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An Efficient Hierarchical Resource Scheduling in Terminal-Side Computing Power Networks 认领 引用
1
作者 Wang Hengjiang Cui Fang +2 位作者 Ni Mao Li Chao Tao Xiaoming 《China Communications》 SCIE EI CSCD 2026年第4期227-237,共11页
To address the critical challenges of nonuniform resource sensing and high dynamism within terminal-side computing power networks,this paper proposes a novel and efficient hierarchical resource scheduling mechanism.Fi... To address the critical challenges of nonuniform resource sensing and high dynamism within terminal-side computing power networks,this paper proposes a novel and efficient hierarchical resource scheduling mechanism.Firstly,architect a collaborative network architecture integrating a terminal layer and a cloud layer.Subsequently,a multi-dimensional model for computing power sensing and standardized measurement is established.Furthermore,investigate a hierarchical scheduling mechanism based on federated learning,which facilitates the effective management and intelligent scheduling of heterogeneous,dynamic resources.Experimental results demonstrate that this mechanism significantly reduces service latency in near-field computing,terminal-cloud collaboration,and ubiquitous computing scenarios. 展开更多
关键词 computing power networks resource scheduling computing power sensing
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Computing power networks for unmanned aerial vehicles:a hierarchical resources trading market 认领 引用
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作者 Xiaofei Wang Hui Deng +3 位作者 Chao Qiu Zheyuan Chen Tao Luo Zhao Ming 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期584-593,共10页
Unmanned Aerial Vehicles(UAVs)are increasingly deployed across military and civilian domains due to their operational flexibility,low maintenance costs,and high mobility.With the growing complexity of UAV applications... Unmanned Aerial Vehicles(UAVs)are increasingly deployed across military and civilian domains due to their operational flexibility,low maintenance costs,and high mobility.With the growing complexity of UAV applications and tasks,robust support from computing power networks is essential.These networks,acting as resource integration paradigms,furnish UAVs with pooled resources to tackle extensive computational demands.In this paper,we develop a framework for trading computing power resources,modeling the transaction process through a three-stage Stackelberg game to facilitate sequential decision-making.We theoretically demonstrate the existence of a Nash equilibrium and introduce a Dynamic Game Reinforcement algorithm to identify optimal strategies.Our experimental results affirm the framework's efficacy and the superior performance of our algorithm.Additionally,we explore how variables like UAV quantity and network congestion influence the market dynamics of the computing power network. 展开更多
关键词 UAVs Cloud computing Computing power network Resource trading Stackelberg game
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Heterogeneous resource allocation with latency guarantee for computing power network 认领 引用
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作者 Ailing Zhong Dapeng Wu +1 位作者 Boran Yang Ruyan Wang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期25-37,共13页
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
关键词 Latency violation probability Subtask dependencies Resource allocation Computing power network
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Heterogeneous Computing Power Scheduling Method Based on Distributed Deep Reinforcement Learning in Cloud-Edge-End Environments 认领 引用
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作者 Jinwei Mao Wang Luo +5 位作者 Jiangtao Xu Daohua Zhu WeiLiang Zhechen Huang Bao Feng Shuang Yang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1964-1985,共22页
With the rapid development of power Internet of Things(IoT)scenarios such as smart factories and smart homes,numerous intelligent terminal devices and real-time interactive applications impose higher demands on comput... With the rapid development of power Internet of Things(IoT)scenarios such as smart factories and smart homes,numerous intelligent terminal devices and real-time interactive applications impose higher demands on computing latency and resource supply efficiency.Multi-access edge computing technology deploys cloud computing capabilities at the network edge;constructs distributed computing nodes and multi-access systems and offers infrastructure support for services with low latency and high reliability.Existing research relies on a strong assumption that the environmental state is fully observable and fails to thoroughly consider the continuous time-varying features of edge server load fluctuations,leading to insufficient adaptability of the model in a heterogeneous dynamic environment.Thus,this paper establishes a framework for