In optical metro-access networks,Access Points(APs)and Data Centers(DCs)are located on the fiber ring.In the cloud-centric solution,a large number of Internet of Things(IoT)data pose an enormous burden on DCs,so the V...In optical metro-access networks,Access Points(APs)and Data Centers(DCs)are located on the fiber ring.In the cloud-centric solution,a large number of Internet of Things(IoT)data pose an enormous burden on DCs,so the Virtual Machines(VMs)cannot be successfully launched due to the server overload.In addition,transferring the data from the AP to the remote DC may cause an undesirable delivery delay.For this end,we propose a promising solution considering the interplay between the cloud DC and edge APs.More specifically,bringing the partial capability of computing in APs close to things can reduce the pressure of DCs while guaranteeing the expected Quality of Service(QoS).In this work,when the cloud DC resource becomes limited,especially for delay sensitive but not computing-dependent IoT applications,we degrade their VMs and migrate them to edge APs instead of the remote DC.To avoid excessive VM degradation and computing offloading,we derive appropriate VM degradation coefficients based on classic microeconomic theory.Simulation results demonstrate that our algorithms improve the service providers'utility with the ratio from 34%to 89%over traditional cloud-centric solutions.展开更多
Video transcoding is to create multiple representations of a video for content adaptation.It is deemed as a core technique in Adaptive BitRate(ABR)streaming.How to manage video transcoding affects the performance of A...Video transcoding is to create multiple representations of a video for content adaptation.It is deemed as a core technique in Adaptive BitRate(ABR)streaming.How to manage video transcoding affects the performance of ABR streaming in various aspects,including operational cost,streaming delays,Quality of Experience(QoE),etc.Therefore,the problems of implementing video transcoding in ABR streaming must be systematically studied to improve the overall performance of the streaming services.These problems become more worthy of investigation with the emergence of the edge-cloud continuum,which makes the resource allocation for video transcoding more complicated.To this end,this paper provides an investigation of the main technical problems related to video transcoding in ABR streaming,including designing a rate profile for video transcoding,providing resources for video transcoding in clouds,and caching multi-bitrate video contents in networks,etc.We analyze these problems from the perspective of resource allocation in the edge-cloud continuum and cast them into resource and Quality of Service(QoS)optimization problems.The goal is to minimize resource consumption while guaranteeing the QoS for ABR streaming.We also discuss some promising research directions for the ABR streaming services.展开更多
Recently,the number of Internet of Things(IoT)devices connected to the Internet has increased dramatically as well as the data produced by these devices.This would require offloading IoT tasks to release heavy computa...Recently,the number of Internet of Things(IoT)devices connected to the Internet has increased dramatically as well as the data produced by these devices.This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing.However,different service architecture and offloading strategies have a different impact on the service time performance of IoT applications.Therefore,this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the network.Also,it introduces the offloading latency models to investigate the delay of different offloading scenarios/schemes and explores the effect of computational and communication demand on each one.A series of experiments conducted on an EdgeCloudSim show that different offloading decisions within the Edge-Cloud system can lead to various service times due to the computational resources and communications types.Finally,this paper presents a comprehensive review of the current state-of-the-art research on task offloading issues in the Edge-Cloud environment.展开更多
To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge serv...To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments.展开更多
Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteri...Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.展开更多
Nowadays,smart electricity grids are managed through advanced tools and techniques.The advent of Artificial Intelligence(AI)and network technology helps to control the energy demand.These advanced technologies can res...Nowadays,smart electricity grids are managed through advanced tools and techniques.The advent of Artificial Intelligence(AI)and network technology helps to control the energy demand.These advanced technologies can resolve common issues such as blackouts,optimal energy generation costs,and peakhours congestion.In this paper,the residential energy demand has been investigated and optimized to enhance the Quality of Service(QoS)to consumers.The energy consumption is distributed throughout the day to fulfill the demand in peak hours.Therefore,an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption.This model gives priority to consumer preferences while planning the operation of appliances.A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes.Furthermore,the allotment mechanism has been designed to manage the grid appliances through the edge node.The proposed model helps to improve the latency in the grid appliances scheduling process.展开更多
