In this paper, we establish a two-timescale framework for the joint optimization for the content placement and content delivery problem in satellite-terrestrial edge computing networks (STECN). Our goal is to optimize content placement to improve network performance while ensuring diverse quality of service (QoS) for content delivery. We decouple the problem into two timescales to balance real-time responsiveness and long-term efficiency. Specifically, considering frequent content placement incurs huge traffic cost, we optimize the content placement in order to reduce resource expenses in large timescales. The optimization problem is formulated as an integer linear programming (ILP) problem to improve both traffic efficiency and cache resource utilization. We leverage a heuristic atom search optimization (ASO) approach to address the problem, which yields an optimal strategy with low computational complexity. In small timescales, we model content delivery as a Markov decision process (MDP) to minimize content delivery delays at small timescales while maintaining smooth network traffic. A deep reinforcement learning (DRL) framework is used for policy learning to dynamically adapt to varying network conditions. By considering the correlation between the small and large timescale optimization, we propose a hierarchical solution to jointly address both issues. Finally, extensive simulations confirm the effectiveness and superiority of the proposed scheme.

As an emerging computing paradigm, Computing Power Networks (CPNs) are dedicated to coordinating and managing network resources and computing resources to achieve interconnectivity in computing power perception. Efficient collaborative computing of massive data can be achieved through the scheduling function of CPNs. However, existing scheduling research mainly focuses on selecting network links and computing nodes, lacking consideration for task execution after scheduling, which may degrade the Quality of Service (QoS), leading to widespread failures and significant losses. To address this issue, we design a priority-aware preemptive task scheduling (P2TS) strategy for CPNs to jointly optimize task scheduling and execution in terms of success rate, average processing delay, and load balancing. Specifically, at the execution level, we propose a priority-aware preemptive mechanism (P2M) to optimize post-scheduling task execution. Then, at the scheduling level, we apply deep reinforcement learning (DRL) to optimize the scheduling process supporting the P2M in CPNs. A series of simulations are conducted to demonstrate the superiority of our strategy.

Low Earth Orbit satellite networks play a significant role in providing global ubiquitous services. The application of Inter-Satellite Links (ISLs) has accelerated development in Non-Terrestrial Networks with satellite backhaul. However, ISL-based backhaul does not match the performance of terrestrial fiber links, which makes its impact on end-to-end performance non-negligible. Existing radio resource allocation solutions usually neglect the backhaul performance, potentially failing to meet end-to-end Quality of Service requirements. In this work, we propose ARA-IBA, an access resource allocation scheme with ISL-based backhaul awareness. The satellite network’s backhaul path states, including delay and packet loss rate, are exposed to on-board base stations to enable dynamic resource scheduling. In ARA-IBA, resources are jointly scheduled for Guaranteed Bit Rate (GBR), Delay-critical GBR, and Non-GBR users. For GBR and Delay-critical GBR users, backhaul delay is utilized to optimize end-to-end delay satisfaction. A clustering game is employed to perform fine-grained allocation adjustments. Overloaded Non-GBR users are then scheduled using a pointer network. ARA-IBA optimizes backhaul packet loss rate while maintaining scalability to accommodate varying user numbers. Simulation results demonstrate that the proposed algorithm outperforms conventional methods in terms of users’ delay satisfaction and backhaul packet loss rates.

Digital Twin (DT) technology is a key enabler of the Industrial Internet of Things (IIoT), facilitating predictive control, fault detection, and simulation through high-fidelity virtual replicas of physical assets. To alleviate the significant computational burden on the Cloud Server (CS) of centralized DT services, existing solutions commonly deploy DT modules on edge servers (ESs). However, deploying each DT module at the edge requires intensive resources to support frequent data updates, processing, and analysis; thus, unrestricted module deployment can pose substantial sustainability challenges, particularly when renewable energy availability is limited. To tackle this challenge, we propose a green DT-IIoT architecture and formulate a DT module placement problem that jointly minimizes DT service freshness and carbon emissions, subject to constraints on data synchronization accuracy, computing resources, and storage capacity. Among these, DT service freshness, which indicates real-time responsiveness, and data synchronization accuracy, which reflects the reliability of real-time input data, are two critical metrics affecting DT service quality. Furthermore, we propose a Dueling Double Deep Q-Network (D3QN) based placement algorithm (DDMP), which achieves high performance with a relatively simple structure that is well-suited for rapidly evolving IIoT scenarios. Simulation results demonstrate that our proposed approach enhances DT service freshness while reducing carbon emissions compared with baseline methods.

The exponential increase in connected user devices poses scalability challenges for centralized cloud computing. Mobile Edge Computing (MEC) and Fog Computing alleviate latency by deploying computation and storage resources closer to end-users. However, due to the resource limitations, heterogeneity, and dispersed nature of edge servers, there is a need to jointly optimize service caching and application placement strategies to enhance service quality. Given the widespread use of containerized services at the edge, we propose a distributed caching scheme that allows all edge nodes to cache services at the granularity of container image layers. This collaborative caching approach reduces the real-time latency, bandwidth consumption, and caching costs associated with retrieving and initializing applications. Additionally, to address the variability in application popularity across different edge regions, we model application popularity using a Zipf distribution and construct a multi-slot joint optimization model for caching and deployment decisions based on deployment cost, application startup time, and average delay. We then propose a two-stage optimization method to solve this model, demonstrating through comparison with centralized and P2P models the effectiveness of the proposed approach.

