Future wireless networks, such as 6G, are envisioned to deliver ultra-reliable, high-quality services with ultra-low latency and dynamic connectivity across heterogeneous environments, driving the adoption of edge–cloud collaborative architectures. Within this paradigm, container-based microservices, with their lightweight, modular, and portable characteristics, offer an effective foundation for scalable and adaptive service provisioning in heterogeneous wireless networks. The layered architecture of microservices facilitates efficient resource management through layer scheduling and caching. However, dynamic service requests and diverse container layers pose major challenges for layer-aware service provisioning in future wireless environments. These includetime-exceeded offline service provisioning, tangled microservice orchestration, andlayer cache redundancy. To address these challenges, we propose Tri-Ring, an asynchronous online provisioning framework for future wireless networks, empowered by large AI models (LAMs). The framework optimizes request dispatching, orchestration, and layer updates across three timescales. At the small timescale, we formulate request dispatching as a linear programming (LP) subproblem. At the medium timescale, the estimator-assessor algorithm manages microservice orchestration, where a diffusion-enhanced prediction model serves as the estimator to predict layer caching strategies. Moreover, submodular optimization serves as the assessor to determine deployment and scheduling. At the large timescale, we introduce the age of layer (AoL) to guide the pruning of infrequently accessed cached layers to reduce storage overhead. Comprehensive evaluations on real-world datasets demonstrates that Tri-Ring outperforms existing baselines, increasing utility by 44.78%, reducing microservice startup time by 78.64%, and optimizing storage resources by 36.38%.

With the rapid advancement of mobile technologies, including self-driving cars and drones, the deployment of mobile software has become increasingly complex. In this context, virtualization plays a pivotal role by simplifying service deployment through containers and enabling container orchestration plat forms to efficiently manage an expanding number of container clusters. This is achieved by leveraging standardized interfaces and minimizing resource optimization overhead. However, the use of distributed servers in mobile edge clusters introduces several challenges, such as bandwidth limitations, network performance fluctuations, and resource constraints, which complicate deployment in these dynamic and resource-constrained environments. In this paper, we rethink the layer-based structure, a fundamental container design, and analyze the challenges and potential of real edge platform traces. Consequently, we propose BREAK, an acceleration middleware for efficient container deployment. With the primary insight of enhancing layer-reuse and deriving benefits from it, we develop a co-design approach centered on layer structure for efficient deployment, ensuring backward compatibility: (i) a container image refactoring solution that optimizes efficiency while preserving the stack-of-layers structure, (ii) distributed shared layer-stack caches, dynamically optimized for collaborative container deployment among mobile edge clusters, (iii) a customized Kubernetes (K8s) scheduler extending awareness of network performance, disk space, and container layer cache for container placement, and (iv) a tailored storage-driver of the standard container runtime for efficient layer extraction. Results indicate that BREAK accelerates the deployment process by up to 2.1× and reduces redundant image size by up to 3.11× compared to the state-of-the-art approach.

The rapid development of Artificial Intelligence Generated Content (AIGC) technology has led to a strong demand for elastic computing resources. The crowdsourced edge cloud system builds a flexible resource pool by integrating heterogeneous idle servers and even personal devices to meet the dynamic computing requirements of AIGC services. The system relies on the serverless architecture to realize the dynamic scheduling of resources, which needs to trade off resource benefits and energy consumption costs to improve the overall social welfare, resource efficiency, and environmental sustainability. However, challenges such as unfair resource pricing, dynamic resource availability, and the complexity of strategy optimization remain unresolved for green and efficient resource management. In this paper, we propose a resource management framework named ReFluid. We introduce a game-theoretical pricing model to ensure fair pricing, a fluid model-based analysis for promoting a more sustainable management of computing resources, and a diffusion-based optimization mechanism to enhance model stability and adaptability. The evaluation shows that ReFluid significantly improves average social welfare and reduces energy consumption.

