期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到33篇文章
< 1 2 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
A Deception Defense Timing Selection Method Based on Time-Delayed FlipIt Game in Cloud-Edge Collaborative Networks 认领 引用
1
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
暂未订购 下载PDF
Energy-Efficient Computation Offloading and Resource Allocation in Cloud-Edge Collaborative Computing Systems 认领 引用
2
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
暂未订购 下载PDF
Intelligent Connected Cloud-Edge Collaborative Architecture for Space-Based Distributed Electromagnetic Spectrum Monitoring 认领 引用
3
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
暂未订购 下载PDF
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
4
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
暂未订购 下载PDF
Achieving Fuzzy Matching Data Sharing for Secure Cloud-Edge Communication 认领 引用 被引量：4
5
作者 Chuan Zhang Mingyang Zhao +4 位作者 Yuhua Xu Tong Wu Yanwei Li Liehuang Zhu Haotian Wang 《China Communications》 SCIE CSCD 2022年第7期257-276,共20页
In this paper,we propose a novel fuzzy matching data sharing scheme named FADS for cloudedge communications.FADS allows users to specify their access policies,and enables receivers to obtain the data transmitted by th... In this paper,we propose a novel fuzzy matching data sharing scheme named FADS for cloudedge communications.FADS allows users to specify their access policies,and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies simultaneously.Specifically,we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge environments.Then,we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the policies.If the matching succeeds,the data can be decrypted.Otherwise,nothing will be revealed.In addition,FADS allows users to dynamically specify the policy for each time,which is an urgent demand in practice.A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack(IND-CCA)in random oracle model against probabilistic polynomial-time(PPT)adversary,and the desirable security properties of privacy and authenticity are achieved.Extensive experiments provide evidence that FADS is with acceptable efficiency. 展开更多
关键词 fuzzy-matching privacy-preserving set intersection cloud-edge communication data sharing
暂未订购 下载PDF
Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration 认领 引用 被引量：3
6
作者 Huabin Wang Ruichao Mo +3 位作者 Yuping Chen Weiwei Lin Minxian Xu Bo Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第11期2025-2047,共23页
Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intellig... Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%. 展开更多
关键词 Pedestrian and vehicle detection YOLOv4 channel pruning cloud-edge collaboration
暂未订购 下载PDF
Efficient Multi-Authority Attribute-Based Searchable Encryption Scheme with Blockchain Assistance for Cloud-Edge Coordination 认领 引用 被引量：1
7
作者 Peng Liu Qian He +2 位作者 Baokang Zhao Biao Guo Zhongyi Zhai 《Computers, Materials & Continua》 SCIE EI 2023年第9期3325-3343,共19页
Cloud storage and edge computing are utilized to address the storage and computational challenges arising from the exponential data growth in IoT.However,data privacy is potentially risky when data is outsourced to cl... Cloud storage and edge computing are utilized to address the storage and computational challenges arising from the exponential data growth in IoT.However,data privacy is potentially risky when data is outsourced to cloud servers or edge services.While data encryption ensures data confidentiality,it can impede data sharing and retrieval.Attribute-based searchable encryption(ABSE)is proposed as an effective technique for enhancing data security and privacy.Nevertheless,ABSE has its limitations,such as single attribute authorization failure,privacy leakage during the search process,and high decryption overhead.This paper presents a novel approach called the blockchain-assisted efficientmulti-authority attribute-based searchable encryption scheme(BEM-ABSE)for cloudedge collaboration scenarios to address these issues.BEM-ABSE leverages a consortium blockchain to replace the central authentication center for global public parameter management.It incorporates smart contracts to facilitate reliable and fair ciphertext keyword search and decryption result verification.To minimize the computing burden on resource-constrained devices,BEM-ABSE adopts an online/offline hybrid mechanism during the encryption process and a verifiable edge-assisted decryption mechanism.This ensures both low computation cost and reliable ciphertext.Security analysis conducted under the random oracle model demonstrates that BEM-ABSE is resistant to indistinguishable chosen keyword attacks(IND-CKA)and indistinguishable chosen plaintext attacks(INDCPA).Theoretical analysis and simulation results confirm that BEM-ABSE significantly improves computational efficiency compared to existing solutions. 展开更多
关键词 Attribute-based encryption search encryption blockchain multi-authority cloud-edge
暂未订购 下载PDF
Anomaly Detection and Access Control for Cloud-Edge Collaboration Networks 认领 引用 被引量：1
8
作者 Bingcheng Jiang Qian He +1 位作者 Zhongyi Zhai Hang Su 《Intelligent Automation & Soft Computing》 SCIE 2023年第8期2335-2353,共19页