The architecture of edge-cloud cooperation is proposed as a compromising solution that combines the advantage of MEC and central cloud. In this paper we investigated the problem of how to reduce the average delay of M...The architecture of edge-cloud cooperation is proposed as a compromising solution that combines the advantage of MEC and central cloud. In this paper we investigated the problem of how to reduce the average delay of MEC application by collaborative task scheduling. The collaborative task scheduling is modeled as a constrained shortest path problem over an acyclic graph. By characterizing the optimal solution, the constrained optimization problem is simplified according to one-climb theory and enumeration algorithm. Generally, the edge-cloud collaborative task scheduling scheme performance better than independent scheme in reducing average delay. In heavy workload scenario, high blocking probability and retransmission delay at MEC is the key factor for average delay. Hence, more task executed on central cloud with abundant resource is the optimal scheme. Otherwise, transmission delay is inevitable compared with execution delay. MEC configured with higher priority and deployed close to terminals obtain more performance gain.展开更多
The rapid growth of online services has led to the emergence of many with similar functionalities,making it necessary to predict their non-functional attributes,namely quality of service(QoS).Traditional QoS predictio...The rapid growth of online services has led to the emergence of many with similar functionalities,making it necessary to predict their non-functional attributes,namely quality of service(QoS).Traditional QoS prediction approaches require users to upload their QoS data to the cloud for centralized training,leading to high user data upload latency.With the help of edge computing,users can upload data to edge servers(ESs)adjacent to them for training,reducing the upload latency.However,shallow models like matrix factorization(MF)are still used,which cannot sufficiently extract context features,resulting in low prediction accuracy.In this paper,we propose a context-aware edge-cloud collaboration framework for QoS prediction,named CQEC.Specially,to reduce the users upload latency,a distributed model training algorithm is designed with the collaboration of ESs and cloud.Furthermore,a context-aware prediction model based on convolutional neural network(CNN)and integrating attention mechanism is proposed to improve the performance.Experiments based on real-world dataset demonstrate that CQEC outperforms the baselines.展开更多
With the fast development of Mobile Internet,data traffic generated by end devices is anticipated to witness substantial growth in the future years.However,processing tasks locally will cause latency due to the limite...With the fast development of Mobile Internet,data traffic generated by end devices is anticipated to witness substantial growth in the future years.However,processing tasks locally will cause latency due to the limited resources of the end devices.Edge-cloud collaboration,an effective solution for latency-sensitive applications,is attracting greater attention from both industry and academia.It combines the advantages of the cloud center with abundant computing resources and edge nodes with low-latency capabilities.In this paper,we propose a two-stage task offloading framework with edge-cloud collaboration to assist end devices processing latency-sensitive tasks either on the edge servers or in the cloud center.As for homogeneous task offloading,in the first stage,the competitive end devices offload tasks to the edge gateways.We formulate the selfish task offloading problem among end devices as a potential game.In the second stage,the edge nodes request resources from the cloud center to process end devices tasks due to their limited resources.Then,we consider the heterogeneous task offloading problem and use intelligent optimization algorithm to obtain the optimal offloading strategy.Simulation results show that the service prices of edge nodes influence the decisions and task offloading costs of end devices.We also verify the intelligent optimization algorithm can achieve optimal performance with low complexity and fast convergence.展开更多
With the application of various information technologies in smart manufacturing,new intelligent production mode puts forward higher demands for real-time and robustness of production scheduling.For the production sche...With the application of various information technologies in smart manufacturing,new intelligent production mode puts forward higher demands for real-time and robustness of production scheduling.For the production scheduling problem in large-scale manufacturing environment,digital twin(DT)places high demand on data processing capability of the terminals.It requires both global prediction and real-time response abilities.In order to solve the above problem,a DT-based edge-cloud collaborative intelligent production scheduling(DTECCS)system was proposed,and the scheduling model and method were introduced.DT-based edge-cloud collaboration(ECC)can predict the production capacity of each workshop,reassemble customer orders,optimize the allocation of global manufacturing resources in the cloud,and carry out distributed scheduling on the edge-side to improve scheduling and tasks processing efficiency.In the production process,the DTECCS system adjusts scheduling strategies in real-time,responding to changes in production conditions and order fluctuations.Finally,simulation results show the effectiveness of DTECCS system.展开更多
