Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient int...Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite networks.Specifically,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization efficiency.Additionally,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two sub-problems.In terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational resources.To deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration.Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.展开更多
Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for t...Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.展开更多
As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can p...As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.展开更多
Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in im...Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.展开更多
Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal ...Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.展开更多
The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC n...The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.展开更多
In order to provide a theoretical guide for choosing the material for the hawsers for the FPSO side-by-side offloading system, which is moored by the yoke system, the 3D potential flow theory and full coupled time-dom...In order to provide a theoretical guide for choosing the material for the hawsers for the FPSO side-by-side offloading system, which is moored by the yoke system, the 3D potential flow theory and full coupled time-domain analysis are presented to study the dynamic response of the offloading system. The MingZhu FPSO offloading system in the field BZ25-1 is simulated here; and four different characteristic fiber ropes are used as the material for the hawsers. To acquire an accurate hawser line tension, the polynomial fitting method is used to calculate the nonlinear stiffness of the hawsers. By comparing the hawser lines＇ tension and the relative motion between the FPSO and the shuttle tanker, a suitable material for the hawser lines is chosen and discussed in this paper. The results indicate that the nonlinear stiffness characteristic of the fiber rope has a small effect on the relative motion of the vessels, but the hawser lines＇ tension is greatly influenced by the different characteristics of the fiber ropes. The hawser lines＇ tension with nonlinear stiffness is in accordance with the one with the upper and lower bound linear stiffness, which proves this method of fitting the fiber ropes＇ nonlinear stiffness is reasonable and reliable.展开更多
With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This ...With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases.展开更多
With the emergence of advanced vehicular applications, the challenge of satisfying computational and communication demands of vehicles has become increasingly prominent. Fog computing is a potential solution to improv...With the emergence of advanced vehicular applications, the challenge of satisfying computational and communication demands of vehicles has become increasingly prominent. Fog computing is a potential solution to improve advanced vehicular services by enabling computational offloading at the edge of network. In this paper, we propose a fog-cloud computational offloading algorithm in Internet of Vehicles(IoV) to both minimize the power consumption of vehicles and that of the computational facilities. First, we establish the system model, and then formulate the offloading problem as an optimization problem, which is NP-hard. After that, we propose a heuristic algorithm to solve the offloading problem gradually. Specifically, we design a predictive combination transmission mode for vehicles, and establish a deep learning model for computational facilities to obtain the optimal workload allocation. Simulation results demonstrate the superiority of our algorithm in energy efficiency and network latency.展开更多
In this paper,we investigate vehicular fog computing system and develop an effective parallel offloading scheme.The service time,that addresses task offloading delay,task decomposition and handover cost,is adopted as ...In this paper,we investigate vehicular fog computing system and develop an effective parallel offloading scheme.The service time,that addresses task offloading delay,task decomposition and handover cost,is adopted as the metric of offloading performance.We propose an available resource-aware based parallel offloading scheme,which decides target fog nodes by RSU for computation offloading jointly considering effect of vehicles mobility and time-varying computation capability.Based on Hidden Markov model and Markov chain theories,proposed scheme effectively handles the imperfect system state information for fog nodes selection by jointly achieving mobility awareness and computation perception.Simulation results are presented to corroborate the theoretical analysis and validate the effectiveness of the proposed algorithm.展开更多
Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers...Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios.Meanwhile,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to appear.Compared with traditional Internet business,these computation tasks have higher processing priority and lower delay requirements.In this paper,we design a 5G-based vehicle-aware Multi-access Edge Computing network(VAMECN)and propose a joint optimization problem of minimizing total system cost.In view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication bandwidth.And,the mixed integer nonlinear programming problem is described as a Markov Decision Process.Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.展开更多
Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy lo...Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations.Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.展开更多
