Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the re...Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting.展开更多
In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computi...In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.展开更多
With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these...With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.展开更多
Driven by diverse intelligent applications,computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes,forming a distributed computing power network.Tasked with bo...Driven by diverse intelligent applications,computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes,forming a distributed computing power network.Tasked with both packet transmission and data processing,it requires joint optimization of communications and computing.Considering the diverse requirements of applications,we develop a dynamic control policy of routing to determine both paths and computing nodes in a distributed computing power network.Different from traditional routing protocols,additional metrics related to computing are taken into consideration in the proposed policy.Based on the multi-attribute decision theory and the fuzzy logic theory,we propose two routing selection algorithms,the Fuzzy Logic-Based Routing(FLBR)algorithm and the low-complexity Pairwise Multi-Attribute Decision-Making(l PMADM)algorithm.Simulation results show that the proposed policy could achieve better performance in average processing delay,user satisfaction,and load balancing compared with existing works.展开更多
As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and...As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and untrusted device terminals.Blockchain,as a shared,immutable distributed ledger,provides a secure resource management solution for WCPN.However,integrating blockchain into WCPN faces challenges like device heterogeneity,monitoring communication states,and dynamic network nature.Whereas Digital Twins(DT)can accurately maintain digital models of physical entities through real-time data updates and self-learning,enabling continuous optimization of WCPN,improving synchronization performance,ensuring real-time accuracy,and supporting smooth operation of WCPN services.In this paper,we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models.We adopt an enumeration-based optimal placement algorithm(EOPA)and an improved simulated annealing-based near-optimal placement algorithm(ISAPA)to achieve minimum average DT synchronization latency under the constraint of DT error.Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency.展开更多
Computing Power Network(CPN)is emerging as one of the important research interests in beyond 5G(B5G)or 6G.This paper constructs a CPN based on Federated Learning(FL),where all Multi-access Edge Computing(MEC)servers a...Computing Power Network(CPN)is emerging as one of the important research interests in beyond 5G(B5G)or 6G.This paper constructs a CPN based on Federated Learning(FL),where all Multi-access Edge Computing(MEC)servers are linked to a computing power center via wireless links.Through this FL procedure,each MEC server in CPN can independently train the learning models using localized data,thus preserving data privacy.However,it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers.To address these issues,we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers.Afterwards,we formulate a comprehensive algorithm to jointly optimize the communication resource(wireless bandwidth and transmission power)allocations and the computation resource(computation capacity of MEC servers)allocations while ensuring the local accuracy of the training of each MEC server.The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.展开更多
Federated Learning(FL)is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security.However,the traditional FL model in communication sce...Federated Learning(FL)is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security.However,the traditional FL model in communication scenarios,whether for uplink or downlink communications,may give rise to several network problems,such as bandwidth occupation,additional network latency,and bandwidth fragmentation.In this paper,we propose an adaptive chained training approach(Fed ACT)for FL in computing power networks.First,a Computation-driven Clustering Strategy(CCS)is designed.The server clusters clients by task processing delays to minimize waiting delays at the central server.Second,we propose a Genetic-Algorithm-based Sorting(GAS)method to optimize the order of clients participating in training.Finally,based on the table lookup and forwarding rules of the Segment Routing over IPv6(SRv6)protocol,the sorting results of GAS are written into the SRv6 packet header,to control the order in which clients participate in model training.We conduct extensive experiments on two datasets of CIFAR-10 and MNIST,and the results demonstrate that the proposed algorithm offers improved accuracy,diminished communication costs,and reduced network delays.展开更多
In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging ...In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm with an attention mechanism,the proposed approach optimizes computation offloading and resource allocation,aiming to minimize energy consumption and service delay.In this paper,vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links.The adaptive attention mechanism enables the system to prioritize relevant state information,leading to faster convergence.Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG(AT-MADDPG)algorithm significantly improves performance,achieving notable reductions in both energy consumption and latency compared to baseline algorithms,especially in high-demand,dynamic scenarios.展开更多