Network slicing, which enables multiple services to coexist on the shared physical infrastructure, has been recognized as a key technology in networks. Leveraging the high processing capability of programmable data plane (PDP) devices, network slicing within PDPs can lead to efficient traffic isolation, priority management, and significantly reduced forwarding delays. However, existing inflexible and coarse-grained network slicing orchestration approaches struggle to address the challenges posed by diversified slicing scenarios and the constrained resources of PDP devices. In this paper, we propose AtlasPro, a framework for network slicing orchestration in PDPs, where each network slice is regarded as an independent Service Function Chain (SFC) routing entity. Thus, it enables the allocation of physical resources at a finer granularity. Additionally, we introduce a novel heuristic approach, the Chained Hyper-Generative Algorithm, which jointly optimizes Virtual Network Function (VNF) deployment and routing costs, minimizing total cost while meeting the performance requirements of all network slices. We implement our framework using BMv2 switches in a Mininet environment and evaluated our algorithm. Compared to existing solutions, our framework and algorithm reduce the number of VNF deployments, lower routing delays by up to 34.2 %, and cut overall costs by up to 48.5%.

Web 4.0 is characterized by decentralized intelligence and blockchain integration, which introduces significant challenges in congestion management for software-defined networking (SDN). Traditional reinforcement learning (RL)-based approaches encounter inefficiencies due to limited adaptability to decentralized and delayed online learning capabilities. To address these issues, we propose an Active Inference-based Reinforcement Learning (AIRL) framework that integrates generative modelling with RL for enhanced decision-making in congestion control. By leveraging blockchain-enabled secure model trading and predictive intelligence, AIRL ensures adaptive policy optimization while maintaining transparency and trust in decentralized network environments. The proposed method demonstrates substantial improvements in delay reduction, packet loss, and efficient utilization of network resources under various dynamic scenarios.

In the era of Artificial Intelligence of Things (AIoT), Unmanned Aerial Vehicles (UAVs) are increasingly deployed in emergency scenarios to provide intelligent environmental sensing and rapid situational awareness. However, the massive raw sensory data generated by UAVs can easily overwhelm bandwidth-limited communication infrastructures, undermining the timeliness and reliability of disaster response. To address this challenge, we propose a cloud-edge-end collaborative semantic communication(SemCom) framework that enables UAVs, mobile edge vehicles, and cloud-based command centers to jointly deliver resilient and scalable AIoT services. In this architecture, UAVs perform real-time environmental sensing, vehicles act as mobile edge nodes for semantic filtering and resource coordination, while the cloud executes global decision-making. To optimize semantic data trading under incomplete information, we integrate matching theory with a multi-armed bandit (MAB) model and design a Matching-UCB algorithm, which allows UAVs and vehicles to dynamically learn preferences through repeated interactions. Theoretical analysis proves that Matching-UCB achieves sub-linear regret and guarantees stable matching outcomes. Simulation results further demonstrate that our semantic-aware AIoT framework reduces transmission load by more than 99% and achieves optimal matching with the highest social welfare and lowest regret among benchmarks.

Edge devices increasingly require efficient, on-device intelligence for diverse applications in IoT networks. In order to bring the advanced capabilities of large foundation models directly to the point of data generation, there is a growing interest in deploying these models on edge devices. However, due to their inherent resource constraints and the diverse, heterogeneous nature of the data and tasks they encounter, deploying large foundation models directly on these devices remains a significant challenge. To address these challenges, we propose a novel Federated Learning Fine-Tuning (FLFT) framework that leverages adapter-based fine-tuning with a similarity-driven selection mechanism, enabling personalized model adaptation with minimal computational overhead. Furthermore, we introduce the Diffusion-based Soft Actor-Critic (FTFL2DSAC) algorithm, which optimizes real-time resource allocation by balancing energy consumption and latency across heterogeneous edge devices. Our experiments on CIFAR-100 using a pre-trained multimodal model demonstrate that FLFT achieves 82.5% accuracy while reducing model parameters by 14%, outperforming baseline methods with faster convergence and enhanced stability in complex environments.