end-edge collaborative task offloading based on a partially observable Markov decision-making process(POMDP)and proposes a method for end-edge collaborative task offloading in heterogeneous scenarios.It achieves time-series modeling of the historical load characteristics of edge servers and endows the agent with the ability to be aware of the load in dynamic environmental states.Moreover,by dynamically assessing the exploration value of historical trajectories in the central trajectory pool and adjusting the sample weight distribution,directional exploration and strategy optimization of high-value trajectories are realized.Experimental results indicate that the proposed method exhibits distinct advantages compared with existing methods in terms of average delay and task failure rate and also verifies the method’s robustness in a dynamic environment. 展开更多
关键词 Edge computing end-edge collaboration heterogeneous computing power scheduling resource allocation
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Energy-Efficient Delay-Constrained Task Scheduling in Heterogeneous Computing Power Networks 认领 引用
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作者 Zhang Wenkai Zhang Shicong +3 位作者 Gu Chenhui Yang Xinglin Hu Bing Wang Wei 《China Communications》 SCIE EI CSCD 2026年第5期317-330,共14页
With the dramatic increase in the demand for computing power across various services,the emergence of the computing power network(CPN)becomes inevitable.This paper studies the task scheduling to minimize energy consum... With the dramatic increase in the demand for computing power across various services,the emergence of the computing power network(CPN)becomes inevitable.This paper studies the task scheduling to minimize energy consumption under delay constraints considering the heterogeneity of computing resources.Specifically,we decompose the original problem and alternatively optimize the scheduling strategy and server parameters until convergence.Dynamic Voltage and frequency scaling(DVFS)technology is leveraged to allocate the optimal voltage and frequency for each server based on their task loads.An enhanced projection gradient descent method is utilized to update the scheduling strategy under the given server parameters.Simulation results show that our algorithm achieves significant performance gains compared to the baselines across various CPN scenarios. 展开更多
关键词 computing power network energy efficiency heterogeneous computing resource task scheduling
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Computing Power Network:A Survey 认领 引用 被引量：33
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作者 Sun Yukun Lei Bo +4 位作者 Liu Junlin Huang Haonan Zhang Xing Peng Jing Wang Wenbo 《China Communications》 SCIE CSCD 2024年第9期109-145,共37页
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
关键词 computing power modeling computing power network computing power scheduling information awareness network forwarding
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Computing Power Network:The Architecture of Convergence of Computing and Networking towards 6G Requirement 认领 引用 被引量：77
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作者 Xiongyan Tang Chang Cao +4 位作者 Youxiang Wang Shuai Zhang Ying Liu Mingxuan Li Tao He 《China Communications》 SCIE EI CSCD 2021年第2期175-185,共11页
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
关键词 6G edge computing cloud computing convergence of cloud and network computing power network
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FedACT:An adaptive chained training approach for federated learning in computing power networks 认领 引用 被引量：1
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作者 Min Wei Qianying Zhao +4 位作者 Bo Lei Yizhuo Cai Yushun Zhang Xing Zhang Wenbo Wang 《Digital Communications and Networks》 SCIE CSCD 2024年第6期1576-1589,共14页
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
关键词 Computing power network(CPN) Federated learning(FL) Segment routing IPv6(SRv6) Communication overheads Model accuracy
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A novel routing method for dynamic control in distributed computing power networks 认领 引用 被引量：2
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作者 Lujie Guo Fengxian Guo Mugen Peng 《Digital Communications and Networks》 SCIE CSCD 2024年第6期1644-1652,共9页
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
关键词 Computing power networks Routing Fuzzy logic Multi-attribute decision making
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A game incentive mechanism for energy efficient federated learning in computing power networks 认领 引用
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作者 Xiao Lin Ruolin Wu +1 位作者 Haibo Mei Kun Yang 《Digital Communications and Networks》 SCIE CSCD 2024年第6期1741-1747,共7页
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
关键词 Computing power network Federated learning Energy efficiency Stackelberg game Resource allocation