Taking autonomous driving and driverless as the research object,we discuss and define intelligent high-precision map.Intelligent high-precision map is considered as a key link of future travel,a carrier of real-time p...Taking autonomous driving and driverless as the research object,we discuss and define intelligent high-precision map.Intelligent high-precision map is considered as a key link of future travel,a carrier of real-time perception of traffic resources in the entire space-time range,and the criterion for the operation and control of the whole process of the vehicle.As a new form of map,it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps.Thus,it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map.Accordingly,we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application.Next,we put forward the data logic structure of intelligent high-precision map,and analyze its application in autonomous driving.Then,we summarize the computing mode of“Crowdsourcing+Edge-Cloud Collaborative Computing”,and carry out key technical analysis on how to improve the quality of crowdsourced data.We also analyze the effective application scenarios of intelligent high-precision map in the future.Finally,we present some thoughts and suggestions for the future development of this field.展开更多
With the development of the Cyber-Physical Internet of Things System(CPIoTS),the number of Cyber-Physical System(CPS)applications accessed in networks has increased dramatically.Latency-sensitive resource orchestratio...With the development of the Cyber-Physical Internet of Things System(CPIoTS),the number of Cyber-Physical System(CPS)applications accessed in networks has increased dramatically.Latency-sensitive resource orchestration in CPS applications is extraordinarily essential for maintaining the Quality of Experience(QoE)for users.Although edge-cloud computing performs effectively in achieving latency-aware resource allocation in CPIoTS,existing methods fail to jointly consider the security and reliability requirements,thereby increasing the process latency of tasks and degrading the QoE of users.This paper aims to minimize the system latency of edge-cloud computing coupled with CPS while simultaneously considering the security and reliability requirements.We first consider a time-varying channel model as a Finite-State Markov Channel(FSMC)and propose a distributed blockchain-assisted CPIoTS to realize secure consensus and reliable resource orchestration by offloading computation tasks in edge-cloud computing.Moreover,we propose an efficient resource allocation algorithm,PPO-SRRA,that optimizes computing offloading and multi-dimension resource(e.g.,communication,computation,and consensus resource)allocation by using a policy-based Deep Reinforcement Learning(DRL)method.The experimental results show that the proposed resource allocation scheme can reduce the system latency and ensure consensus security.展开更多
Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the eff...Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.展开更多
Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems....Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.展开更多
基金supported by the Researchers Supporting Project of King Saud University,Riyadh,Saudi Arabia,under Project RSPD2025R681。
文摘In optical metro-access networks,Access Points(APs)and Data Centers(DCs)are located on the fiber ring.In the cloud-centric solution,a large number of Internet of Things(IoT)data pose an enormous burden on DCs,so the Virtual Machines(VMs)cannot be successfully launched due to the server overload.In addition,transferring the data from the AP to the remote DC may cause an undesirable delivery delay.For this end,we propose a promising solution considering the interplay between the cloud DC and edge APs.More specifically,bringing the partial capability of computing in APs close to things can reduce the pressure of DCs while guaranteeing the expected Quality of Service(QoS).In this work,when the cloud DC resource becomes limited,especially for delay sensitive but not computing-dependent IoT applications,we degrade their VMs and migrate them to edge APs instead of the remote DC.To avoid excessive VM degradation and computing offloading,we derive appropriate VM degradation coefficients based on classic microeconomic theory.Simulation results demonstrate that our algorithms improve the service providers'utility with the ratio from 34%to 89%over traditional cloud-centric solutions.
基金supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20200486.
文摘Video transcoding is to create multiple representations of a video for content adaptation.It is deemed as a core technique in Adaptive BitRate(ABR)streaming.How to manage video transcoding affects the performance of ABR streaming in various aspects,including operational cost,streaming delays,Quality of Experience(QoE),etc.Therefore,the problems of implementing video transcoding in ABR streaming must be systematically studied to improve the overall performance of the streaming services.These problems become more worthy of investigation with the emergence of the edge-cloud continuum,which makes the resource allocation for video transcoding more complicated.To this end,this paper provides an investigation of the main technical problems related to video transcoding in ABR streaming,including designing a rate profile for video transcoding,providing resources for video transcoding in clouds,and caching multi-bitrate video contents in networks,etc.We analyze these problems from the perspective of resource allocation in the edge-cloud continuum and cast them into resource and Quality of Service(QoS)optimization problems.The goal is to minimize resource consumption while guaranteeing the QoS for ABR streaming.We also discuss some promising research directions for the ABR streaming services.
基金In addition,the authors would like to thank the Deanship of Scientific Research,Prince Sattam bin Abdulaziz University,Al-Kharj,Saudi Arabia,for supporting this work.