With the support of Vehicle-to-Everything(V2X)technology and computing power networks,the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such ...With the support of Vehicle-to-Everything(V2X)technology and computing power networks,the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such as de-signalization.How to effectively manage autonomous vehicles for traffic control with high throughput at unsignalized intersections while ensuring safety has been a research hotspot.This paper proposes a collision-free autonomous vehicle scheduling framework based on edge-cloud computing power networks for unsignalized intersections where the lanes entering the intersections are undirectional,and designs an efficient communication system and protocol.First,by analyzing the collision point occupation time,this paper formulates an absolute value programming problem.Second,this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time(EI-OET)algorithm based on edge-cloud computing power support.Then,the communication system and protocol are designed for the proposed scheduling scheme to realize efficient and low-latency vehicular communications.Finally,simulation experiments compare the proposed scheduling framework with directional and traditional traffic light scheduling mechanisms,and the experimental results demonstrate its high efficiency,low latency,and low complexity.展开更多
With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communic...With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communication is analyzed.In this study,5G technology features,application principles,and practical strategies are discussed,and methods such as network slicing,customized deployment,edge computing collaborative application,communication equipment integration and upgrading,and multi-technology collaboration and complementation are proposed.It aims to effectively improve the efficiency,reliability,and security of power communication,solve the problem that traditional communication technology is difficult to meet the diversified needs of power business,and achieve the effect of optimizing the power communication network and supporting the intelligent development of the power system.展开更多
边缘算力网络(Edge Computing Power Network,EdgeCPN)作为一种新的计算范式,能够根据不同的任务需求灵活调度CPN中的碎片化计算资源,以实现面向大规模终端场景的高效计算任务卸载。文中设计了基于边边协同的移动设备计算任务卸载模型,...边缘算力网络(Edge Computing Power Network,EdgeCPN)作为一种新的计算范式,能够根据不同的任务需求灵活调度CPN中的碎片化计算资源,以实现面向大规模终端场景的高效计算任务卸载。文中设计了基于边边协同的移动设备计算任务卸载模型,将EdgeCPN中的任务卸载分为边缘计算资源池的构建和端边资源分配两个阶段,进而提出了基于最优成本计算资源池的差分进化搜索方案,实现任务卸载总延迟的最小化。首先根据用户预算选择出最优成本的计算资源池子集,然后基于资源池的可用计算资源,以最小化总延迟为目标,使用差分进化算法共同优化移动设备任务卸载决策,为每个终端的计算任务找到相应的服务器。仿真结果表明该方案显著提高了EdgeCPN中计算资源调度性能的效率和稳定性。展开更多
基金supported in part by the Chongqing Postgraduate Research and Innovation Project(CYB22250)National Natural Science Foundation of China(62271096,U20A20157)+2 种基金Natural Science Foundation of Chongqing-China(CSTB2023NSCQ-LZX0134,CSTB2024NSCQ-LZX0124)University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)。
文摘Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting.
基金This work was supported by the National Key R&D Program of China No.2019YFB1802800.
文摘In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.
基金supported by the National Science Foundation of China under Grant 62271062 and 62071063by the Zhijiang Laboratory Open Project Fund 2020LCOAB01。
文摘With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.
文摘Driven by diverse intelligent applications,computing capability is moving from the central cloud to the edge of the network in the form of small cloud nodes,forming a distributed computing power network.Tasked with both packet transmission and data processing,it requires joint optimization of communications and computing.Considering the diverse requirements of applications,we develop a dynamic control policy of routing to determine both paths and computing nodes in a distributed computing power network.Different from traditional routing protocols,additional metrics related to computing are taken into consideration in the proposed policy.Based on the multi-attribute decision theory and the fuzzy logic theory,we propose two routing selection algorithms,the Fuzzy Logic-Based Routing(FLBR)algorithm and the low-complexity Pairwise Multi-Attribute Decision-Making(l PMADM)algorithm.Simulation results show that the proposed policy could achieve better performance in average processing delay,user satisfaction,and load balancing compared with existing works.
基金supported by the National Natural Science Foundation of China under Grant 62272391in part by the Key Industry Innovation Chain of Shaanxi under Grant 2021ZDLGY05-08.
文摘As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and untrusted device terminals.Blockchain,as a shared,immutable distributed ledger,provides a secure resource management solution for WCPN.However,integrating blockchain into WCPN faces challenges like device heterogeneity,monitoring communication states,and dynamic network nature.Whereas Digital Twins(DT)can accurately maintain digital models of physical entities through real-time data updates and self-learning,enabling continuous optimization of WCPN,improving synchronization performance,ensuring real-time accuracy,and supporting smooth operation of WCPN services.In this paper,we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models.We adopt an enumeration-based optimal placement algorithm(EOPA)and an improved simulated annealing-based near-optimal placement algorithm(ISAPA)to achieve minimum average DT synchronization latency under the constraint of DT error.Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency.