In recent years, the rapid development of the industrial Internet of things (IIoT) has brought new innovation opportunities to the manufacturing industry, but it also faces some major challenges. Currently, the IIoT systems often lack flexibility in sensing capabilities and have rigid communication architectures, resulting in insufficient coordination between different control tasks. In addition, the disconnect between sensing, communication, and computing further limits the system's ability to achieve optimal control, and the lack of intelligent data processing in the system also increases the control cost. In response to these challenges, this paper proposes an intelligent control framework, CISCC, which combines industrial edge computing technology with artificial intelligence (AI) models to achieve a deep integration of sensing, communication, and computing resources in IIoT systems, aiming to jointly optimize the configuration of these resources to improve the overall system performance and reduce control costs. Simulation results show that the CISCC framework can effectively handle resource allocation problems in IIoT systems, thereby better supporting the development of smart manufacturing applications.

In this paper, we propose an innovative architecture for sharing intelligence in low earth orbit (LEO) satellite edge computing networks. Specifically, we adopt the sharing of intel-ligence to satellites via ground stations to improve the response speed of satellites in processing intelligent services. Considering the burden of frequent transmission of intelligent models over unstable ground-satellite links, the satellites share intelligence with each other in the coalition, which greatly reduces the service response delay. In addition, considering the poor generalization of pre-trained intelligent models transmitted by ground stations, we design a model aggregation scheme with differentiated weights. Each coalition appoints a coalition center satellite, tasked with aggregating models and re-sharing them to individual satellites, thereby enhancing model performance. Then, we propose two low-time complexity algorithms to solve the above problems. Finally, the effectiveness and superiority of the proposed schemes are verified through extensive simulations.

In recent years, Low Earth Orbit (LEO) constellation based networks have attracted wide attention from both the academia and the industry. Broadband access and backhaul becomes a typical application for LEO satellite networks. However, the high-speed motion of satellites brings periodic fluctuation of delays, which is a great violation to the Quality of Service (QoS) provisioning. In this work, inspired by the success of Software Defined Networking (SDN), and considering the dynamics of LEO satellite networks, we propose a low-jitter multiple slots routing in LEO satellite networks to provide low-jitter end-to-end delay path computing and control. The proposed multiple slots routing optimization mechanism takes account of the topology shift as well as the dynamic propagation delay across multiple topology snapshots, and thus achieves low-jitter performance. The performance improvement of the proposed mechanism is validated by the simulation, which promises the value of jitter under 30 ms.

With the rise of the computing power network (CPN), which integrate edge computing, cloud computing, and network infrastructure, replicated computing services are increasingly distributed to meet user demands for location-independent, reliable, and low latency services. Service anycast forwarding coordinates distributed service instances by binding them to a unified identifier and dynamically routing requests to the optimal instance. However, challenges such as varying user demand distribution, network complexity, and service instance heterogeneity complicate balanced service forwarding. To address these, we propose an SDN-based service anycast forwarding mechanism for CPN (SA-CPN). In the data plane, a cyclic forwarding queue efficiently maps weighted strategies and selects instances for each service request, improving policy performance. In the control plane, an optimal transport model balances network and computation latency based on service instance capabilities. We further design an optimal transport-based service anycast forwarding algorithm (OTSAF) using Sinkhorn iterations. Our implementation of SA-CPN in a real system shows that OTSAF consistently outperforms four baseline methods across various performance metrics.

With the continuous advancement of network technologies and hardware devices, computation-intensive and latency-sensitive tasks have emerged worldwide, requiring networks to provide extensive coverage, low latency, and robust computing capabilities. Leveraging the global coverage of LowEarth Orbit (LEO) satellites and the flexible resource invocation capabilities of the Computing Power Network (CPN), we propose a Satellite-Terrestrial Computing Power Network (ST-CPN) framework that integrates both strengths. In this framework, tasks can be offloaded to satellites closer to users for processing, ensuring high-quality services anytime and anywhere. However, due to the dynamic nature of the network and the limited resources of individual nodes, efficiently executing complex dependent tasks presents significant challenges. Therefore, we investigate the dependent task offloading problem in the dynamic ST-CPN environment. Considering dynamic changes of topology and available resources caused by satellite mobility, we propose a Time-Space-Varying Resource Graph (TSVRG) to capture the status of the communication, storage, and computation resources. On this basis, given that individual nodes struggle to process dependent tasks, we offload multiple subtasks of a task to different nodes for collaborative processing. In this paper, we model the task as a Directed Acyclic Graph (DAG) and transform the offloading problem into a mapping problem from the DAG to TSVRG. We then introduce a Delay Predictionbased Graph Mapping Algorithm (DPGMA) to address this problem. Simulation results indicate that our scheme achieves better performance than the benchmark schemes.