Software-defined networking(SDN)enables the separation of control and data planes,allowing for centralized control and management of the network.Without adequate access control methods,the risk of unau-thorized access... Software-defined networking(SDN)enables the separation of control and data planes,allowing for centralized control and management of the network.Without adequate access control methods,the risk of unau-thorized access to the network and its resources increases significantly.This can result in various security breaches.In addition,if authorized devices are attacked or controlled by hackers,they may turn into malicious devices,which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted.To solve those problems,an anomaly detection and access control mechanism based on SDN and neural networks is proposed for cloud-edge collaboration networks.The system employs the Attribute Based Access Control(ABAC)model and smart contract for fine-grained control of device access to the network.Furthermore,a cloud-edge collaborative Key Performance Indicator(KPI)anomaly detection method based on the Gated Recurrent Unit and Generative Adversarial Nets(GRU-GAN)is designed to discover the anomaly devices.An access restriction mechanism based on reputation value and anomaly detection is given to prevent anomalous devices.Experiments show that the proposed mechanism performs better anomaly detection on several datasets.The reputation-based access restriction effectively reduces the number of malicious device attacks. 展开更多
关键词 Cloud-edge SDN anomaly detection GRU-GAN
暂未订购 下载PDF
SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment 认领 引用
9
作者 HAN Yingchao MENG Weixiao FAN Wentao 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第4期906-921,共16页
With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the netw... With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources. 展开更多
关键词 cloud-edge environment virtual network function(VNF)performance-resource(P-R)function edge resource allo-cation
暂未订购 下载PDF
Cloud-Edge Intelligent Collaborative Computing Model Based on Transfer Learning in IoT 认领 引用
10
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
A Cloud-Edge Collaborative System Based on the Framework of Multi-Device Semantic Interoperability in ICU 认领 引用
11
作者 Yan Zhuang Junyan Zhang +2 位作者 Juan Xu Desen Cao Kunlun He 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第2期1216-1232,共17页
Multi-source and multi-modal data in the medical area include structured data,texts,images,and continuous vital sign monitoring data generated by multiple devices of the Internet of Things,known as the Internet of med... Multi-source and multi-modal data in the medical area include structured data,texts,images,and continuous vital sign monitoring data generated by multiple devices of the Internet of Things,known as the Internet of medical things(IoMT)data.The IoMT system integrates a multitude of sensors,medical devices,and intelligent equipment in hospitals,leveraging perceptual and communication technologies,is popularized increasingly.The devices communicate through diverse protocols,and the absence of standardized IoMT interfaces presents a realistic dilemma in integrating IoMT data for holistic clinical analysis.Additionally,the scarcity of computing resources poses a constraint for the extensive training of models and the execution of complex reasoning processes,particularly in high-stakes settings such as intensive care unit(ICU).To address these challenges,we introduce a novel framework designed to facilitate semantic interoperability across multiple devices and to transform multi-source and multi-modal data into a unified data structure.Furthermore,we propose an innovative cloud-edge collaborative system,which could conduct intelligent computing in resource-constrained environments.Our approach was rigorously tested across various metrics,including system response time,data transmission latency,and overall system accuracy.The outcomes demonstrate clear advantages,and offer promising prospects for the future of medical data integration and analysis. 展开更多
关键词 multi-modal clinical data Internet of medical things(IoMT) semantic interoperability cloud-edge collaboration intelligent computing low-resource scenarios
暂未订购 下载PDF
Resource Optimisation Method for Multi-Agent Manufacturing System Based on Cloud-Edge Collaboration Architecture 认领 引用
12
作者 Zijian Zhu Zequn Zhang +2 位作者 Kai Chen Dunbing Tang Qixiang Cai 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第2期1198-1215,共18页