Intelligent edge computing carries out edge devices of the Internet of things(Io T) for data collection, calculation and intelligent analysis, so as to proceed data analysis nearby and make feedback timely. Because of...Intelligent edge computing carries out edge devices of the Internet of things(Io T) for data collection, calculation and intelligent analysis, so as to proceed data analysis nearby and make feedback timely. Because of the mobility of mobile equipments(MEs), if MEs move among the reach of the small cell networks(SCNs), the offloaded tasks cannot be returned to MEs successfully. As a result, migration incurs additional costs. In this paper, joint task offloading and migration schemes in mobility-aware Mobile Edge Computing(MEC) network based on Reinforcement Learning(RL) are proposed to obtain the maximum system revenue. Firstly, the joint optimization problems of maximizing the total revenue of MEs are put forward, in view of the mobility-aware MEs. Secondly, considering time-varying computation tasks and resource conditions, the mixed integer non-linear programming(MINLP) problem is described as a Markov Decision Process(MDP). Then we propose a novel reinforcement learning-based optimization framework to work out the problem, instead traditional methods. Finally, it is shown that the proposed schemes can obviously raise the total revenue of MEs by giving simulation results.展开更多
Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the ...Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the computing ability at the network edge,multi-access edge computing(MEC)is a promising technique to tackle such challenges.Compared to traditional full offloading,partial offloading offers more flexibility in the perspective of application as well as deployment of such systems.Hence,in this paper,we investigate the application of partial computing offloading in-vehicle networks.In particular,by analyzing the structure of many emerging applications,e.g.,AR and online games,we convert the application structure into a sequential multi-component model.Focusing on shortening the application execution delay,we extend the optimization problem from the single-vehicle computing offloading(SVCOP)scenario to the multi-vehicle computing offloading(MVCOP)by taking multiple constraints into account.A deep reinforcement learning(DRL)based algorithm is proposed as a solution to this problem.Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.展开更多
The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation tasks.To face the challenge,an Offloading scheme leveraging on NEighboring node Resourc...The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation tasks.To face the challenge,an Offloading scheme leveraging on NEighboring node Resources(ONER)for EC over Fiber-Wireless(FiWi)access networks is proposed in this paper.In the ONER scheme,the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly,so that it can support the offloading transmission with low delay and wide bandwidth.Based on the ONER scheme supported by FiWi networks,computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops(e.g.,just one wireless hop),which achieves low delay.Additionally,an efficient Computation Resource Scheduling(CRS)algorithm based on the ONER scheme is also proposed to make offloading decision.The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm.Therefore,the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.展开更多
The cloud radio access network(C-RAN) and the fog computing have been recently proposed to tackle the dramatically increasing traffic demands and to provide better quality of service(QoS) to user equipment(UE).Conside...The cloud radio access network(C-RAN) and the fog computing have been recently proposed to tackle the dramatically increasing traffic demands and to provide better quality of service(QoS) to user equipment(UE).Considering the better computation capability of the cloud RAN(10 times larger than that of the fog RAN) and the lower transmission delay of the fog computing,we propose a joint resource allocation and coordinated computation offloading algorithm for the fog RAN(F-RAN),which takes the advantage of C-RAN and fog computing.Specifically,the F-RAN splits a computation task into the fog computing part and the cloud computing part.Based on the constraints of maximum transmission delay tolerance,fronthaul and backhaul capacity limits,we minimize the energy cost and obtain optimal computational resource allocation for multiple UE,transmission power allocation of each UE and the event splitting factor.Numerical results have been proposed with the comparison of existing methods.展开更多
In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal de...In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.展开更多
Smart containers have been extensively applied in the maritime industry by embracing the Internet of Things to realize container status monitoring and data offloading without human intervention.However, the offloading...Smart containers have been extensively applied in the maritime industry by embracing the Internet of Things to realize container status monitoring and data offloading without human intervention.However, the offloading rate and delay in the offshore region are limited by the coverage of the onshore base station(BS). In this paper, we investigate the unmanned aerial vehicle(UAV)-assisted data offloading for smart containers in offshore maritime communications where the UAV is as a relay node between smart containers and onshore BS. We first consider the mobility of container vessel in the offshore region and establish a UAV-assisted data offloading model. Based on this model, a data offloading algorithm is proposed to reduce the average offloading delay under data-size requirements and available energy constraints of smart containers. Specifically, the convex-concave procedure is used to update time-slot assignment,offloading approach selection, and power allocation in an iterative manner. Simulation results show that the proposed algorithm can efficiently reduce average offloading delay and increase offloading success ratio.Moreover, it is shown that the UAV relay cannot always bring the performance gain on offloading delay especially in the close-to-shore area, which could give an insight on the deployment of UAV relay in offshore communications.展开更多
Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a co...Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading(DDLCCO) algorithm, where multiple parallel deep neural networks(DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.展开更多
基金supported in part by Sub Project of National Key Research and Development plan in 2020 NO.2020YFC1511704Beijing Information Science and Technology University NO.2020KYNH212,NO.2021CGZH302+1 种基金Beijing Science and Technology Project(Grant No.Z211100004421009)in part by the National Natural Science Foundation of China(Grant No.62301058).