Industrial Cyber-Physical Systems (CPS) have made significant strides in recent years, driving the future of manufacturing. However, for further advancement in Cloud-Fog Automation (CFA), several challenges remain: rigid sensor sampling, inflexible communication configurations, insufficient coordination between cloud and fog resources, and a lack of integration between sensing, communication, and computing for effective control. To address these issues, this article presents SeCo4, an intelligent control framework for the co-design of sensing, communication, and computing in industrial CPS. The SeCo4 optimization problem is analyzed and divided into two sub-problems: a multi-controller cloud resource competition problem, formulated with a combinatorial auction to enable multi-controller competition for additional cloud resources and improve control performance; and a joint resource optimization problem for sensing, communication, and computing, modeled using a Mixed Integer Programming (MIP) problem to minimize control costs. Given the interdependence of these sub-problems, a hierarchical solution based on the online matching mechanism and the heuristic approach is developed to iteratively find the optimal solution. Finally, extensive simulations demonstrate the effectiveness and superiority of the proposed approach.

Recently, Computing Force Networks (CFNs) have emerged to deeply integrate and flexibly schedule multi-layer, multi-domain, distributed, and heterogeneous computing force resources. CFNs build a resources trading platform between consumers and providers, facilitating efficient resource sharing. Therefore, resources trading is an important issue but it faces some challenges. Firstly, because all kinds of large-scale and small-scale resource providers are distributed in a wide area and the number of consumers is larger compared with edge/cloud computing scenarios, the credibility of consumers and providers is hard to guarantee. Secondly, due to market monopolies by large resource providers, fixed pricing strategies, and information asymmetry, both consumers and providers exhibit a low willingness to engage in resources trading. To solve these challenges, the paper proposes an incentive mechanism for trust-driven resources trading to guarantee trusted and efficient resources trading. We first design a trust guarantee scheme based on reputation evaluation, blockchain, and trust threshold setting. Then, the proposed incentive scheme can dynamically adjust prices and enable the platform to provide appropriate rewards based on providers’ classified types and contributions. We formulate an optimization problem aiming at maximizing the trading platform’s utility and obtaining an optimal contract based on individual rationality and incentive compatible constraints. Simulation results verify the feasibility and effectiveness of our scheme, highlighting its potential to reshape the future of computing resource management, increase overall economic efficiency, and foster innovation and competitiveness in the digital economy.

Background As visual simultaneous localization and mapping (SLAM) is primarily based on the assumption of a static scene, the presence of dynamic objects in the frame causes problems such as a deterioration of system robustness and inaccurate position estimation. In this study, we propose a YGC-SLAM for indoor dynamic environments based on the ORB-SLAM2 framework combined with semantic and geometric constraints to improve the positioning accuracy and robustness of the system. Methods First, the recognition accuracy of YOLOv5 was improved by introducing the convolution block attention model and the improved EIOU loss function, whereby the prediction frame converges quickly for better detection. The improved YOLOv5 was then added to the tracking thread for dynamic target detection to eliminate dynamic points. Subsequently, multi-view geometric constraints were used for re-judging to further eliminate dynamic points while enabling more useful feature points to be retained and preventing the semantic approach from over-eliminating feature points, causing a failure of map building. The K-means clustering algorithm was used to accelerate this process and quickly calculate and determine the motion state of each cluster of pixel points. Finally, a strategy for drawing keyframes with de-redundancy was implemented to construct a clear 3D dense static point-cloud map. Results Through testing on TUM dataset and a real environment, the experimental results show that our algorithm reduces the absolute trajectory error by 98.22% and the relative trajectory error by 97.98% compared with the original ORB-SLAM2, which is more accurate and has better real-time performance than similar algorithms, such as DynaSLAM and DS-SLAM. Conclusions The YGC-SLAM proposed in this study can effectively eliminate the adverse effects of dynamic objects, and the system can better complete positioning and map building tasks in complex environments.

Recently, the advancement of information technologies have accelerated the generation of big data, necessitating substantial computing power. This has spurred the development of Computing Power Networks (CPNs), which can overcome the limitations of computing power isolation. However, CPNs consume significant energy and produce large carbon emissions during big data processing. Therefore, an energy-efficient task scheduling scheme, coupled with the utilization of renewable energy, appears to be particularly necessary. Nevertheless, the interplay between computing power and networks, and the spatiotemporal variations in green CPNs pose a challenge to designing the task scheduling scheme. In this paper, we propose a transferable spatiotemporal task scheduling scheme with a triple selection of CPN nodes, routing paths, and forwarding time of tasks. The scheme can overcome the dynamics of green CPNs, and jointly optimize the energy consumption and carbon emissions with ensuring delay constraints and long-term load balancing. Then, we present a task scheduling algorithm based on improved nondominated sorting genetic algorithm-II (NSGA-II) to solve the problem, and numerical results demonstrate that our scheme is effective in reducing the overall energy consumption and carbon emissions of CPNs.

