has given rise to new requirements for the Internet, such as supporting billions of mobile devices and transmitting huge amount of multimedia content in real time. Content-Centric Networking (CCN), a future Internet architecture for efficient content dissemination, has been attracting ever-increasing attention from both academia and industry. In this paper, a new analytical model is developed as a cost-effective tool to investigate the performance of caching in CCN under bursty content requests. The accuracy of the model is validated through comparing the analytical results with those obtained from the extensive simulation experiments. As an example of its applications, the analytical model is used to investigate the effects of the cache size, content size, and bursty content requests on the cache hit ratio in CCN.