文摘Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite networks.Specifically,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization efficiency.Additionally,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two sub-problems.In terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational resources.To deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration.Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.
基金supported by National Natural Science Foundation of China No.62231012Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province under Grant YQ2020F001Heilongjiang Province Postdoctoral General Foundation under Grant AUGA4110004923.
文摘Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.
文摘As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.
基金supported by the Talent Fund of Beijing Jiaotong University(No.2023XKRC028)CCFLenovo Blue Ocean Research Fund and Beijing Natural Science Foundation under Grant(No.L221003).
文摘Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility.
基金supported by National Key Research and Development Program of China(2018YFC1504502).
文摘Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.
基金supported in part by the National Natural Science Foundation of China under Grants 62201105,62331017,and 62075024in part by the Natural Science Foundation of Chongqing under Grant cstc2021jcyj-msxmX0404+1 种基金in part by the Chongqing Municipal Education Commission under Grant KJQN202100643in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515110056.
文摘The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.
基金Supported by the National 111 Project of China under Grant No.B07019Important National Science & Technology Specific Project:Numerical Simulation and Experimental Investigation of FPSO and Offloading System,No.2011ZX05030-006-002
文摘In order to provide a theoretical guide for choosing the material for the hawsers for the FPSO side-by-side offloading system, which is moored by the yoke system, the 3D potential flow theory and full coupled time-domain analysis are presented to study the dynamic response of the offloading system. The MingZhu FPSO offloading system in the field BZ25-1 is simulated here; and four different characteristic fiber ropes are used as the material for the hawsers. To acquire an accurate hawser line tension, the polynomial fitting method is used to calculate the nonlinear stiffness of the hawsers. By comparing the hawser lines＇ tension and the relative motion between the FPSO and the shuttle tanker, a suitable material for the hawser lines is chosen and discussed in this paper. The results indicate that the nonlinear stiffness characteristic of the fiber rope has a small effect on the relative motion of the vessels, but the hawser lines＇ tension is greatly influenced by the different characteristics of the fiber ropes. The hawser lines＇ tension with nonlinear stiffness is in accordance with the one with the upper and lower bound linear stiffness, which proves this method of fitting the fiber ropes＇ nonlinear stiffness is reasonable and reliable.
基金supported by the Fundamental Research Funds for the Central Universities (No. 2018YJS008)the National Natural Science Foundation of China (61471031, 61661021, 61531009)+4 种基金Beijing Natural Science Foundation (L182018)the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2017D14)the State Key Laboratory of Rail Traffi c Control and Safety (Contract No. RCS2017K009)Science and Technology Program of Jiangxi Province (20172BCB22016, 20171BBE50057)Shenzhen Science and Technology Program under Grant (No. JCYJ20170817110410346)
文摘With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases.
基金supported by National Natural Science Foundation of China with No. 61733002 and 61842601National Key Research and Development Plan 2017YFC0821003-2the Fundamental Research Funds for the Central University with No. DUT17LAB16 and No. DUT2017TB02
文摘With the emergence of advanced vehicular applications, the challenge of satisfying computational and communication demands of vehicles has become increasingly prominent. Fog computing is a potential solution to improve advanced vehicular services by enabling computational offloading at the edge of network. In this paper, we propose a fog-cloud computational offloading algorithm in Internet of Vehicles(IoV) to both minimize the power consumption of vehicles and that of the computational facilities. First, we establish the system model, and then formulate the offloading problem as an optimization problem, which is NP-hard. After that, we propose a heuristic algorithm to solve the offloading problem gradually. Specifically, we design a predictive combination transmission mode for vehicles, and establish a deep learning model for computational facilities to obtain the optimal workload allocation. Simulation results demonstrate the superiority of our algorithm in energy efficiency and network latency.