Large language models (LLMs) have emerged as a transformative advancement in artificial intelligence (AI). To fully leverage their potential, Docker containers, serving as a lightweight, portable, and isolated framework, facilitate the seamless deployment of LLM-based applications. However, the deployment of containerized AI microservices faces challenges such as heavy network loads, delayed image loading, and redundancy. In this paper, we introduce MetaPipe, an innovative incremental deployment approach for containerized AI microservices in edge cloud environments. MetaPipe aims to optimize startup times through a dynamic workflow that incorporates proactive layer pre-fetching and reinforcement layer re-scheduling. The proactive pre-fetching reduces service deployment time through layer caching prediction and pre-scheduling before requests arrive, while the reinforcement re-scheduling addresses inaccuracies by dynamically adjusting layer scheduling strategies after requests arrive. Extensive experiments on realworld datasets show that MetaPipe significantly outperforms traditional methods, achieving 83.58% reduction in initialization startup time and 85.58% reduction in cold startup time. These results highlight its effectiveness in enhancing the performance of AI microservices deployment within edge cloud environments.

The rapid growth of distributed service infrastructures has promoted container-based deployment as a lightweight and flexible approach for large-scale service delivery. To enhance the trustworthiness of service deployment, blockchain has been incorporated into container networks as a decentralized trust layer. However, most existing blockchain solutions still rely on coarse-grained trust models that neither capture the evolution of node or layer credibility nor incorporate the layered structure of container images into deployment decisions, which may lead to services being deployed on low-reputation nodes and to the propagation of untrusted layers across the network. To address these limitations, this paper proposes ReTainer, a reputation-aware containerized service deployment framework in blockchain networks. We formulate the deployment problem as a joint optimization and decompose it into a linear-programming request routing subproblem and a service orchestration subproblem, which simultaneously covers service deployment, layer precaching, and layer re-scheduling. A hierarchical trust model captures the temporal evolution of node-level and layer-level credibility, and the resulting reputation priorities are used to select the top-k trustworthy layers for pre-caching. We then formulate the service activation and layer re-scheduling as a submodular optimization problem over a p-extendible system. Experimental evaluations demonstrate that ReTainer improves utility by up to 13.56% and reduces startup latency by 43.31% compared with existing deployment schemes.

The escalating demands for Artificial Intelligence-generated content (AIGC) services greatly require computing resources. The edge-cloud market offers an effective solution for AIGC services by integrating, managing, and trading distributed computing resources. Within this novel service market, participants contribute idle resources to support AIGC services to earn income, creating a more flexible market environment. Meanwhile, the generation quality and computing resource requirements of AIGC services are related to input prompts. Therefore, this relationship introduces new challenges, such asthe information uncertainty in input prompts, the inability to model resource continuity, and high-dimensional complexity for optimization.In this paper, we propose a resource fluid equilibrium management strategy for supporting AIGC services within edge-cloud market, termedFluE. To address the challenge of information uncertainty in user prompts, we measure the content value of AIGC prompts by information entropy and introduce a redundancy reduction approach to focus on meaningful information in prompts. To tackle the challenge of the inability to model the continuity provision of computing resources, we utilize the fluid model to ensure seamless resource provision and facilitate a more balanced management of computing resources. To address the challenge of high-dimensional complexity of strategy optimization, we develop a diffusion-based algorithm namedReDiffto reconstruct the target strategy distribution and generate precise and effective optimization decisions. We evaluate our proposed scheme under a dynamic resource provisioning environment. Based on the DiffusionDB dataset, the publicly available real trace of AIGC service prompt, ourReDiffalgorithm achieves up to 69.8% and 77.4% improvements in average social welfare compared to LySAC and CD-PPO, respectively.

