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Energy-Efficient Computation Offloading and Resource Allocation in Cloud-Edge Collaborative Computing Systems 认领 引用
1
作者 Cheng Zhaoping Jiang Tao +3 位作者 Ke Chenxi Zhang Guoqiang Peng Miaoran Feng Mingjie 《China Communications》 SCIE EI CSCD 2026年第3期316-329,共14页
With the increasing adoption of cloud–edge collaborative computing in delay-sensitive applications,sleep control of edge nodes has become a key approach to reducing operational energy consumption.However,existing sch... With the increasing adoption of cloud–edge collaborative computing in delay-sensitive applications,sleep control of edge nodes has become a key approach to reducing operational energy consumption.However,existing schemes have not balanced energy efficiency and performance under edge node sleep control and still lack a joint optimization mechanism for computation offloading and resource allocation.This paper tackles the problem of optimizing energy-efficient computation offloading and resource allocation(CORA)in cloud-edge collaborative computing systems,where edge servers can dynamically enter sleep mode to reduce power consumption.We model the problem as a mixed-integer nonlinear programming formulation,with the objective of minimizing a weighted sum of overall task latency and the cumulative energy consumption of all IoT devices and edge servers.To handle the hybrid nature of discrete and continuous decision variables and the complex system dynamics,we reformulate the problem for each device as a Markov Decision Process and develop a deep deterministic policy gradient with multi-agent algorithm tailored for such hybrid action spaces.Simulation results show that the proposed CORA strategy achieves superior performance compared to three benchmark schemes with reduced latency and energy consumption. 展开更多
关键词 cloud-edge collaborative computing computation offloading deep reinforcement learning energy-efficient design
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A Deception Defense Timing Selection Method Based on Time-Delayed FlipIt Game in Cloud-Edge Collaborative Networks 认领 引用
2
作者 Jinchuan Pei Yuxiang Hu +2 位作者 Hongtao Yu Zihao Wang Menglong Li 《Computers, Materials & Continua》 SCIE EI 2026年第7期1523-1542,共20页
In the cloud-edge collaborative network,advanced persistent threats(APTs)pose a serious security risk to critical network assets.Although network deception defense can mislead attackers’cognition,its effectiveness de... In the cloud-edge collaborative network,advanced persistent threats(APTs)pose a serious security risk to critical network assets.Although network deception defense can mislead attackers’cognition,its effectiveness depends on dynamically selecting appropriate rotation timings of the deception defense.However,the deployment of deception resources and state updates is not completed instantaneously,and existing methods ignore the state transition delay and the dynamic interaction between the attackers and defenders during the real attack and defense process.To address this,we propose a deception defense timing selection method based on the time-delayed FlipIt game.Firstly,a network state evolution model integrating state transition delay is constructed,and the dynamic transfer process between node states is characterized by a set of delay differential equations.Secondly,a cloud-edge collaborative defense architecture is designed.On this basis,a time-delayed FlipIt game model(TD-FlipIt)is established,and the gate control mechanism is introduced to formalize the defense cooling period as a constraint for the rotation action of deception resources.Subsequently,we use the multi-agent deep deterministic policy gradient(MADDPG)algorithm to solve the rotation strategy for deception defense timing.Experimental results show that the proposed method can effectively optimize the selection of defense timing,ensuring defense effectiveness while reducing resource consumption,and providing effective support for defense in the cloud-edge collaborative environment. 展开更多
关键词 Cloud-edge collaborative network deception defense timing FlipIt game multi-agent reinforcement learning
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Heterogeneous Computing Power Scheduling Method Based on Distributed Deep Reinforcement Learning in Cloud-Edge-End Environments 认领 引用
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作者 Jinwei Mao Wang Luo +5 位作者 Jiangtao Xu Daohua Zhu WeiLiang Zhechen Huang Bao Feng Shuang Yang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1964-1985,共22页
With the rapid development of power Internet of Things(IoT)scenarios such as smart factories and smart homes,numerous intelligent terminal devices and real-time interactive applications impose higher demands on comput... With the rapid development of power Internet of Things(IoT)scenarios such as smart factories and smart homes,numerous intelligent terminal devices and real-time interactive applications impose higher demands on computing latency and resource supply efficiency.Multi-access edge computing technology deploys cloud computing capabilities at the network edge;constructs distributed computing nodes and multi-access systems and offers infrastructure support for services with low latency and high reliability.Existing research relies on a strong assumption that the environmental state is fully observable and fails to thoroughly consider the continuous time-varying features of edge server load fluctuations,leading to insufficient adaptability of the model in a heterogeneous dynamic environment.Thus,this