The multi-agent manufacturing system has emerged as a well-established paradigm in intelligent manufacturing.Presently,challenges such as limited adaptability,elevated maintenance expenses,and complexities in enabling... The multi-agent manufacturing system has emerged as a well-established paradigm in intelligent manufacturing.Presently,challenges such as limited adaptability,elevated maintenance expenses,and complexities in enabling local agent deployment at end devices persist.To address such issues,a deployment model for the multi-agent manufacturing system was proposed,leveraging a cloud-edge collaboration architecture.However,managing agents effectively in this environment to establish resilient services,which are services capable of maintaining high availability,stability,and reliability even in the face of uncertainty,emergencies,or failures,for manufacturing systems remains a critical challenge that requires immediate resolution.In the present study,a cloud-edge-end oriented deployment architecture for multi-agent manufacturing system was proposed,and a real-time mapping method between edge agents and production resources based on the 5th generation mobile communication technology is constructed.At the same time,a resource optimisation method called swarm avian evolutionary algorithm is proposed.This method integrates particle swarm optimisation and meta-heuristics to minimise computation time and enhance system response speed.Finally,the proposed resource optimisation method is compared with the genetic algorithm,particle swarm optimisation,and snake optimiser algorithms.The results demonstrate that the convergence time is significantly reduced,indicating that the proposed method offers superior performance. 展开更多
关键词 cloud-edge collaboration multi-agent system resource mapping service allocation optimisation
暂未订购 下载PDF
Duba:Cost-Efficient Serverless Cloud-Edge Collaborative Machine Learning Serving with Dual-Batching 认领 引用
13
作者 Jian-Xiong Liao Jing Peng +1 位作者 Zhi Zhou Fei Xu 《Journal of Computer Science & Technology》 SCIE EI CSCD 2026年第2期494-505,共12页
The integration of edge and serverless cloud computing,which combines the low-latency advantages of edge processing with the cost efficiency and scalability of serverless cloud architectures,provides an ideal foundati... The integration of edge and serverless cloud computing,which combines the low-latency advantages of edge processing with the cost efficiency and scalability of serverless cloud architectures,provides an ideal foundation for serving machine learning(ML)applications.While batching has demonstrated significant improvements in resource utilization through parallel execution,current approaches that independently optimize batching for edge or serverless cloud environments overlook their synergistic potential,leading to suboptimal end-to-end performance.To bridge this gap,we present Duba,a serverless cloud-edge collaborative system designed for cost-efficient ML serving.At its core,Duba introduces a novel dual-batching mechanism that harmonizes batching strategies across edge and serverless cloud environments.To implement this design,Duba combines lightweight configuration optimization with an adaptive scheduling policy,delivering substantial improvements in both cost efficiency and performance.Experimental results demonstrate that Duba consistently outperforms state-of-the-art systems,reducing serving costs by up to 74.1%and improving service-level objective(SLO)compliance by over 6.9%. 展开更多
关键词 serverless cloud-edge collaboration machine learning
暂未订购 下载PDF
Cloud-Edge-Collaboration-Based Flexibility Scheduling Strategy Considering Communication and Computation Delay 认领 引用
14
作者 Wei Zhang Hui Miao 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2025年第4期1858-1869,共12页
As the penetration rate of renewable energy sources(RES)gradually increases,demand-side resources(DSR)should be fully utilized to provide flexibility and rapidly respond to real-time power supply-demand imbalance.Howe... As the penetration rate of renewable energy sources(RES)gradually increases,demand-side resources(DSR)should be fully utilized to provide flexibility and rapidly respond to real-time power supply-demand imbalance.However,scheduling a large number of DSR clusters will inevitably bring unbearable transmission delay,and computation delay,which in turn lead to lower response speeds.This paper examines flexibility scheduling of DSR clusters within a smart distribution network(SDN)in view of both kinds of delay.Building upon a SDN model,maximum schedulable flexibility of DSR clusters is first quantified.Then,a flexibility response curve is analyzed to reflect the effect of delay on flexibility scheduling.Aiming at reducing flexibility shortage brought by delay,we propose a modified flexibility scheduling strategy based on cloud-edge collaboration.Compared with traditional strategy,centralized optimization is replaced by distributed optimization to consider both economic efficiency and effect of delay.Besides,an offloading strategy is also formulated to decide optimal edge nodes and corresponding wired paths for edge computations.In a case study,we evaluate scheduled flexibility,operational cost,average delay and the chosen edge nodes for edge computations with traditional strategy and our proposed strategy.Evaluation results show the proposed strategy can significantly reduce the effect of delay on flexibility scheduling,and guarantee the optimality of operational cost to some extent. 展开更多
关键词 Cloud-edge collaboration demand-side resources distributedooptimization flexibility scheduling offloading strategy smart distribution network
暂未订购 下载PDF
Multi-resource any price share fair allocation with placement constraints and an external resource in cloud-edge collaboration systems 认领 引用
15