The presence of Artificial Intelligence Generated Content (AIGC) has garnered widespread interest. AIGC enables content creation by analyzing big data, leveraging the capabilities of extensive AI models, and substantial AI computing. Computing power networks (CPNs) represent an excellent approach for offering pervasive AI computing resources to AIGC. However, these characteristics have posed unprecedented challenges to the CPNs helped AIGC, including the uncertainty of prompts’ information value, the inability to model the continuity of computing resources, and the incapacity to represent complex multi-dimensional spaces. In this paper, we propose a computing resources equilibrium strategy based on the fluid model for AIGC helped by CPNs, namely FluE. This mechanism obtains information entropy by constructing an AIGC prompt tree to measure the information value of AIGC prompts. In addition, we model the continuity of computing resources by the fluid model. A fluid-stopping equilibrium strategy is formulated to obtain the average fluid level of computing resources based on the Laplace-Stieltjes transform. To solve the equilibrium strategy, we develop a diffusion-based algorithm for FluE to adjust the fluid policy dynamically to maximize resource rewards. Finally, the evaluations demonstrate improvements in average social welfare.

Web 3.0 pursues the establishment of decentralized ecosystems through blockchain technologies, driving digital transformation in commerce and governance. With consensus algorithms and smart contracts grounded in cryptographic technologies, Web 3.0 enables secure and transparent digital services, such as digital identity, asset management, decentralized autonomous organizations (DAOs), and decentralized finance (DeFi), fostering integration between digital and physical economies. As quantum devices rapidly advance, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with enhanced quantum computing and communication capabilities. This article provides a comprehensive overview of blockchain-based Web 3.0, examining its quantum and postquantum advancements from two key perspectives. On the one hand, postquantum migration methods and quantum-resistant signatures offer robust solutions to safeguard blockchain against quantum threats. On the other hand, quantum and postquantum encryption and verification algorithms boost blockchain performance, creating a decentralized, secure, and value-driven system. Additionally, we outline potential applications of quantum blockchain and offer guidance for implementation within the Web 3.0 ecosystem. Finally, we discuss future directions for developing a provably secure and decentralized digital ecosystem.

The development of the mobile Metaverse has garnered increasing attention in the next-generation Internet, fueled by the rapid advancements of mobile Internet, communication, and computing technologies. With the resource limitations faced by mobile Metaverse users (MUs), the mobile Metaverse market is flourishing. This market enables MUs to access high-quality immersive experiences by trading resources with Metaverse service providers (MSPs) across geographically distributed resource pools. However, the mobile Metaverse market still faces several challenges, includingthe hierarchical mobile Metaverse service structure, temporal dependencies, and heterogeneous incentive mechanisms. To address these problems, this paper proposes a dual-level resources trading approach for mobile Metaverse based on blockchain. This approach employs a dual-level structure consisting of resource provisioning and heterogeneous auction mechanisms. Specifically, we formulate the resource provisioning as a temporal-dependent average delay minimization problem at the low level. To solve this low-level problem, we introduce a novel algorithm calledLyDif, which leverages Lyapunov optimization techniques and diffusion models. At the high level, we propose a price-guided double dutch auction (PG-DDA) mechanism to match heterogeneous resources and determine pricing strategies. The PG-DDA smart contract is deployed on a consortium blockchain platform, facilitating resource trading management and transaction monitoring. Based on a real trace of edge-cloud service requests, our experimental results demonstrate the effectiveness of our proposed scheme in achieving optimal latency and social welfare.

Edge computing has evolved to enable mobile applications to run in an efficient and cost-effective manner at explosive-growing edge nodes. Under this paradigm, a new business resource trading market has emerged to provide edge-cloud services, offering a convenient way for mobile users to obtain resources from distributed computing power providers (CPPs). Blockchain, as a promising technology, provides a reliable platform for multi-party resource transactions (TXs), enabling secure and reliable computing services. Notably, the distributed CPPs not only offer mobile services but also act as blockchain nodes to maintain the stability of TXs. In this case, there exist certain bottlenecks in the blockchain-enabled edge-cloud resource market, such as limited scalability, inefficient resource allocation, and large system cost. In this paper, assisted by the permissioned blockchain, we study the fundamental problem of resource allocation by minimizing the system cost to handle mobile services and blockchain TXs in parallel. We first partition the Practical Byzantine Fault Tolerant (PBFT) consensus by hierarchical sharding to improve the scalability and ensure the security of the blockchain system. Next, based on the optimal sharding strategies, we formulate the parallel resource allocation as a multi-scale Lyapunov optimization problem, and develop a dual-alternation actor-critic with an attention mechanism (DA3C) algorithm to solve it. We evaluate the performance of theParamartusing trace-driven experiments. Simulation results demonstrate the superiority of our proposed framework as compared with the benchmark algorithms.