文摘Recently,the number of Internet of Things(IoT)devices connected to the Internet has increased dramatically as well as the data produced by these devices.This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing.However,different service architecture and offloading strategies have a different impact on the service time performance of IoT applications.Therefore,this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the network.Also,it introduces the offloading latency models to investigate the delay of different offloading scenarios/schemes and explores the effect of computational and communication demand on each one.A series of experiments conducted on an EdgeCloudSim show that different offloading decisions within the Edge-Cloud system can lead to various service times due to the computational resources and communications types.Finally,this paper presents a comprehensive review of the current state-of-the-art research on task offloading issues in the Edge-Cloud environment.
基金supported by 2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R. China “Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks”Research on Key Technologies of wireless edge intelligent collaboration for industrial internet scenarios (L202017)+1 种基金Natural Science Foundation of China, No.61971050BUPT Excellent Ph.D. Students Foundation (CX2020214)。
文摘To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments.
基金supported by the National Natural Science Foundation of China(No.61903023)the Natural Science Foundation of Bejing Municipality(No.4204110)+1 种基金State Key Laboratory of Rail Traffic Control and Safety(No.RCS2020ZT006,RCS2021ZT006)the Fundamental Research Funds for the Central Universities(No.2020JBM087).
文摘Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.
文摘Nowadays,smart electricity grids are managed through advanced tools and techniques.The advent of Artificial Intelligence(AI)and network technology helps to control the energy demand.These advanced technologies can resolve common issues such as blackouts,optimal energy generation costs,and peakhours congestion.In this paper,the residential energy demand has been investigated and optimized to enhance the Quality of Service(QoS)to consumers.The energy consumption is distributed throughout the day to fulfill the demand in peak hours.Therefore,an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption.This model gives priority to consumer preferences while planning the operation of appliances.A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes.Furthermore,the allotment mechanism has been designed to manage the grid appliances through the edge node.The proposed model helps to improve the latency in the grid appliances scheduling process.
文摘The architecture of edge-cloud cooperation is proposed as a compromising solution that combines the advantage of MEC and central cloud. In this paper we investigated the problem of how to reduce the average delay of MEC application by collaborative task scheduling. The collaborative task scheduling is modeled as a constrained shortest path problem over an acyclic graph. By characterizing the optimal solution, the constrained optimization problem is simplified according to one-climb theory and enumeration algorithm. Generally, the edge-cloud collaborative task scheduling scheme performance better than independent scheme in reducing average delay. In heavy workload scenario, high blocking probability and retransmission delay at MEC is the key factor for average delay. Hence, more task executed on central cloud with abundant resource is the optimal scheme. Otherwise, transmission delay is inevitable compared with execution delay. MEC configured with higher priority and deployed close to terminals obtain more performance gain.
基金supported by the National Natural Science Foundation of China(Nos.41975183 and 41875184).
文摘The rapid growth of online services has led to the emergence of many with similar functionalities,making it necessary to predict their non-functional attributes,namely quality of service(QoS).Traditional QoS prediction approaches require users to upload their QoS data to the cloud for centralized training,leading to high user data upload latency.With the help of edge computing,users can upload data to edge servers(ESs)adjacent to them for training,reducing the upload latency.However,shallow models like matrix factorization(MF)are still used,which cannot sufficiently extract context features,resulting in low prediction accuracy.In this paper,we propose a context-aware edge-cloud collaboration framework for QoS prediction,named CQEC.Specially,to reduce the users upload latency,a distributed model training algorithm is designed with the collaboration of ESs and cloud.Furthermore,a context-aware prediction model based on convolutional neural network(CNN)and integrating attention mechanism is proposed to improve the performance.Experiments based on real-world dataset demonstrate that CQEC outperforms the baselines.
基金the National Natural Science Foundation of China under Grant No.71971188the Humanity and Social Science Foundation of Ministry of Education of China under Grant No.22YJCZH086+1 种基金the Hebei Natural Science Foundation under Grant Nos.G2022203003 and G2023203008the support Funded by Science Research Project of Hebei Education Department under Grant No.ZD2022142.