paper establishes a framework for end-edge collaborative task offloading based on a partially observable Markov decision-making process(POMDP)and proposes a method for end-edge collaborative task offloading in heterogeneous scenarios.It achieves time-series modeling of the historical load characteristics of edge servers and endows the agent with the ability to be aware of the load in dynamic environmental states.Moreover,by dynamically assessing the exploration value of historical trajectories in the central trajectory pool and adjusting the sample weight distribution,directional exploration and strategy optimization of high-value trajectories are realized.Experimental results indicate that the proposed method exhibits distinct advantages compared with existing methods in terms of average delay and task failure rate and also verifies the method’s robustness in a dynamic environment. 展开更多
关键词 Edge computing end-edge collaboration heterogeneous computing power scheduling resource allocation
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Privacy-Preserving Transformer Inference with Optimized Homomorphic Encryption and Secure Collaborative Computing 认领 引用
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作者 Tao Bai Yang Tang +2 位作者 Kuan Shao Zhenyong Zhang Yuanteng Liu 《Computers, Materials & Continua》 SCIE EI 2026年第7期1242-1265,共24页
In recent years,the rapid development of artificial intelligence has greatly promoted the application of Machine Learning as a Service(MLaaS).Users can upload their requirements through front-end applications,and the ... In recent years,the rapid development of artificial intelligence has greatly promoted the application of Machine Learning as a Service(MLaaS).Users can upload their requirements through front-end applications,and the server provides model inference services after receiving the user input.However,MLaaS may lead to serious privacy breaches.Large language model services are typical representatives of MLaaS,and the Transformer is a typical structure in large language models.Therefore,this paper proposes a privacy-protected Transformer inference scheme based on the CKKS fully homomorphic encryption scheme to optimize computational and communication efficiency.Firstly,this paper implements efficient matrix multiplication based on ring multiplication and optimizes the matrix partition parameters to adapt to different types(including ciphertext-plaintext and ciphertext-ciphertext)and different matrix dimensions.Secondly,this paper optimizes and designs secure Softmax,LayerNorm,and Gelu protocols based on parameter fuzzing and collaborative computing to perform efficient,secure atomic computations over ciphertexts.Finally,experiments on text classification were conducted on the IMDB and AGNEWS datasets.The results show that,under our experimental settings(including an AMD Ryzen 75700G CPU with 32 GB RAM and 8-thread parallel computing using the Lattigo library),the scheme proposed in this paper completes the inference process within 3 s,with communication costs below 1 GB,and the computing accuracy is comparable to that of plaintext computing. 展开更多
关键词 Machine learning as a service privacy preservation Transformer collaborative computing
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An Adaptive Hybrid Edge-Cloud Collaborative Offloading Method for Large-Scale Computational Tasks of Intelligent Machine Tool:Low-Latency,Energy-Efficient,and Secure 认领 引用
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作者 Zhiwen Lin Kaien Wei +4 位作者 Yiqiao Wang Chuanhai Chen Jinyan Guo Qiang Cheng Zhifeng Liu 《Engineering》 SCIE EI CSCD 2026年第1期201-218,共18页
Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generat... Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generation of vast multi-source sensor data streams and numerous computational tasks with interdependent data relationships.The stringent real-time constraints and intricate dependency structures present considerable challenges to traditional single-mode computational frameworks.Furthermore,there is a growing demand for computational offloading solutions in intelligent machine tools that extend beyond merely optimizing latency.These solutions must also address energy management for sustainable manufacturing and ensure security to protect sensitive industrial data.This paper introduces an adaptive hybrid edge-cloud collaborative offloading mechanism that combines single-edge-cloud collaboration with multi-edge-cloud collaboration.This mechanism is capable of dynamically switching between collaborative modes based on the status of computational nodes,task characteristics,dependency complexity,and resource availability,ultimately facilitating low-latency,energy-efficient,and secure task processing.A novel hybrid hyper-heuristic algorithm has been developed to address largescale task allocation challenges in heterogeneous edge-cloud environments,enabling the flexible allocation of computational resources and performance optimization.Extensive experiments indicate that the proposed approach achieves average enhancements of 27.36%in task processing time and 7.89%in energy efficiency when compared to state-of-the-art techniques,all while maintaining superior security performance.Validation through case studies on a digital twin gantry five-axis machining center illustrates that the mechanism effectively coordinates task execution across multi-source concurrent data processing,complex dependency task collaboration,high-computational machine learning workloads,and continuous batch task deployment scenarios,achieving a 37.03%reduction in latency and a 25.93%optimization in energy use relative to previous generation collaboration methods.These results provide both theoretical and technical backing for sustainable and secure computational offloading in intelligent machine tools,thereby contributing to the evolution of next-generation smart manufacturing systems. 展开更多