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Efficient Digital Twin Placement for Blockchain-Empowered Wireless Computing Power Network 认领 引用
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作者 Wei Wu Liang Yu +2 位作者 Liping Yang Yadong Zhang Peng Wang 《Computers, Materials & Continua》 SCIE EI 2024年第7期587-603,共17页
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
关键词 Wireless computing power network blockchain digital twin placement minimum synchronization latency
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A UAV-enabled mobile edge computing paradigm for dependent tasks based on a computing power pool 认领 引用 被引量：1
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作者 Xuebin LAI Yan GUO +3 位作者 Ming HE Hao YUAN Wei LI Xiaonan CUI 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2025年第4期623-638,共16页
With the evolution of 5th generation(5G)and 6th generation(6G)wireless communication technologies,various Internet of Things(IoT)devices and artificial intelligence applications are proliferating,putting enormous pres... With the evolution of 5th generation(5G)and 6th generation(6G)wireless communication technologies,various Internet of Things(IoT)devices and artificial intelligence applications are proliferating,putting enormous pressure on existing computing power networks.Unmanned aerial vehicle(UAV)-enabled mobile edge computing(U-MEC)shows potential to alleviate this pressure and has been recognized as a new paradigm for responding to data explosion.Nevertheless,the conflict between computing demands and resource-constrained UAVs poses a great challenge.Recently,researchers have proposed resource management solutions in U-MEC for computing tasks with dependency.However,the repeatability among the tasks was ignored.In this paper,considering repeatability and dependency,we propose a U-MEC paradigm based on a computing power pool for processing computationally intensive tasks,in which UAVs can share information and computing resources.To ensure the effectiveness of computing power pool construction,the problem of balancing the energy consumption of UAVs is formulated through joint optimization of an offloading strategy,task scheduling,and resource allocation.To address this NP-hard problem,we adopt a two-stage alternate optimization algorithm based on successive convex approximation(SCA)and an improved genetic algorithm(GA).The simulation results show that the proposed scheme reduces time consumption by 18.41%and energy consumption by 21.68%on average,which can improve the working efficiency of UAVs. 展开更多
关键词 Unmanned aerial vehicle(UAV) UAV-enabled mobile edge computing(U-MEC) Computing power pool Dependency Repeatability
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Introduction to the Special Issue on Scientific Computing and Its Application to Engineering Problems 认领 引用
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作者 Higinio Ramos M Chandru 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期22-24,共3页
Scientific computing has become a cornerstone of modern scientific discovery and engineering innovation.With the rapid advancement of computational power and numerical algorithms,problems that were once analytically i... Scientific computing has become a cornerstone of modern scientific discovery and engineering innovation.With the rapid advancement of computational power and numerical algorithms,problems that were once analytically intractable can now be studied through accurate simulations and large-scale numerical experiments.Scientific computing provides a bridge between mathematical theory,computational algorithms,and real-world engineering applications,enabling researchers to model complex phenomena such as fluid flow,structural deformation,wave propagation,stochastic processes,and nonlinear dynamical systems.The special issue entitled“Scientific Computing and Its Application to Engineering Problems”was conceived to bring together high-quality research contributions that demonstrate the role of computational mathematics in solving challenging engineering problems.The issue highlights both theoretical advances in numerical methods and their practical deployment in real-world engineering scenarios,with emphasis on robustness,computational efficiency,stability,and scalability. 展开更多
关键词 scientific computing accurate simulations fluid flows numerical algorithmsproblems computational power numerical algorithms engineering problems model complex phenomena
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Collaborative Scheduling Strategy for Computation and Power among Multiple Data Centers Based on New Energy State Recognition 认领 引用
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作者 Qian Yang Shenglei Du +3 位作者 Boyang Chen Yalu Sun Ding Li Zhiheng Zhang 《Energy Engineering》 EI 2026年第8期346-373,共28页