This paper establishes a dual-timescale framework for joint resource slicing and task scheduling in satellite edge computing (SEC) networks. Specifically, to capture network dynamics and task stochasticity at small timescales, we formulate the task scheduling problem as a Markov decision process (MDP) to minimize task delay, network energy consumption, and packet loss. We design a deep reinforcement learning-assisted task scheduling (DRTS) algorithm inspired by the soft actor-critic (SAC) algorithm to learn the scheduling policy. Task processing performance is affected by communication and computing re-sources allocated to respective resource slices. Thus, considering that frequent resource slicing has a significant management over-head, we further optimize resource slices on a larger timescale. To obtain a policy with low complexity, we propose a greedy-based heuristic algorithm. A hierarchical solution is constructed to find the optimal solution due to the correlation between the two timescale problems. Finally, to validate the effectiveness and superiority of the proposed scheme, extensive simulations are performed.

Service Customized Networking (SCN) is emerging as an escalating technological trend to address the personalized requirements of services, which are plagued in traditional “best-effort” transmission networks. Organically coordinating heterogeneous domains in Wide Area Networks (WANs) is essential for establishing end-to-end customized service delivery. However, the peer-to-peer centralized communication mode between Au-tonomous Domains (ADs) hinders their connectivity and makes it challenging to support diverse intra-domain routing protocols for the desired Quality of Service (QoS). To achieve customized service scheduling for predictable QoS in WANs, we propose a contract theory-based incentive mechanism. In specific, we first select trusted ADs with high service qualities by computing their reputations through a subjective logic model. The Service Provider (SP) decomposes the overall QoS requirements by domains logically from a global perspective. These decomposed QoS metrics will splice differentiated service capabilities from ADs to obtain the expected end-to-end connection. To address information asymmetry between the SP and ADs, we formulate contribution-reward contract items and devise an optimization problem of maximizing the whole system utility. The optimal contract problem is solved through constraints of individual rationality and incentive compatibility. Simulation results indi-cate the feasibility and effectiveness of our scheme on service customization and economic benefits.

In recent years, there has been a proliferation of Edge Intelligence (EI) services, especially within Internet of Vehicles (IoV) scenarios, accompanied by a growing demand for multi-modal content generation. In response, Generative Artificial Intelligence (GAI) has emerged as a promising solution, equipping EI to produce diverse Artificial Intelligence-Generated Content (AIGC) for ubiquitous edge services. However, existing cloud-based GAI capabilities, which are mostly provided via the web and the Internet, introduce unacceptable latency overhead and heightened security risks for vehicular services. To address the above shortcomings and the lack of endogenous mechanisms for applying GAI to IoV scenarios, in this paper, we propose a layered vehicular GAI framework that seamlessly integrates GAI and EI. Within this framework, we devise a distributed collaborative inference mechanism between Road-Side Units (RSUs) and vehicles. Furthermore, we formulate the shared and local inference splitting problem, a pivotal challenge influencing both GAI service latency and content-generation capability. To tackle this issue, we introduce a backward induction-based algorithm, which enables the system can make splitting decisions using a simple threshold-based policy. Simulation results underscore the remarkable performance of the proposed system and vehicular collaborative inference mechanism, promising to facilitate diverse content generation within vehicular networks.

In the era of data-driven intelligent Internet, efficient utilization of computing power is paramount. Yet, current cloud-edge collaboration architectures face challenges with computing power isolation, adversely affecting efficiency and user experience. Computing Power Networks (CPNs) leverage networks with cloud-native applications to connect and manage resources, offering a blueprint for a collaborative computing ecosystem. In the CPN, scheduling stands as a pivotal function. However, conventional scheduling often neglects the interplay between computing and networks. To rectify this, we present a collaborative task scheduling system in CPNs that simultaneously contemplates the selections of computing nodes and network links. Aiming to maintain a balanced load for both computing and network resources, we formulate the scheduling challenge as a Constrained Markov Decision Process (CMDP). This approach focuses on optimizing both execution delay and success rate of computing tasks with load balancing constraints in CPNs. To facilitate the resolution of the CMDP, we introduce a Lyapunov-optimized Deep Reinforcement Learning (DRL) algorithm, which reconfigures the long-term constraint into immediate optimization. We provide numerical results to demonstrate the effectiveness of our suggested policy and algorithm.

The utilization of emergent Generative Artificial Intelligence (GAl) within the realm of Internet of Vehicles (loV) can augment edge intelligence, thereby catering to the diverse content-generation needs of novel in-vehicle services. Nonethe-less, existing cloud-centric GAl paradigms are not inherently suitable for wireless vehicular networks, primarily due to their extensive computing requirements, lack of specificity, and spatial detachment from end users. To cope with these challenges, we introduce an innovative Generative 10 V (g 10 V)architecture that employs a collaborative fine-tuning mechanism for pre-trained GAl models. The mechanism is mainly orchestrated collaboratively by Road-Side Units (RSUs) and vehicles within a Federated Learning (FL) paradigm. Here, we take text-to-image diffusion models as typical examples to show the co-fine-tuning workflow in detail, aiming to utilize edge traffic data to realize rapid, customized, and lightweight GAl in the resource-limited 10 V scenario. Thereafter, we formulate the problem of edge communication and computation resource allocation during RSU-vehicle co-fine-tuning, which is pivotal for optimizing time and energy consumption within this process. To address the challenge, we deploy a Self-adaptive Harmony Search (SHS)-based resource allocation strategy. Experiments based on Stable Diffusion vl-4 model validate the excellent performance in image generating and the time and energy consumption during co-fine-tuning in resource-limited and fast-changing 10 V scenarios.