作者 Bin Deng Guangqin Hu +1 位作者 Weidong Li Jin Xu 《CCF Transactions on High Performance Computing》 EI CSCD 2025年第6期652-670,共19页
In cloud-edge collaboration systems,real-time data generated by massive heterogeneous terminal devices,such as smart sensors,industrial controllers,and wearable devices,needs to be processed with low latency through d... In cloud-edge collaboration systems,real-time data generated by massive heterogeneous terminal devices,such as smart sensors,industrial controllers,and wearable devices,needs to be processed with low latency through distributed servers.However,the heterogeneity of servers,such as differences in computing power,storage,and dedicated acceleration chips,and placement constraints,such as location-sensitive devices only being able to access specific servers,make the multi-resource allocation problem highly complex.At the same time,as an external resource independent of the server,the limited bandwidth of wireless channels needs to be shared by all devices in competition,further exacerbating the difficulty of ensuring fairness.The existing multi-resource allocation mechanism does not consider the placement constraints of servers and the collaborative scheduling of communication computing resources.In addition,in cloud-edge collaboration systems,"least picky users",which can access all edge servers,coexist with"picky users",which can only access some nodes,and a new mechanism needs to be designed to avoid excessive resource allocation bias towards devices with limited access capabilities.This article proposes a multi-resource allocation mechanism based on any price share(APS)(Babaioff et al.in Math Oper Res 49(4):2180-2211,2023),called APSF,which achieves fair allocation of computing,storage,and communication resources in cloud-edge collaborative systems with placement constraints and an external resource.Through theoretical proof and large-scale simulation verification,the APSF mechanism significantly improves performance while ensuring important properties such as Pareto optimality,sharing incentive,strategy-proofness,local envy-freeness,and bottleneck fairness. 展开更多
关键词 Any price share Multi-resource allocation Fair allocation Cloud-edge collaboration
CloudEdgeRec:the cloud-edge joint strategy for short video recommendation 认领 引用
16
作者 Luo Wen Cheng Yalu Huang Fan 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2025年第4期34-44,共11页
Real-time performance is very important for recommender systems.In short video recommendation scenarios,users usually give explicit or implicit feedback in time during browsing,and the recommender system needs to sens... Real-time performance is very important for recommender systems.In short video recommendation scenarios,users usually give explicit or implicit feedback in time during browsing,and the recommender system needs to sense users'preferences in real time to meet their needs.However,traditional recommender systems are usually deployed on the cloud side,whenever the client requests the recommender system,it will return a list of short video results from the cloud side.Therefore,before the next recommendation request,the recommender system cannot adjust the recommendation result in real time according to the user's real-time feedback,resulting in an inaccurate recommender system on the cloud side.Consequently,in this paper,a cloud-edge joint strategy for short video recommendation(CloudEdgeRec)is proposed to address the aforementioned problems.Specifically,a lightweight model was deployed on edge devices to enable reranking based on user feedback.Furthermore,an interest-heuristic reranking(IHR)system was proposed to be implemented on the cloud side,which can provide a refresh mechanism to solve the problem that the limited cache on the edge devices cannot meet the drastic changes in user interests.The Markov decision process(MDP)is incorporated into IHR to preserve each generated distribution,and a matrix of exponential mean relevance is proposed to balance relationships between diversity and relevance.Finally,the experimental results show that both the offline evaluation of public datasets and online performance in short video platform demonstrate the effectiveness of CloudEdgeRec. 展开更多
关键词 short video recommendation cloud-edge rerank diversity
暂未订购 下载PDF
A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications 认领 引用 被引量：7
17
作者 Wenmin Lin Min Zhu +4 位作者 Xinyi Zhou Ruowei Zhang Xiaoran Zhao Shigen Shen Lu Sun 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第3期897-910,共14页
Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and... Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices.However,the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods.In view of these challenges,we propose a deep neural collaborative filtering based service recommendation method with multi-source data(i.e.,NCF-MS)in this paper,which adopts the cloud-edge collaboration computing paradigm to build recommendation model.More specifically,the Stacked Denoising Auto Encoder(SDAE)module is adopted to extract user/service features from auxiliary user profiles and service attributes.The Multiple Layer Perceptron(MLP)module is adopted to integrate the auxiliary user/service features to train the recommendation model.Finally,we evaluate the effectiveness of the NCF-MS method on three public datasets.The experimental results show that our proposed method achieves better performance than existing methods. 展开更多