The presence of Metaverse has elicited escalating attention in the next-generation Internet, followed by a large number of computationally intensive tasks, such as augmented reality, virtual reality, artificial intelligence-generated content (AIGC) applications, etc. With the popularity of mobile communication technology, mobile metaverse is becoming increasingly widespread. The resources required for these applications are rapidly growing in parallel with increasing demands from mobile Metaverse users (MUs), putting pressure on Metaverse service providers (MSPs) with limited resources, especially in mobile computing scenarios. Inspired by the burgeoning communication and computing technologies, the mobile Metaverse market between mobile MUs and MSPs is developing vigorously. However, there still remain numerous challenges in this market, including hierarchical mobile Metaverse structure, temporal dependencies, as well as heterogeneous incentive. In this paper, we propose a bi-alternating resource provisioning and heterogeneous auction approach for mobile Metaverse, named Bi-Meta. At the high level, resource provisioning is formulated as a Lyapunov problem minimizing average delay, solved by a novel bi-level based generative adversarial network, i.e., BiGAN. At the low level, a price- guided double dutch auction (PG-DDA) mechanism is presented for heterogeneous resource matching, with the designed PG- DDA smart contract. Based on the realistic edge-cloud company's traces, the experimental results verify that our proposed scheme achieves optimal latency and social welfare.

Driven by the burgeoning growth of the Internet of Everything and the substantial breakthroughs in deep learning (DL) algorithms, a booming of artificial intelligence (AI) applications keep emerging. Meanwhile, the advance in existing computing paradigms, i.e., cloud computing and edge computing, provide assorted computing solutions to satisfy the increasingly high requirements for ubiquitous AI services. Nevertheless, there are some non-trivial issues in the computing frameworks, including the underutilization of computing power, the self-interest of computing-power trading mechanism, and the inefficiency of AI services management. To tackle the above issues, we propose a computing-power trading framework based on blockchain, also named AI-Bazaar. In AI-Bazaar, the AI consumers play multiple roles and feel free to contribute the computing power rented from the computing-power provider (CPP) for blockchain mining and AI services. Accordingly, we formulate the computing trading problem as a Stackelberg game. Based on the win or learn fast principle (WoLF), we design a profit-balanced multi-agent reinforcement learning (PB-MARL) algorithm to search the AI-Bazaar equilibrium, while finding the balanced profits for AI consumers and CPP. Numerical simulations are carried out to demonstrate the satisfactory performance and effectiveness of the proposed framework.

As the footing stone of artificial intelligence (AI), ubiquitous computing resource is beginning to receive interest. With this trend, a new form of edge-cloud service market dedicated to collecting, trading, and scheduling computing resources is rising. The computing participants in the service market, as providers and demanders of computing resources, are becoming more diversified and open. As such, the intriguing economic phenomenon and the circulation mechanism have emerged. These bring inherent challenges, such as a volatile market, the ossification of pricing, isolation, and inefficiency. In this article, we propose a novel space-time-request trading framework for the edge-cloud service market, namelyCompCube. To ensure stability,CompCubeadopts the dual-circulation futures-spot trading method, including space-time dynamic pricing in the macro-cycle, request intention conversion, and resource matching in the micro-cycle. Among this, an incomplete information game model is designed to determine the long-term trading pricing in the macro-cycle. Then, to tackle the indicator isolation problem due to the inconsistency between the user's requests and the computing-power provider's (CPP’s) resources, we focus on minimizing the rental cost of computing resources while satisfying diverse service level agreements (SLA) of users. To address this problem, a spatiotemporal scale Lyapunov optimization and an alternating actor-critic algorithm, A2SC, are developed. Besides, in the micro-cycle, a discriminatory double auction helps to determine the computing resource matching results efficiently and impersonally. We evaluate theCompCubeof the A2SC algorithm with realistic datasets. Compared to other baselines, i.e., DYRECEIVE, Price Preferred, and Random, A2SC reduces the average rental cost by 30.45%, 5.74%, and 17.57%, respectively. Furthermore,CompCubecan improve SLA satisfaction, as well as promote resource efficiency and social welfare compared with the traditional methods.