文摘With the fast development of Mobile Internet,data traffic generated by end devices is anticipated to witness substantial growth in the future years.However,processing tasks locally will cause latency due to the limited resources of the end devices.Edge-cloud collaboration,an effective solution for latency-sensitive applications,is attracting greater attention from both industry and academia.It combines the advantages of the cloud center with abundant computing resources and edge nodes with low-latency capabilities.In this paper,we propose a two-stage task offloading framework with edge-cloud collaboration to assist end devices processing latency-sensitive tasks either on the edge servers or in the cloud center.As for homogeneous task offloading,in the first stage,the competitive end devices offload tasks to the edge gateways.We formulate the selfish task offloading problem among end devices as a potential game.In the second stage,the edge nodes request resources from the cloud center to process end devices tasks due to their limited resources.Then,we consider the heterogeneous task offloading problem and use intelligent optimization algorithm to obtain the optimal offloading strategy.Simulation results show that the service prices of edge nodes influence the decisions and task offloading costs of end devices.We also verify the intelligent optimization algorithm can achieve optimal performance with low complexity and fast convergence.
基金supported by the 2020 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R.Chinathe State Grid Liaoning Electric Power Supply Co.,Ltd.,Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks。
文摘With the application of various information technologies in smart manufacturing,new intelligent production mode puts forward higher demands for real-time and robustness of production scheduling.For the production scheduling problem in large-scale manufacturing environment,digital twin(DT)places high demand on data processing capability of the terminals.It requires both global prediction and real-time response abilities.In order to solve the above problem,a DT-based edge-cloud collaborative intelligent production scheduling(DTECCS)system was proposed,and the scheduling model and method were introduced.DT-based edge-cloud collaboration(ECC)can predict the production capacity of each workshop,reassemble customer orders,optimize the allocation of global manufacturing resources in the cloud,and carry out distributed scheduling on the edge-side to improve scheduling and tasks processing efficiency.In the production process,the DTECCS system adjusts scheduling strategies in real-time,responding to changes in production conditions and order fluctuations.Finally,simulation results show the effectiveness of DTECCS system.
基金National Key Research and Development Program(No.2018YFB1305001)Major Consulting and Research Project of Chinese Academy of Engineering(No.2018-ZD-02-07)。
文摘Taking autonomous driving and driverless as the research object,we discuss and define intelligent high-precision map.Intelligent high-precision map is considered as a key link of future travel,a carrier of real-time perception of traffic resources in the entire space-time range,and the criterion for the operation and control of the whole process of the vehicle.As a new form of map,it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps.Thus,it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map.Accordingly,we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application.Next,we put forward the data logic structure of intelligent high-precision map,and analyze its application in autonomous driving.Then,we summarize the computing mode of“Crowdsourcing+Edge-Cloud Collaborative Computing”,and carry out key technical analysis on how to improve the quality of crowdsourced data.We also analyze the effective application scenarios of intelligent high-precision map in the future.Finally,we present some thoughts and suggestions for the future development of this field.
基金This work has been supported by the National Natural Science Foundation of China(Nos.62071354)Open Research Projects of Zhejiang Lab(No.2019KD0AD01/013)+1 种基金the Key Research and Development Program of Shaanxi(ProgramNo.2022ZDLGY05-08)also supported by the ISN State Key Laboratory.
文摘With the development of the Cyber-Physical Internet of Things System(CPIoTS),the number of Cyber-Physical System(CPS)applications accessed in networks has increased dramatically.Latency-sensitive resource orchestration in CPS applications is extraordinarily essential for maintaining the Quality of Experience(QoE)for users.Although edge-cloud computing performs effectively in achieving latency-aware resource allocation in CPIoTS,existing methods fail to jointly consider the security and reliability requirements,thereby increasing the process latency of tasks and degrading the QoE of users.This paper aims to minimize the system latency of edge-cloud computing coupled with CPS while simultaneously considering the security and reliability requirements.We first consider a time-varying channel model as a Finite-State Markov Channel(FSMC)and propose a distributed blockchain-assisted CPIoTS to realize secure consensus and reliable resource orchestration by offloading computation tasks in edge-cloud computing.Moreover,we propose an efficient resource allocation algorithm,PPO-SRRA,that optimizes computing offloading and multi-dimension resource(e.g.,communication,computation,and consensus resource)allocation by using a policy-based Deep Reinforcement Learning(DRL)method.The experimental results show that the proposed resource allocation scheme can reduce the system latency and ensure consensus security.
基金supported by National Natural Science Foundation of China(NSFC)under Grant Number T2350710232.
文摘Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.
文摘Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.