关键词 deep neural collaborative filtering multi-source data cloud-edge collaboration application stackeddenoising auto encoder multiple layer perceptron
暂未订购 下载PDF
A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation 认领 引用 被引量：2
18
作者 Xiaojun BAI Yang ZHANG +2 位作者 Haixing WU Yuting WANG Shunfu JIN 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2024年第5期664-684,共21页
How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research topic.In this paper,we investigate the offoading decision,analytical model... How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research topic.In this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance measures.According to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant tasks.To meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,respectively.We establish a four-dimensional continuous-time Markov chain as the system model.By using the Gauss-Seidel method,we derive the stationary probability distribution of the system model.Accordingly,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of tasks.Numerical experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading scheme.Finally,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks. 展开更多
关键词 Edge computing Ofloading scheme Cloud-edge device collaboration Markov chain Cost function
暂未订购 下载PDF
Market Equilibrium Based on Cloud-edge Collaboration 认领 引用
19
作者 Tong Cheng Haiwang Zhong Qing Xia 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2024年第1期96-104,共9页
Market participants can only bid with lagged information disclosure under the existing market mechanism,which can lead to information asymmetry and irrational market behavior,thus influencing market efficiency.To prom... Market participants can only bid with lagged information disclosure under the existing market mechanism,which can lead to information asymmetry and irrational market behavior,thus influencing market efficiency.To promote rational bidding behavior of market participants and improve market efficiency,a novel electricity market mechanism based on cloudedge collaboration is proposed in this paper.Critical market information,called residual demand curve,is published to market participants in real-time on the cloud side,while participants on the edge side are allowed to adjust their bids according to the information disclosure prior to closure gate.The proposed mechanism can encourage rational bids in an incentive-compatible way through the process of dynamic equilibrium while protecting participants’privacy.This paper further formulates the mathematical model of market equilibrium to simulate the process of each market participant’s strategic bidding behavior towards equilibrium.A case study based on the IEEE 30-bus system shows the proposed market mechanism can effectively guide bidding behavior of market participants,while condensing exchanged information and protecting privacy of participants. 展开更多
关键词 Cloud-edge collaboration market mechanism residual demand curve strategic bidding
暂未订购 下载PDF
A Cloud-edge Cooperative Dispatching Method for Distribution Networks Considering Photovoltaic Generation Uncertainty 认领 引用 被引量：10
20
作者 Lu Shen Xiaobo Dou +3 位作者 Huan Long Chen Li Ji Zhou Kang Chen 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2021年第5期1111-1120,共10页
With the increasing penetration of renewable energy generation,uncertainty and randomness pose great challenges for optimal dispatching in distribution networks.We propose a cloud-edge cooperative dispatching(CECD)met... With the increasing penetration of renewable energy generation,uncertainty and randomness pose great challenges for optimal dispatching in distribution networks.We propose a cloud-edge cooperative dispatching(CECD)method to exploit the new opportunities offered by Internet of Things(IoT)technology.To alleviate the huge pressure on the modeling and computing of large-scale distribution system,the method deploys edge nodes in small-scale transformer areas in which robust optimization subproblem models are introduced to address the photovoltaic(PV)uncertainty.Considering the limited communication and computing capabilities of the edge nodes,the cloud center in the distribution automation system(DAS)establishes a utility grid master problem model that enforces the consistency between the solution at each edge node with the utility grid based on the alternating direction method of multipliers(ADMM).Furthermore,the voltage constraint derived from the linear power flow equations is adopted for enhancing the operation security of the distribution network.We perform a cloud-edge system simulation of the proposed CECD method and demonstrate a dispatching application.The case study is carried out on a modified 33-node system to verify the remarkable performance of the proposed model and method. 展开更多
关键词 Cloud-edge cooperative dispatching method transformer areas uncertainty alternating direction method of multipliers(ADMM)
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