With the continuous development of network technology and hardware devices, panoramic live cast is promising and widely used in various industries. To meet massive heterogeneous viewer demands, live cast video streams must be transcoded into multiple versions and then transmitted to viewers. By offloading transcoding workloads to network nodes closer to broadcasters and viewers, computing power networks (CPN) have been considered an effective means to provide viewers with a higher quality of experience (QoE). In this paper, we study the joint video transmission and transcoding resource allocation in the CPN-based panoramic livecast system. Considering the versatility and simplicity, we offer a multi-layer network model to capture the stochastic characteristics of transmission and transcoding processes and transform the joint resource allocation problem into a broader shortest path problem (SPP). As the scale of networks expands, the SPP may become intractable on classical computers. This paper explores the viability of solving the SPP on quantum computers by utilizing quantum resources such as superposition and entanglement, then proposes a shortest path algorithm based on the quantum approximate optimization algorithm (QAOA-SPP) for jointly optimizing the latency and overhead of transmission and transcoding. Simulation results indicate that our algorithm can achieve better performance than the benchmark schemes under reasonable parameter settings.

In this paper, we establish a two-timescale framework for the joint service deployment and task scheduling problem in satellite edge computing networks.We aim to optimize the computing performance of networks with diverse quality-of-service (QoS) guarantees for computing tasks. Specifically, to capture the small-timescale network dynamics and task randomness, we formulate the task scheduling problem as a constrained Markov decision process (CMDP) to minimize the energy consumption, load imbalance and packet loss of networks while ensuring the long-term delay. The Lyapunov technique is employed to deal with the delay constraints. A soft actor-critic (SAC)-based deep reinforcement learning (DRL) framework is designed to learn the stationary scheduling policy. We further explore the significant impact of deploying diverse services on the performance of task scheduling in satellite edge computing. Considering that frequent deployment of services will incur huge deployment overhead, we optimize the service deployment on a larger timescale. The optimization problem is modeled as an integer programming problem to improve the service capability of networks and reduce service deployment costs. A heuristic-based atomic orbital search (AOS) approach is proposed to obtain the superior policy with low complexity. Due to the correlation between the problems of two timescales, a hierarchical solution is constructed to iteratively find the excellent solution. Finally, extensive simulations are conducted to validate the effectiveness and superiority of the proposed scheme.

Under the development of computing and network convergence, considering the computing and network resources of multiple providers as a whole in a computing force network (CFN) has gradually become a new trend. However, since each computing and network resource provider (CNRP) considers only its own interest and competes with other CNRPs, introducing multiple CNRPs will result in a lack of trust and difficulty in unified scheduling. In addition, concurrent users have different requirements, so there is an urgent need to study how to optimally match users and CNRPs on a many-to-many basis, to improve user satisfaction and ensure the utilization of limited resources. In this paper, we adopt a reputation model based on the beta distribution function to measure the credibility of CNRPs and propose a performance-based reputation update model. Then, we formalize the problem into a constrained multi-objective optimization problem and find feasible solutions using a modified fast and elitist non-dominated sorting genetic algorithm (NSGA-II). We conduct extensive simulations to evaluate the proposed algorithm. Simulation results demonstrate that the proposed model and the problem formulation are valid, and the NSGA-II is effective and can find the Pareto set of CFN, which increases user satisfaction and resource utilization. Moreover, a set of solutions provided by the Pareto set give us more choices of the many-to-many matching of users and CNRPs according to the actual situation.

In this paper, we optimize network computational performance and ensure diverse quality of service (QoS) for tasks by developing a dual-timescale joint optimization framework for satellite-terrestrial integrated edge computing networks (STECN). In our architecture, STECN can handle intelligent tasks for the Internet of remote things (IoRT) devices based on multiple configured applications deployed. Specifically, we formulate task scheduling as a Markov decision process (MDP) to minimize network energy consumption and task processing delay at small timescales. A deep reinforcement learning (DRL) framework is designed for policy learning. Recognizing the impact of resource slicing on task scheduling in STECN and the deployment overhead from frequent changes, we further optimize resource slicing at larger timescales. To enhance network service capability under dynamic demand, we establish a resource slice gap index, characterizing the difference between actual resources and service demand. By a heuristic-based artificial electric field (AEF) approach, we obtain an optimal strategy with low complexity. Considering the correlation between two timescales, the optimal solution is found by iteratively constructing a hierarchical solution. In addition, to guarantee the global load balancing of the network, we introduce a self-attention mechanism, which allows the knowledge of other satellites to be taken into account when slicing the satellite resources. Finally, extensive simulations confirm the effectiveness and superiority of the proposed scheme.