关键词 Intelligent machine tools Edge-cloud collaboration Task offloading Resilient resources Sustainable computing
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A Multi-Agent Deep Reinforcement Learning-Based Task Offloading Method for 6G-Enabled Internet of Vehicles with Cloud-Edge-Device Collaboration 认领 引用
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作者 Fangxiang Hu Qi Fu +1 位作者 Shiwen Zhang Jing Huang 《Computers, Materials & Continua》 SCIE EI 2026年第6期1842-1872,共31页
In the Internet of Vehicles(IoV)environment,the growing demand for computational resources from diverse vehicular applications often exceeds the capabilities of intelligent connected vehicles.Traditional approaches,wh... In the Internet of Vehicles(IoV)environment,the growing demand for computational resources from diverse vehicular applications often exceeds the capabilities of intelligent connected vehicles.Traditional approaches,which rely on one or more computational resources within the cloud-edge-device computing model,struggle to ensure overall service quality when handling high-density traffic flows and large-scale tasks.To address this issue,we propose a computational offloading scheme based on a cloud-edge-device collaborative 6G IoV edge computing model,namely,Multi-Agent Deep Reinforcement Learning-based and Server-weighted scoring Selection(MADRLSS),which aims to optimize dynamic offloading decisions and resource allocation.The scheme first designs an improved multi-agent proximal policy optimization(MAPPO)algorithm,decoupling centralized training from distributed execution for multiple terminal vehicle agents.Specifically,the centralized training of terminal vehicles is migrated to the high-performance edge layer,while lightweight decision-making networks are retained at the terminal vehicles to enable efficient and dynamic task offloading decisions.Additionally,a server-weighted scoring selection(SS)algorithm is proposed,which integrates two key metrics—short-term server load and geographical proximity—to select the optimal server and allocate communication resources.The proposed scheme improves the quality of experience(QoE)while balancing energy consumption.Simulation results demonstrate that the MADRLSS scheme significantly outperforms existing benchmark methods in terms of task offloading efficiency and stability,maintaining QoE consistently above 82%and effectively enhancing service quality in complex vehicular scenarios. 展开更多
关键词 Vehicular edge computing cloud-edge-device collaboration multi-agent proximal policy optimization high-density vehicle task offloading resource allocation 6G
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Collaborative Scheduling Strategy for Computation and Power among Multiple Data Centers Based on New Energy State Recognition 认领 引用
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作者 Qian Yang Shenglei Du +3 位作者 Boyang Chen Yalu Sun Ding Li Zhiheng Zhang 《Energy Engineering》 EI 2026年第8期346-373,共28页
In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon ... In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon neutrality”,data centers have gradually introduced new energy power such as wind and photovoltaic power.However,the randomness and volatility of their output pose challenges to efficient absorption.Based on the spatiotemporal complementary characteristics of new energy output in multiple data centers and the spatiotemporal migration capability of computing tasks,this paper proposes a new energy-aware adaptive collaborative scheduling strategy for computation and power.The strategy first constructs a regionally differentiated load model to accurately depict the characteristic differences among the Jiangsu-Zhejiang-Shanghai mixed computing power hub,the Gansu highefficiency computing power base,and the coastal green computing power nodes.Then,a dual-mode scheduling algorithm based on Lyapunov optimization is designed,integrating a prediction-reaction mechanism to achieve dynamic balance between system stability and new energy absorption rate.Furthermore,a V-parameter adaptive adjustment mechanism and a hierarchical fault-tolerant guarantee system are proposed to cope with new energy fluctuations and improve system robustness.Simulation results show that the proposed strategy achieves an average new energy absorption rate of 62.3%and 52.8%in normal weather and severe weather scenarios,respectively.The carbon emission per unit computing power is reduced by 20.9%,and the computing power-electricity efficiency is improved by 9.1%,which is significantly better than the static scheduling strategy.This verifies its effectiveness and practicability in improving new energy utilization,ensuring service quality,and reducing carbon emissions. 展开更多
关键词 Collaborative scheduling for computation and power data centers Lyapunov optimization V-parameter adjustment mechanism wind-solar volatility
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Intelligent Connected Cloud-Edge Collaborative Architecture for Space-Based Distributed Electromagnetic Spectrum Monitoring 认领 引用
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作者 Chen Jianyun Qu Zhi +1 位作者 Wang Ding Liu Sili 《China Communications》 SCIE EI CSCD 2025年第5期28-47,共20页