In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon ... In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon neutrality”,data centers have gradually introduced new energy power such as wind and photovoltaic power.However,the randomness and volatility of their output pose challenges to efficient absorption.Based on the spatiotemporal complementary characteristics of new energy output in multiple data centers and the spatiotemporal migration capability of computing tasks,this paper proposes a new energy-aware adaptive collaborative scheduling strategy for computation and power.The strategy first constructs a regionally differentiated load model to accurately depict the characteristic differences among the Jiangsu-Zhejiang-Shanghai mixed computing power hub,the Gansu highefficiency computing power base,and the coastal green computing power nodes.Then,a dual-mode scheduling algorithm based on Lyapunov optimization is designed,integrating a prediction-reaction mechanism to achieve dynamic balance between system stability and new energy absorption rate.Furthermore,a V-parameter adaptive adjustment mechanism and a hierarchical fault-tolerant guarantee system are proposed to cope with new energy fluctuations and improve system robustness.Simulation results show that the proposed strategy achieves an average new energy absorption rate of 62.3%and 52.8%in normal weather and severe weather scenarios,respectively.The carbon emission per unit computing power is reduced by 20.9%,and the computing power-electricity efficiency is improved by 9.1%,which is significantly better than the static scheduling strategy.This verifies its effectiveness and practicability in improving new energy utilization,ensuring service quality,and reducing carbon emissions. 展开更多
关键词 Collaborative scheduling for computation and power data centers Lyapunov optimization V-parameter adjustment mechanism wind-solar volatility
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Computing for power system operation and planning: Then, now, and the future 认领 引用
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作者 Yousu Chen Zhenyu Huang +1 位作者 Shuangshuang Jin Ang Li 《iEnergy》 2022年第3期315-324,共10页
With the global trend of pursuing clean energy and decarbonization,power systems have been evolving in a fast pace that we have never seen in the history of electrification.This evolution makes the power system more d... With the global trend of pursuing clean energy and decarbonization,power systems have been evolving in a fast pace that we have never seen in the history of electrification.This evolution makes the power system more dynamic and more distributed,with higher uncertainty.These new power system behaviors bring significant challenges in power system modeling and simulation as more data need to be analyzed for larger systems and more complex models to be solved in a shorter time period.The conventional computing approaches will not be sufficient for future power systems.This paper provides a historical review of computing for power system operation and planning,discusses technology advancements in high performance computing(HPC),and describes the drivers for employing HPC techniques.Some high performance computing application examples with different HPC techniques,including the latest quantum computing,are also presented to show how HPC techniques can help us be well prepared to meet the requirements of power system computing in a clean energy future. 展开更多
关键词 Power system computing high performance computing quantum computing contingency analysis state estimation dynamic simulation machine learning optimization exascale computing.
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Resource pre-allocation algorithms for low-energy task scheduling of cloud computing 认领 引用 被引量：8
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作者 Xiaolong Xu Lingling Cao Xinheng Wang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2016年第2期457-469,共13页
In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the r... In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems. 展开更多
关键词 green cloud computing power consumption prediction resource allocation probabilistic matching simulated annealing
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Game-theoretic private blockchain design in edge computing networks 认领 引用
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作者 Daoqi Han Yang Liu +1 位作者 Fangwei Zhang Yueming Lu 《Digital Communications and Networks》 SCIE CSCD 2024年第6期1622-1634,共13页