基金supported in part by the National Natural Science Foundation of China under Grant 61971077,Grant 61901066in part by the Chongqing Science and Technology Commission under Grant cstc2019jcyj-msxmX0575in part by the Program for Innovation Team Building at colleges and universities in Chongqing,China under Grant CXTDX201601006
文摘In this paper,we investigate vehicular fog computing system and develop an effective parallel offloading scheme.The service time,that addresses task offloading delay,task decomposition and handover cost,is adopted as the metric of offloading performance.We propose an available resource-aware based parallel offloading scheme,which decides target fog nodes by RSU for computation offloading jointly considering effect of vehicles mobility and time-varying computation capability.Based on Hidden Markov model and Markov chain theories,proposed scheme effectively handles the imperfect system state information for fog nodes selection by jointly achieving mobility awareness and computation perception.Simulation results are presented to corroborate the theoretical analysis and validate the effectiveness of the proposed algorithm.
基金supported in part by the National Key R&D Program of China under Grant 2018YFC0831502.
文摘Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios.Meanwhile,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to appear.Compared with traditional Internet business,these computation tasks have higher processing priority and lower delay requirements.In this paper,we design a 5G-based vehicle-aware Multi-access Edge Computing network(VAMECN)and propose a joint optimization problem of minimizing total system cost.In view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication bandwidth.And,the mixed integer nonlinear programming problem is described as a Markov Decision Process.Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.
基金supported by the National High-Tech R&D Program (863 Program) under grant No. 2015AA01A705Beijing Municipal Science and Technology Commission research fund project under grant No. D151100000115002+1 种基金China Scholarship Council under grant No. 201406470038BUPT youth scientific research innovation program under grant No. 500401238
文摘Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations.Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.
基金supported in part by the National Natural Science Foundation of China under Grant 61701038。
文摘Intelligent edge computing carries out edge devices of the Internet of things(Io T) for data collection, calculation and intelligent analysis, so as to proceed data analysis nearby and make feedback timely. Because of the mobility of mobile equipments(MEs), if MEs move among the reach of the small cell networks(SCNs), the offloaded tasks cannot be returned to MEs successfully. As a result, migration incurs additional costs. In this paper, joint task offloading and migration schemes in mobility-aware Mobile Edge Computing(MEC) network based on Reinforcement Learning(RL) are proposed to obtain the maximum system revenue. Firstly, the joint optimization problems of maximizing the total revenue of MEs are put forward, in view of the mobility-aware MEs. Secondly, considering time-varying computation tasks and resource conditions, the mixed integer non-linear programming(MINLP) problem is described as a Markov Decision Process(MDP). Then we propose a novel reinforcement learning-based optimization framework to work out the problem, instead traditional methods. Finally, it is shown that the proposed schemes can obviously raise the total revenue of MEs by giving simulation results.
基金the National Natural Science Foundation of China(NSFC)(Grant No.61671072).
文摘Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the computing ability at the network edge,multi-access edge computing(MEC)is a promising technique to tackle such challenges.Compared to traditional full offloading,partial offloading offers more flexibility in the perspective of application as well as deployment of such systems.Hence,in this paper,we investigate the application of partial computing offloading in-vehicle networks.In particular,by analyzing the structure of many emerging applications,e.g.,AR and online games,we convert the application structure into a sequential multi-component model.Focusing on shortening the application execution delay,we extend the optimization problem from the single-vehicle computing offloading(SVCOP)scenario to the multi-vehicle computing offloading(MVCOP)by taking multiple constraints into account.A deep reinforcement learning(DRL)based algorithm is proposed as a solution to this problem.Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.