Using satellites to complete spectrum monitoring tasks can effectively receive and process electromagnetic spectrum signals emitted by radiation sources.However,due to the shortage of satellite storage,computing and n... Using satellites to complete spectrum monitoring tasks can effectively receive and process electromagnetic spectrum signals emitted by radiation sources.However,due to the shortage of satellite storage,computing and network resources,the intersatellite coordination is weak,and with the massive growth of spectrum data,the traditional cloud computing mode cannot meet the requirements of electromagnetic spectrum monitoring in terms of real-time,bandwidth,and security.We apply edge computing technology and deep learning technology to the satellite.Aiming at the problems of distributed satellite management and control,we propose a space-based distributed electromagnetic spectrum monitoring intelligent connected cloud-edge collaborative architecture SpaceEdge.SpaceEdge applies edge computing and artificial intelligence technology to space-based spectrum monitoring.SpaceEdge deploys intelligent monitoring algorithms to edge nodes to form edge intelligent satellite,and uses the cloud to uniformly manage and control heterogeneous edge satellite and monitor satellite resources.In addition,SpaceEdge can also adjust edge intelligent spectrum monitoring applications as needed to achieve effective coordination of inter-satellite algorithms and data to achieve the purpose of collaborative monitoring.Finally,SpaceEdge was experimentally verified,and the results proved the feasibility of SpaceEdge and can improve the timeliness and autonomy of the distributed satellite’s coordinated signal monitoring. 展开更多
关键词 cloud-edge collaborative electromagnetic spectrum monitoring intelligent connected satellite network
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High-Throughput and Energy-Saving Blockchain for Untrusted IIoT Device Participation in Edge-to-End Collaborative Computing 认领 引用
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作者 Zhang Zhen Huang Xiaowei +2 位作者 Li Chengjie Li Aihua Xiao Liqun 《China Communications》 SCIE EI CSCD 2025年第11期132-143,共12页
The integration of blockchain and edgeto-end collaborative computing offers a solution to address the trust issues arising from untrusted IIoT devices.However,ensuring efficiency and energy-saving in applying blockcha... The integration of blockchain and edgeto-end collaborative computing offers a solution to address the trust issues arising from untrusted IIoT devices.However,ensuring efficiency and energy-saving in applying blockchain to edge-to-end collaborative computing remains a significant challenge.To tackle this,this paper proposes an innovative task-oriented blockchain architecture.The architecture comprises trusted Edge Computing(EC)servers and untrusted Industrial Internet of Things(IIoT)devices.We organize untrusted IIoT devices into several clusters,each executing a task in the form of smart contracts,and package the work logs of a task into a block.Executing a task with smart contracts within a cluster ensures the reliability of the task result.Reducing the scope of nodes involved in block consensus increases the overall throughput of the blockchain.Packaging task logs into blocks,storing and propagating blocks through corresponding Edge Computing(EC)servers reduces network load and avoids computing power competition.The paper also presents the proposed architecture’s theoretical TPS(Transactions Per Second)and failure probability calculations.Experimental results demonstrate that this architecture ensures computational security,improves TPS,and reduces resource consumption. 展开更多
关键词 blockchain technology consensus mechanism edge-to-end collaborative computing untrusted IIoT devices
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Cloud-Edge Intelligent Collaborative Computing Model Based on Transfer Learning in IoT 认领 引用
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作者 Yang Long Zhixin Li 《国际计算机前沿大会会议论文集》 2023年第1期389-403,共15页
The deep neural network is a reliable technical support for cloud com-puting and edge computing.It has excellent nonlinear approximation and gener-alization capabilities,making it suitable for classifying and predicti... The deep neural network is a reliable technical support for cloud com-puting and edge computing.It has excellent nonlinear approximation and gener-alization capabilities,making it suitable for classifying and predicting Internet of Things data in cloud computing and edge computingfields.However,the increas-ing size of neural networks poses a challenge for their deployment on devices with limited computing and storage resources.Traditional cloud computing ser-vices also suffer from high latency,which hinders real-time tasks.To address these challenges,this paper proposes a cloud-side cooperation model for deep learning based on migration learning technology.This model used migration learning tech-nology to reduce the size of deep neural networks.Specifically,it deployed the deep neural network model(CDLM)in the cloud and the shallow neural network model(EDLM)at the edge.CDLM is used to help train EDLM and improve its performance,enabling it to run independently on edge devices with high accu-racy and respond to real-time tasks.This approach reduced the amount of user data transmitted to the cloud,alleviated bandwidth pressure,and protected user privacy.Experimental results show that the proposed model improved the accu-racy of EDLM by 19.58% compared with traditional neural network models.Thesefindings provide a theoretical and experimental foundation for the study of cloud-edge collaborative models. 展开更多