Distributed clouds decentralize cloud resources, moving from a single high-level point in the network to multiple low-level points, allowing for the dynamic distribution of services across the “cloud-edge-end”. Nevertheless, the “best-effort” traditional networks suffer from unpredictable service quality and limited collaboration between services and networks. To address these shortcomings, we present a novel solution named “Network-as-a-Service (NaaS) Tickets,” inspired by traffic tickets in transportation systems to empower distributed clouds with customized service capabilities. Specifically, we first propose NaaS Tickets-enabled service-customized distributed clouds (NT-SCDC) to realize on-demand and service-oriented interconnection in a wide area. To establish a solid connection between services and networks, we introduce an auction-driven matching mechanism for NaaS Tickets. Then, the matching problem is formulated via an online framework MatOnline, which translates the long-term market problem into a series of one-shot auctions for NaaS Tickets. Based on the Vickrey-Clarke-Groves (VCG) mechanism, we develop MatVCG algorithm to handle one-shot matching problems, guaranteeing truthfulness, individual rationality, and social welfare. Moreover, we improve the performance of MatOnline to find the minimum feasible scale-down ratio with reduced budget expenditure. Experimental results demonstrate our algorithm achieves a stable competitive ratio on social welfare, effectively meeting customized demands

In this paper, a clustered parallel training algorithm is designed for personalized federated learning (Per-FL), called CPPer-FL. CPPer-FL improves the communication and training efficiency of Per-FL from two perspectives, namely, less burden for the central server and lower interaction idling delay. CPPer-FL adopts a client-edge-center learning architecture, which offloads the central server's model aggregation and communication burden to distributed edge servers. Also, CPPer-FL redesigns the cascading model synchronization and updating procedure in conventional Per-FL and changes it to a parallel manner, thus improving the interaction efficiency in the training process. Further, for the proposed hierarchical architecture, two approaches are proposed to cater to Per-FL: similarity-based clustering for client-edge association and personalized model aggregation for parallel model updating, such that clients' personal features can be preserved in the training process. The convergence of CPPer-FL has been formally analyzed and proved. Evaluation results validate the communication efficiency, model convergence, and model accuracy improvement.

In the era driven by big data and algorithms, the efficient collaboration of pervasive computing power is crucial for rapidly meeting computing demands and enhancing resource utilization. However, current mainstream end-edge-cloud collaboration faces challenges of computing isolation, adversely affecting resource efficiency and user experience. The Computing Power Network (CPN) is a novel architecture designed to sense and collaborate ubiquitous computing resources through networks. Nevertheless, the expansion of its scope and the integration of networks complicate task scheduling. To address this, we design a collaborative scheduling system that considers the joint selection of computing nodes and network links, aiming to reduce delay, enhance reliability, and ensure long-term load balance. First, we propose a delay-prioritized reliable scheduling policy based on a dual-priority mechanism for forwarding and computing. Second, we define the scheduling problem as a Constrained Markov Decision Process (CMDP) and introduce Lyapunov optimization to transform constraints into instantaneous optimizations, achieving a long-term balanced load of computing and network resources. Lastly, we employ an enhanced Deep Reinforcement Learning (DRL) approach to solve the problem. Performance evaluation demonstrates that compared to standard DRL, the proposed algorithm effectively reduces delay and improves reliability while maintaining long-term load balance, resulting in an overall performance improvement of 54.7%.

Recently, Computing Force Networks (CFN) is emerging to deeply integrate and flexibly schedule multi-layer, multi-domain, distributed, and heterogeneous computing force resources among the cloud, edge network, and end devices. In CFN, the market monopoly of large resource providers results in a lack of bargaining power for small-sized providers. Existing resources pricing strategies ignore dynamic market factors affecting prices. Moreover, there is information asymmetry between resource consumers and providers. These problems destroy the fairness of the trading market and damage the benefits of consumers and providers, making them have a low degree of willingness to participate in resources trading. Therefore, this paper proposes a contract theory-based incentive mechanism to solve the above problems and motivate resource consumers and providers to join CFN. The proposed scheme classifies resource commodities into different types and enables the trading platform to provide appropriate rewards based on commodities' types and contributions. More specifically, we formulate an optimization problem aiming at maximizing the trading platform's utility and obtain an optimal contract scheme based on the individual rationality and incentive compatible constraints. Simulation results verify the feasibility and effectiveness of our scheme.

As Artificial Intelligence (AI) has flourished in various industries in recent years, the evolutionary trend of endogenous network intelligence continues to accelerate. Connected intelligence, which aims to achieve a widely distributed and collaborative evolution of intelligence, has received much attention. Meanwhile, the emerging Computing Power Network (CPN) provides more robust computation and communication capabilities for intelligence training and intelligent application processing. In this context, the integration of CPN and connected intelligence becomes a potential solution to drive the digital and intelligent transformation of networks. In this paper, we propose a scheme to jointly consider task scheduling, routing, and intelligence capability improvement during the processing of smart applications represented by Digital Twin (DT) in the CPN-enabled connected intelligence systems. We formulate the problem of jointly optimizing the processing time consumption and training accuracy improvement. We solve the problem using a modified NSGA-II algorithm and numerical results show that our approach is effective in optimizing the overall average time consumption and improving the accuracy of the intelligence models distributed in the system during task processing.