基金supported by National Natural Science Foundation of China(Grant No.61471053,61901052)Fundamental Research Funds for the Central Universities(Grant 2018RC03)Beijing Laboratory of Advanced Information Networks
文摘The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation tasks.To face the challenge,an Offloading scheme leveraging on NEighboring node Resources(ONER)for EC over Fiber-Wireless(FiWi)access networks is proposed in this paper.In the ONER scheme,the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly,so that it can support the offloading transmission with low delay and wide bandwidth.Based on the ONER scheme supported by FiWi networks,computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops(e.g.,just one wireless hop),which achieves low delay.Additionally,an efficient Computation Resource Scheduling(CRS)algorithm based on the ONER scheme is also proposed to make offloading decision.The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm.Therefore,the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.
基金supported in part by National Natural Science Foundation of China(No. 61372070)Natural Science Basic Research Plan in Shaanxi Province of China(No. 2015JM6324)+3 种基金Ningbo Natural Science Foundation(2015A610117)Hong Kong, Macao and Taiwan Science & Technology Cooperation Program of China(No. 2015DFT10160)EU FP7 Project MONICA (No.PIRSES-GA-2011-295222)the 111 Project(No.B08038)
文摘The cloud radio access network(C-RAN) and the fog computing have been recently proposed to tackle the dramatically increasing traffic demands and to provide better quality of service(QoS) to user equipment(UE).Considering the better computation capability of the cloud RAN(10 times larger than that of the fog RAN) and the lower transmission delay of the fog computing,we propose a joint resource allocation and coordinated computation offloading algorithm for the fog RAN(F-RAN),which takes the advantage of C-RAN and fog computing.Specifically,the F-RAN splits a computation task into the fog computing part and the cloud computing part.Based on the constraints of maximum transmission delay tolerance,fronthaul and backhaul capacity limits,we minimize the energy cost and obtain optimal computational resource allocation for multiple UE,transmission power allocation of each UE and the event splitting factor.Numerical results have been proposed with the comparison of existing methods.
基金supported by Qinghai Natural Science Foundation under No.2020-ZJ-943Q.
文摘In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.
基金supported in part by National Key Research and Development Program of China under Grant 2019YFE0111600in part by National Natural Science Foundation of China under Grants 62101089, 62002042, 61971083, and 51939001+4 种基金in part by China Postdoctoral Science Foundation under Grants 2021M700655 and 2021M690022in part by Cooperative Scientific Research Project, Chunhui Program of Ministry of Education, P. R. Chinain part by LiaoNing Revitalization Talents Program under Grant XLYC2002078in part by Dalian Science and Technology Innovation Fund under Grant 2019J11CY015in part by the Fundamental Research Funds for Central Universities under Grants 3132021237 and 3132021223。
文摘Smart containers have been extensively applied in the maritime industry by embracing the Internet of Things to realize container status monitoring and data offloading without human intervention.However, the offloading rate and delay in the offshore region are limited by the coverage of the onshore base station(BS). In this paper, we investigate the unmanned aerial vehicle(UAV)-assisted data offloading for smart containers in offshore maritime communications where the UAV is as a relay node between smart containers and onshore BS. We first consider the mobility of container vessel in the offshore region and establish a UAV-assisted data offloading model. Based on this model, a data offloading algorithm is proposed to reduce the average offloading delay under data-size requirements and available energy constraints of smart containers. Specifically, the convex-concave procedure is used to update time-slot assignment,offloading approach selection, and power allocation in an iterative manner. Simulation results show that the proposed algorithm can efficiently reduce average offloading delay and increase offloading success ratio.Moreover, it is shown that the UAV relay cannot always bring the performance gain on offloading delay especially in the close-to-shore area, which could give an insight on the deployment of UAV relay in offshore communications.
基金partially supported by the National Key R&D Program of China (2020YFB1806900)by Ericsson, by the Natural Science Foundation of Jiangsu Province (No. BK20200822)+1 种基金by the Natural Science Foundation of Jiangsu Higher Education Institutions of China (No. 20KJB510036)by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications),Ministry of Education (No. JZNY202103)。
文摘Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading(DDLCCO) algorithm, where multiple parallel deep neural networks(DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.