Considering the privacy challenges of secure storage and controlled flow,there is an urgent need to realize a decentralized ecosystem of private blockchain for cyberspace.A collaboration dilemma arises when the partic... Considering the privacy challenges of secure storage and controlled flow,there is an urgent need to realize a decentralized ecosystem of private blockchain for cyberspace.A collaboration dilemma arises when the participants are self-interested and lack feedback of complete information.Traditional blockchains have similar faults,such as trustlessness,single-factor consensus,and heavily distributed ledger,preventing them from adapting to the heterogeneous and resource-constrained Internet of Things.In this paper,we develop the game-theoretic design of a two-sided rating with complete information feedback to stimulate collaborations for private blockchain.The design consists of an evolution strategy of the decision-making network and a computing power network for continuously verifiable proofs.We formulate the optimum rating and resource scheduling problems as two-stage iterative games between participants and leaders.We theoretically prove that the Stackelberg equilibrium exists and the group evolution is stable.Then,we propose a multi-stage evolution consensus with feedback on a block-accounting workload for metadata survival.To continuously validate a block,the metadata of the optimum rating,privacy,and proofs are extracted to store on a lightweight blockchain.Moreover,to increase resource utilization,surplus computing power is scheduled flexibly to enhance security by degrees.Finally,the evaluation results show the validity and efficiency of our model,thereby solving the collaboration dilemma in the private blockchain. 展开更多
关键词 Ecosystem Edge computing Feedback Stackelberg game Private blockchain Scalable computing power
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Toward edge-computing-enabled collision-free scheduling management for autonomous vehicles at unsignalized intersections 认领 引用
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作者 Ziyi Lu Tianxiong Wu +4 位作者 Jinshan Su Yunting Xu Bo Qian Tianqi Zhang Haibo Zhou 《Digital Communications and Networks》 SCIE CSCD 2024年第6期1600-1610,共11页
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
关键词 Unsignalized intersection Automatic vehicle scheduling Edge computing Communication protocol Computing power network
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Isomorphic Control in Energy Storage Systems Based on Graph-Based Digital Twins 认领 引用
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作者 Zhijun Zhou Ye Chen +2 位作者 Qingning Lu Ning Guo Zhechen Huang 《Digital Twins and Applications》 EI 2026年第1期222-237,共16页
High-fidelity digital twin implementation for energy storage systems(ESS)is frequently hindered by the‘translation gap’in conventional hardware-in-the-loop platforms,the rigid coupling between control logic and firm... High-fidelity digital twin implementation for energy storage systems(ESS)is frequently hindered by the‘translation gap’in conventional hardware-in-the-loop platforms,the rigid coupling between control logic and firmware undermines the consistency between the virtual model and its physical deployment.To bridge this divide,this paper proposes a graph-based framework leveraging activity-on-edge(AOE)networks to abstract control logic into directed acyclic graphs(DAGs).This topological abstraction explicitly decouples strategy formulation from hardware constraints,ensuring structural isomorphism between the simulated model and the deployed logic.The framework's capability in complex control and optimisation is substantiated via a mixed-integer linear programming(MILP)-based automatic generation control(AGC)strategy.Experimental validation confirms that the platform achieves deterministic real-time performance with precise power tracking.Ultimately,this approach provides a scalable theoretically rigorous solution for eliminating the implementation gap in industrial ESS digitalisation and cyber-physical systems. 展开更多
关键词 digital twins electrical engineering electrical engineering computing electricity supply industry power engineering computing
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How can the new paradigm of LNG cold energy+wind and solar power generation+green fuel reshape the energy gene of data centers? 认领 引用
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作者 Dashu Li Xinghui Wang +4 位作者 Mengdi Liu Hongyin Chen Zhenlan Dou Shiman Zhang Duo Zhang 《The Innovation》 EI 2026年第1期13-14,共2页
INTRODUCTION Large-scale computing power has become pivotal in enhancing national core competitiveness as the new quality productive force in the digital economy era.With the rapid advancement of artificial general in... INTRODUCTION Large-scale computing power has become pivotal in enhancing national core competitiveness as the new quality productive force in the digital economy era.With the rapid advancement of artificial general intelligence,the computing power industry is experiencing exponential growth.Projections suggest that China’s total computing capacity will exceed 300 exaflops per second(EFLOPS)by 2025,with associated electricity consumption accounting for approximately 5%of national power demand. 展开更多
关键词 wind power solar power lng enhancing national core competitiveness data centers cold energy computing power green fuel
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