Serverless edge computing is seen as a promising enabler to execute differentiated Industrial Internet of Things (IIoT) applications without managing the underlying servers and clusters. In IIoT serverless edge computing, IIoT workflow scheduling for cloud-edge collaborative processing is closely related to the service quality of users. However, serverless functions decomposed by IIoT applications are limited in their deployment at the edge due to the resource-constrained nature of edge infrastructures. In addition, the scheduling of complex IIoT applications supported by serverless computing is more challenging. Therefore, considering the limited function deployment and the complex dependencies of serverless workflows, we model the workflow application as directed acyclic graph and formulate the scheduling problem as a multiobjective optimization problem. A dueling double deep Q-network-based solution is proposed to make scheduling decisions under dynamically changing systems. Extensive simulation experiments are conducted to validate the superiority of the proposed scheme.

Edge intelligence is emerging as a new interdiscipline to push learning intelligence from remote centers to the edge of the network. However, with its widespread deployment, new challenges arise in terms of training efficiency and service of quality (QoS). Massive repetitive model training is ubiquitous due to the inevitable needs of users for the same types of data and training results. Additionally, a smaller volume of data samples will cause the over-fitting of models. To address these issues, driven by the Internet of intelligence, this paper proposes a distributed edge intelligence sharing scheme, which allows distributed edge nodes to quickly and economically improve learning performance by sharing their learned intelligence. Considering the time-varying edge network states including data collection states, computing and communication states, and node reputation states, the distributed intelligence sharing is formulated as a multi-agent Markov decision process (MDP). Then, a novel collective deep reinforcement learning (CDRL) algorithm is designed to obtain the optimal intelligence sharing policy, which consists of local soft actor-critic (SAC) learning at each edge node and collective learning between different edge nodes. Simulation results indicate our proposal outperforms the benchmark schemes in terms of learning efficiency and intelligence sharing efficiency.

In this paper, we design a task scheduling scheme for Internet of Things (IoT) workflow applications in serverless edge computing. Notice the fact that complex applications in traditional serverless computing are decomposed into several stateless, dependent functions, whose execution environments are pre-deployed at the resource-finite edge domain, we model the workflow application as Directed Acyclic Graph (DAG) by considering the distribution of edge resources and the deployment of serverless functions. We further formulate the scheduling problem as a multi-objective optimization problem to reduce the time consumption, energy consumption, and cost simultaneously. Then, considering the diversity of solution space and the fast convergence to optimal solutions, an improved hybrid algorithm that combines Particle Swarm Optimization and Genetic Algorithm (PSO–GA) is introduced and utilized to make the scheduling decision. Finally, extensive simulation experiments are conducted to validate the superiority of the proposed scheme.

By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. Task scheduling is a critical issue in serverless edge computing as it significantly impacts the quality of user experience. In contrast to the centralized scheduling in the cloud center, serverless edge task scheduling is more challenging due to the heterogeneous and resource-constrained nature of edge resources. This article aims to study the distributed task scheduling for the IoT in serverless edge computing networks, in which heterogeneous serverless edge computing nodes are rational individuals with interests to optimize their own scheduling utility while the nodes only have access to local observations. The task scheduling competition process is formulated as a partially observable stochastic game (POSG) to enable serverless edge computing nodes to noncooperatively schedule tasks and allocate computing resources depending on their locally observed system state, which takes into account the associated task generation state, data queue state, communication channel state, and previous computing resource allocation state. To solve the proposed POSG and deal with the partial observability, a multiagent task scheduling algorithm based on the dueling double deep recurrent$Q$-network (D3RQN) method is developed to approximate the optimal task scheduling and resource allocation solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

Viewport prediction and prefetch have an important influence on VR video streaming performance. This work proposes a novel federated learning-based viewport prediction model training algorithm, ComPer-FedAvg. The proposed algorithm leverages a VR video’s common viewing pattern and users’ personal viewing patterns to train the prediction model in a distributed and privacy-preserving manner. Further, considering the VR video viewport prediction accuracy, a stochastic game is formulated to solve the VR streaming network’s communication resource allocation problem, where limited communication resource blocks are auctioned to users to achieve the optimal overall VR viewing experience. For each user, the auction is decomposed into two disjoint subproblems, namely, the optimal number of data rate requesting and true value claiming (bidding). The optimal true value claiming has been analytically proved to be equal to the VR viewing reward with given data rate. Due to the lack of global information when users request data rate, we reformulate users’ data rate requesting problem as a POMDP problem. A novel deep reinforcement learning algorithm is adopted to solve the problem. Evaluation and simulation results show the proposed viewport prediction and VR streaming schemes outperform conventional solutions in terms of prediction accuracy and VR viewing experience.