Energy Internet (EI) is developing and booming rapidly with the increase of distributed energy resources, which is beneficial to address the severe condition of industrial energy. However, there are inevitable credit crises and utility optimization challenges in EI that need to be settled. In this article, we propose a blockchain-assisted software defined energy Internet (BSDEI), where a distributed energy market smart contract is designed to ensure transactions executed reliably and participants’ accounts dealt accurately. In order to jointly optimize the utilities of operators, retailers, and industrial prosumers in BSDEI, we formulate the whole trading process as a three-stage Stackelberg game, with the proof of existence and uniqueness for the Stackelberg equilibrium. Then, we design a hierarchical distributed policy gradient algorithm to solve the Stackelberg game under incomplete information. We implement a blockchain-based industrial energy trading system using a middleware platform. The smart contract is deployed on the consortium blockchain, providing website interfaces for participants to operate. Furthermore, we conduct experiments for analyzing economic benefits. Our system prototype demonstrates the feasibility of BSDEI and the algorithm exceeds about 18% in total mean reward than comparing algorithms.

In microgrid, peer-to-peer (P2P) electricity trading has quickly ascended to the spotlight and gained enormous popularity. However, there are inevitable credit problems and system security problems. Besides, the current model in the electricity trading system cannot balance the utilities of multiple trading entities. In this paper, we propose a blockchain-based distributed P2P electricity trading system. We define elecoins as currency in circulation within our trading system. In order to jointly optimize the utilities of both parties in the elecoins trading, we formulate the elecoins purchasing problem as a hierarchical Stackelberg game. Then, we design a distributed multi-agent utility-balanced reinforcement learning (DMA-UBRL) algorithm to search the Nash equilibrium. Finally, we factually build a blockchain system with a blockchain explorer and deploy an electricity trading smart contract (ETSC) on Ethereum, with a website interface for operating. The numerical results and the implemented realistic system show the advantages of our work.

Abstract For industrial fault diagnostics, many model‐based fault diagnosis approaches have been proposed so far and some of them have been put into practice. However, for modern complex processes, owing to the variable nature of faults and model uncertainty, no single method can diagnose all faults and meet different contradictory criteria. In this paper, the importance of integration of different fault detection and isolation schemes in a generic problem‐solving environment is emphasized. A service‐oriented architecture for the integration is proposed, based on Grid technologies. As an engineering implementation, a decision support system for the gas turbine engine fault diagnosis is presented and some deployed services are discussed. Copyright © 2006 John Wiley & Sons, Ltd.

Model-based methods are commonly used for fault diagnosis. Many model-based fault diagnosis approaches have been proposed so far. But for modern complex processes, due to the variable nature of faults and model uncertainty, no single approach can diagnose all faults and meet different contradictory criteria. In this paper, the importance of integration of different fault diagnosis schemes in a common framework is emphasised. A service-oriented architecture for the integration is proposed based on grid technologies. The preliminary implementation of this integration for the gas turbine engine fault diagnosis is discussed.

Software testing, as an important phase in the life cycle of software development, is getting more and more recognition in recent years. While some work has been done in white-box verification, much less work has been none in black-box validation. This paper provides a novel approach to test safety critical software as a black-box. The purpose is to find if the end product meets the requirement and has the expectant attributes. The method is elaborated at the beginning, then an illustration is introduced to demonstrate its feasibility.