基金partly funded by MOST Major Research and Development Project(Grant No 2021YFB2900204)Natural Science Foundation of China(Grant No 62132004)+1 种基金Sichuan Major R&D Project(Grant No 22QYCX0168)the Key Research and Development Program of Zhejiang Province(Grant No 2022C01093)。
文摘Computing Power Network(CPN)is emerging as one of the important research interests in beyond 5G(B5G)or 6G.This paper constructs a CPN based on Federated Learning(FL),where all Multi-access Edge Computing(MEC)servers are linked to a computing power center via wireless links.Through this FL procedure,each MEC server in CPN can independently train the learning models using localized data,thus preserving data privacy.However,it is challenging to motivate MEC servers to participate in the FL process in an efficient way and difficult to ensure energy efficiency for MEC servers.To address these issues,we first introduce an incentive mechanism using the Stackelberg game framework to motivate MEC servers.Afterwards,we formulate a comprehensive algorithm to jointly optimize the communication resource(wireless bandwidth and transmission power)allocations and the computation resource(computation capacity of MEC servers)allocations while ensuring the local accuracy of the training of each MEC server.The numerical data validates that the proposed incentive mechanism and joint optimization algorithm do improve the energy efficiency and performance of the considered CPN.
基金supported by the National Key R&D Program of China(No.2021YFB2900200)。
文摘Federated Learning(FL)is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security.However,the traditional FL model in communication scenarios,whether for uplink or downlink communications,may give rise to several network problems,such as bandwidth occupation,additional network latency,and bandwidth fragmentation.In this paper,we propose an adaptive chained training approach(Fed ACT)for FL in computing power networks.First,a Computation-driven Clustering Strategy(CCS)is designed.The server clusters clients by task processing delays to minimize waiting delays at the central server.Second,we propose a Genetic-Algorithm-based Sorting(GAS)method to optimize the order of clients participating in training.Finally,based on the table lookup and forwarding rules of the Segment Routing over IPv6(SRv6)protocol,the sorting results of GAS are written into the SRv6 packet header,to control the order in which clients participate in model training.We conduct extensive experiments on two datasets of CIFAR-10 and MNIST,and the results demonstrate that the proposed algorithm offers improved accuracy,diminished communication costs,and reduced network delays.
基金supported by the National Key Research and Development Program of China under grant 2021YFA0716600。
文摘In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm with an attention mechanism,the proposed approach optimizes computation offloading and resource allocation,aiming to minimize energy consumption and service delay.In this paper,vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links.The adaptive attention mechanism enables the system to prioritize relevant state information,leading to faster convergence.Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG(AT-MADDPG)algorithm significantly improves performance,achieving notable reductions in both energy consumption and latency compared to baseline algorithms,especially in high-demand,dynamic scenarios.
基金supported by the Natural Science Fund for Distinguished Young Scholars of Jiangsu Province under Grant BK20220067。
文摘With the support of Vehicle-to-Everything(V2X)technology and computing power networks,the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such as de-signalization.How to effectively manage autonomous vehicles for traffic control with high throughput at unsignalized intersections while ensuring safety has been a research hotspot.This paper proposes a collision-free autonomous vehicle scheduling framework based on edge-cloud computing power networks for unsignalized intersections where the lanes entering the intersections are undirectional,and designs an efficient communication system and protocol.First,by analyzing the collision point occupation time,this paper formulates an absolute value programming problem.Second,this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time(EI-OET)algorithm based on edge-cloud computing power support.Then,the communication system and protocol are designed for the proposed scheduling scheme to realize efficient and low-latency vehicular communications.Finally,simulation experiments compare the proposed scheduling framework with directional and traditional traffic light scheduling mechanisms,and the experimental results demonstrate its high efficiency,low latency,and low complexity.
文摘With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communication is analyzed.In this study,5G technology features,application principles,and practical strategies are discussed,and methods such as network slicing,customized deployment,edge computing collaborative application,communication equipment integration and upgrading,and multi-technology collaboration and complementation are proposed.It aims to effectively improve the efficiency,reliability,and security of power communication,solve the problem that traditional communication technology is difficult to meet the diversified needs of power business,and achieve the effect of optimizing the power communication network and supporting the intelligent development of the power system.
文摘边缘算力网络(Edge Computing Power Network,EdgeCPN)作为一种新的计算范式,能够根据不同的任务需求灵活调度CPN中的碎片化计算资源,以实现面向大规模终端场景的高效计算任务卸载。文中设计了基于边边协同的移动设备计算任务卸载模型,将EdgeCPN中的任务卸载分为边缘计算资源池的构建和端边资源分配两个阶段,进而提出了基于最优成本计算资源池的差分进化搜索方案,实现任务卸载总延迟的最小化。首先根据用户预算选择出最优成本的计算资源池子集,然后基于资源池的可用计算资源,以最小化总延迟为目标,使用差分进化算法共同优化移动设备任务卸载决策,为每个终端的计算任务找到相应的服务器。仿真结果表明该方案显著提高了EdgeCPN中计算资源调度性能的效率和稳定性。