Driven by the growing popularity of mobile applications, such as the Internet of Things (IoT), fog computing has been envisioned as a promising approach to enhance the computation capability of mobile devices and reduce the energy consumption. In this article, we aim to investigate the dynamic computation offloading problem in the IoT fog system under the fast time-varying wireless channel conditions. Our work differs from the existing work, which is based on the assumption that the channel-state information can be perfectly obtained by the offloading agent (e.g., the IoT device). In reality, due to hardware limitation, short sensing time, and network connectivity issues in IoT fog systems, it is difficult for the IoT device to have the perfect knowledge of a dynamic channel environment. Therefore, in this article, we propose a partially observable offloading scheme to enable the IoT device to make the optimal offloading decision with imperfect channel-state information. The optimization problem is formulated as a partially observable Markov decision process (POMDP) formulation, with the objective of minimizing the IoT device's energy consumption while meeting its requirement on task processing delay. To find the optimal offloading solution, an offline algorithm based on the deep recurrent $Q$ -network (DRQN) is developed. Finally, extensive simulation experiments are performed to evaluate the effectiveness of the proposed offloading scheme.

Recently, fog computing has emerged as a prospective technique to provide pervasive and agile computation services for Internet-of-Things (IoT) devices and support advanced applications. Introducing the energy harvesting (EH) technique into the fog computing system can extend the battery lifetime and provide a higher quality of experiences (QoE) for IoT devices. In the EH-enabled IoT fog system, computation offloading is an important issue and has attracted much attention. In most existing works, it is assumed that the IoT device is fully aware of the system state. However, in practical offloading problems, the IoT device may not be able to obtain accurate system state information, and only have a partial observation of the environment. Therefore, in this article, we investigate the decentralized partially observable offloading problem in the EH-enabled IoT fog system, in which multiple IoT devices cooperate to maximize the network performance while meeting their QoE requirements. We formulate the optimization problem as a decentralized partially observable Markov decision process (Dec-POMDP) in which each IoT device makes the task offloading decisions according to its local observation of the environment. The Lagrangian approach and the policy gradient method are adopted to find the optimal solution for the proposed problem. Due to the high complexity of solving the Dec-POMDP, a learning-based decentralized offloading algorithm with low complexity is presented to find the approximate optimal solution. Finally, extensive experimental evaluation and comparison are carried out to show the effectiveness of the proposed scheme.

Computation offloading has been considered as one of the key research issues in edge computing fields. In order to reduce the energy consumption of the mobile terminal, the energy efficiency issue of computation offloading has attracted a lot of attention from academia and industry. In this study, the authors propose an energy‐efficient computation offloading scheme in 5G cellular networks with edge computing and device‐to‐device (D2D) communications. They consider the computation offloading to fog computing devices via D2D communications and mobile edge computing (MEC) servers via cellular networks. And thus the computation task execution model can be composed of local execution, fog computing device execution and MEC server execution. Then, they formulate the computation offloading issue as stochastic optimisation problem, and use the Lyapunov optimisation technology framework to solve this problem. Finally, extensive simulation results are presented to illustrate the effectiveness of the proposed scheme.

The caching in fifth generation (5G) networks has been considered as a promising technique to reduce the duplicate traffic transmission and improve the users' quality of experience (QoE). Although many works have been done for caching in 5G networks, most of them focus on the content caching policy design to optimise the users' QoE, the issue to realise the energy efficiency of the whole network is not fully considered. Therefore, in this study, by introducing the hierarchical cooperative caching property, i.e. the core gateway and the base stations can cooperative cache the content, the authors study the problem of the hierarchical cooperative caching policy to realise the energy efficiency. They then formulate the hierarchical cooperative content placement problem as an integer programming problem to minimise the total energy consumption. Also, to reduce the computation complexity, the optimisation algorithm based on the idea of quantum‐inspired evolutionary is proposed, which has the fast convergence and approximate to the optimal solution. Finally, extensive simulation results are illustrated to demonstrate the performance of the proposed scheme.

Caching in mobile networks can reduce the redundant data transmission and cope with the challenge of the explosive growth of mobile data traffic. It has been considered as a promising technology in 5G networks and has been attracting a lot of attention in recent years. Although many existing works have addressed the content placement problem or the cache optimisation problem, most of them do not consider the issue of hierarchical collaborative caching. Collaborative caching can further alleviate the traffic pressure and reduce the user‐perceived latency by reducing duplicate content transmission. Therefore, in this study, the authors consider a hierarchical collaborative caching framework with the cache deployment at the distributed gateway and mobile edge computing servers, and then design a novel caching strategy based on this framework. They formulate the hierarchical collaborative content placement problem as an optimisation problem to maximise the latency saving under the constraint of limited cache capacity. Since finding the optimal solution is an NP‐hard problem, they propose a genetic placement algorithm to find the near‐optimal solution to reduce the computation complexity. Numerical experiment results show that the proposed algorithms can significantly improve the performance compared with the reference algorithms.