关键词 Deep Learning Cloud Computing Edge Computing Transfer learning Cloud-edge collaboration
Optimization and Design of Cloud-Edge-End Collaboration Computing for Autonomous Robot Control Using 5G and Beyond 认领 引用
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作者 Hao Wang 《Journal of Beijing Institute of Technology》 EI CAS 2022年第5期454-463,共10页
Robots have important applications in industrial production, transportation, environmental monitoring and other fields, and multi-robot collaboration is a research hotspot in recent years. Multi-robot autonomous colla... Robots have important applications in industrial production, transportation, environmental monitoring and other fields, and multi-robot collaboration is a research hotspot in recent years. Multi-robot autonomous collaborative tasks are limited by communication, and there are problems such as poor resource allocation balance, slow response of the system to dynamic changes in the environment, and limited collaborative operation capabilities. The combination of 5G and beyond communication and edge computing can effectively reduce the transmission delay of task offloading and improve task processing efficiency. First, this paper designs a robot autonomous collaborative computing architecture based on 5G and beyond and mobile edge computing(MEC).Then, the robot cooperative computing optimization problem is studied according to the task characteristics of the robot swarm. Then, a reinforcement learning task offloading scheme based on Qlearning is further proposed, so that the overall energy consumption and delay of the robot cluster can be minimized. Finally, simulation experiments demonstrate that the method has significant performance advantages. 展开更多
关键词 robot collaboration mobile edge computing(MEC) 5G and beyond network task offloading resource allocation
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Joint computation offloading and service downloading in satellite edge computing networks 认领 引用
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作者 Wu Qi Zhu Lidong 《China Communications》 SCIE EI CSCD 2026年第4期238-258,共21页
The limited onboard cache and computing resources significantly constrain the computational service capabilities of individual edge satellites in hotspot regions.To address this challenge,we propose a twotier cloud-ed... The limited onboard cache and computing resources significantly constrain the computational service capabilities of individual edge satellites in hotspot regions.To address this challenge,we propose a twotier cloud-edge computing architecture that organizes edge satellites and their associated ground clouds into multiple collaborative domains.Within each domain,we formulate a joint optimization problem for computation offloading and service downloading under the constraints of edge satellites’service deployment and caching space,aiming to minimize the sum of weighted energy consumption and latency.The originally non-convex problem is transformed into a more tractable convex optimization formulation through variable relaxation.Subsequently,we develop an alternating direction method of multipliers(ADMM)-based distributed optimization framework that enables cooperative decision-making among domain satellites for the optimization of computational offloading,service downloading,and service deleting variables.Additionally,we propose an innovative binary variable recovery algorithm that ensures feasible conversion from continuous solutions to discrete decision variables while preserving constraint satisfaction.Extensive simulations demonstrate that our approach achieves lower task execution cost and packet loss rate compared with benchmarks. 展开更多
关键词 alternating direction method of multipliers(ADMMs) cloud-edge computing architecture computation offloading edge satellites service downloading
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Intelligent Task Offloading and Collaborative Computation in Multi-UAV-Enabled Mobile Edge Computing 认领 引用 被引量：12
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作者 Jingming Xia Peng Wang +1 位作者 Bin Li Zesong Fei 《China Communications》 SCIE CSCD 2022年第4期244-256,共13页
This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay o... This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity. 展开更多
关键词 mobile edge computing multi-UAV collaborative cloud and edge computing deep neural network differential evolution
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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system 认领 引用
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作者 Uchechukwu Awada Jiankang Zhang +2 位作者 Sheng Chen Shuangzhi Li Shouyi Yang 《Digital Communications and Networks》 SCIE CSCD 2024年第6期1837-1850,共14页
Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enabl... Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive tasks.One fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource utilization.Existing approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource availability.These approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider lock-in.Therefore,we present Edge Colla,which is based on the integration of edge resources running across multi-edge deployments.Edge Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize them.Extensive experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes. 展开更多
关键词 Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling
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Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities 认领 引用 被引量：5
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作者 Qin Zhen He Shoushuai +5 位作者 Wang Hai Qu Yuben Dai Haipeng Xiong Fei Wei Zhenhua Li Hailong 《China Communications》 SCIE CSCD 2024年第5期1-16,共16页
By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-grow... By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC. 展开更多
关键词 air-ground architecture collaborative mobile edge computing
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Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing 认领 引用 被引量：4
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作者 Xu Wenjing Wang Wei +2 位作者 Li Zuguang Wu Qihui Wang Xianbin 《China Communications》 SCIE CSCD 2024年第4期218-229,共12页
Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus t... Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case. 展开更多
关键词 blockchain collaborative edge computing resource optimization task allocation
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Demand-aware mobile bike-sharing service using collaborative computing and information fusion in 5G IoT environment 认领 引用
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作者 Xiaoxian Yang Yueshen Xu +2 位作者 Yishan Zhou Shengli Song Yinchen Wu 《Digital Communications and Networks》 SCIE CSCD 2022年第6期984-994,共11页
Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)envir... Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities,especially in the new era of 5G and Internet-of-Things(IoT)environments.A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems.In the real world,the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage.In addition to pure manual rebalancing,in several works,attempts were made to predict the demand for bikes.In this paper,we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion.We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering.We carefully analyzed and discovered the group of features that impact the demand of bikes,from historical bike-sharing records and 5G IoT environment data.We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand.We performed sufficient experiments on two real-world datasets.The results confirm that compared to some existing methods,our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments. 展开更多
关键词 Mobile bike-sharing service Demand prediction Collaborative computing Information fusion 5G IoT
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Recommendation algorithm of cloud computing system based on random walk algorithm and collaborative filtering model 认领 引用 被引量：1
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作者 Feng Zhang Hua Ma +1 位作者 Lei Peng Lanhua Zhang 《International Journal of Technology Management》 2017年第3期79-81,共3页
The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is... The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed. 展开更多
关键词 Random walk algorithm collaborative filtering model cloud computing system recommendation algorithm
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Collaborative Design Theory and Related Key Technology Study Based on Cloud Computing 认领 引用
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作者 Meiren Zhang Ying Chen 《Journal of Software Engineering and Applications》 2013年第3期18-22,共5页
Analyzes the main way of product distribution for collaborative design. According to the requirement of manufacturing collaborative design, apply cloud computing in manufacturing collaborative design and come up the c... Analyzes the main way of product distribution for collaborative design. According to the requirement of manufacturing collaborative design, apply cloud computing in manufacturing collaborative design and come up the concept of product collaborative cloud design. Study the product collaborative design theory based on cloud computing and the general key technology of cloud computing, semantic web, intelligent matching selection algorithm, STEP and XML technology, version management and conflict resolution arithmetic and so on which related to this theory. The study object of this article is automotive product. Construct an automotive collaborative design system with the key technology to verify the feasibility and validity of the cloud basing collaborative design theory and related technology. This collaborative design system will overcome the weakness that resource and information can not be shared between different department in the same enterprise or different enterprises. Join up this system will help directly enterprise for collaborative design and the repetition construction of collaborative design platform of each enterprise will be avoid. It will reduce the investment of enterprises for constructing and managing collaborative design platform and further reduce the cost of product R&D with a better and more efficient design. 展开更多
关键词 Cloud Computing Cloud Design Collaborative Design Semantic Web Intelligent Matching Version Management
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Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture 认领 引用 被引量：4
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作者 Siyuan Jiang Hongjun Gao +2 位作者 Xiaohui Wang Junyong Liu Kunyu Zuo 《Global Energy Interconnection》 EI CSCD 2023年第1期1-14,共14页
With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provi... With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system. 展开更多
关键词 Cloud-edge collaboration architecture Multi-agent deep reinforcement learning Multi-level dynamic reconfiguration Offline learning Online learning
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