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Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management 认领 引用 被引量：3
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作者 Xiangnan Liu Haijun Zhang +3 位作者 Min Sheng Wei Li Saba Al-Rubaye Keping Long 《China Communications》 SCIE CSCD 2023年第10期262-275,共14页
With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integ... With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks. 展开更多
关键词 satellite-enabled 6G networks network architecture resource optimization interference management
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Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing 认领 引用 被引量：4
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作者 Xu Wenjing Wang Wei +2 位作者 Li Zuguang Wu Qihui Wang Xianbin 《China Communications》 SCIE CSCD 2024年第4期218-229,共12页
Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus t... Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case. 展开更多
关键词 blockchain collaborative edge computing resource optimization task allocation
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Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing 认领 引用
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作者 Xu Wenjing Wang Wei +2 位作者 Li Zuguang Wu Qihui Wang Xianbin 《China Communications》 SCIE CSCD 2024年第12期231-242,共12页
Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus t... Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case. 展开更多
关键词 blockchain collaborative edge comput-ing resource optimization task allocation
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Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications 认领 引用
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作者 Punit Gupta Sanjit Bhagat +3 位作者 Dinesh Kumar Saini Ashish Kumar Mohammad Alahmadi Prakash Chandra Sharma 《Computers, Materials & Continua》 SCIE EI 2022年第6期5659-5676,共18页
In the next generation of computing environment e-health care services depend on cloud services.The Cloud computing environment provides a real-time computing environment for e-health care applications.But these servi... In the next generation of computing environment e-health care services depend on cloud services.The Cloud computing environment provides a real-time computing environment for e-health care applications.But these services generate a huge number of computational tasks,real-time computing and comes with a deadline,so conventional cloud optimizationmodels cannot fulfil the task in the least time and within the deadline.To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time.In order to overcome existing issues,an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare applications.In this work,two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary classifier.The predictive model enhances the quality of service using performance metrics,makespan,least average task completion time,resource usages cost and utilization of the system.Fromresults as compared to existing algorithms the proposedANN-WHOalgorithms prove to improve the average start time by 29.3%,average finish time by 29.5%and utilization by 11%. 展开更多
关键词 Cloud computing whale optimization health care resource optimization
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Leveraging Geospatial Technologies for Resource Optimization in Livestock Management 认领 引用
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作者 Luwaga Denis Mavuto Denis Tembo +4 位作者 Mtafu Manda Alimasi Wilondja Ngagne Ndong Joshua Koskei Kimeli Nansamba Phionah 《Journal of Geoscience and Environment Protection》 2024年第10期287-307,共21页
Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can e... Geospatial technologies can be leveraged to optimize the available resources for better productivity and sustainability. The resources can be human, software and hardware equipment and their effective management can enhance operational efficiency through better and informed decision making. This review article examines the application of geospatial technologies, including GPS, GIS, and remote sensing, for optimizing resource utilization in livestock management. It compares these technologies to traditional livestock management practices and highlights their potential to improve animal tracking, feed intake monitoring, disease monitoring, pasture selection, and rangeland management. Previously, animal management practices were labor-intensive, time-consuming, and required more precision for optimal animal health and productivity. Digital technologies, including Artificial Intelligence (AI) and Machine Learning (ML) have transformed the livestock sector through precision livestock management. However, major challenges such as high cost, availability and accessibility to these technologies have deterred their implementation. To fully realize the benefits and tremendous contribution of these digital technologies and to address the challenges associated with their widespread adoption, the review proposes a collaborative approach between different stakeholders in the livestock sector including livestock farmers, researchers, veterinarians, industry professionals, technology developers, the private sector, financial institutions and government to share knowledge and expertise. The collaboration would facilitate the integration of various strategies to ensure the effective and wide adoption of digital technologies in livestock management by supporting the development of user-friendly and accessible tools tailored to specific livestock management and production systems. 展开更多
关键词 Geospatial Technologies Resource Optimization Smart Livestock Management Artificial Intelligence Machine Learning
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Review of Metaheuristic Optimization Techniques for Enhancing E-Health Applications 认领 引用
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作者 Qun Song Chao Gao +3 位作者 Han Wu Zhiheng Rao Huafeng Qin Simon Fong 《Computers, Materials & Continua》 SCIE EI 2026年第2期185-233,共49页
Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a syst... Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health.We selected representative algorithms published between 2019 and 2024,and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts.CThe Harris Hawks Optimizer(HHO)demonstrated the highest early citation impact.The study also examined applications in disease prediction models,clinical decision support,and intelligent health monitoring.Notably,the Chaotic Salp Swarm Algorithm(CSSA)achieved 99.69% accuracy in detecting Novel Coronavirus Pneumonia.Future research should progress in three directions:improving theoretical reliability and performance predictability in medical contexts;designing more adaptive and deployable mechanisms for real-world systems;and integrating ethical,privacy,and technological considerations to enable precision medicine,digital twins,and intelligent medical devices. 展开更多
关键词 Metaheuristic optimization E-Health disease diagnosis medical resource optimization complex optimization
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A Heterogeneous Graph Cooperative Representation Approach for Multidimensional Coupling Resources in High-Dynamic Satellite-Terrestrial Integrated Networks 认领 引用
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作者 Fan Tian Hu Bo +2 位作者 Zhou Jizhe Chen Shanzhi Wang Guangchao 《China Communications》 SCIE EI CSCD 2026年第3期68-86,共19页
Satellite-terrestrial integrated networks(STINs)are a key enabler for ubiquitous coverage in 6G communication services.However,the satelliteterrestrial resources exhibit multi-dimensional heterogeneity and inherent co... Satellite-terrestrial integrated networks(STINs)are a key enabler for ubiquitous coverage in 6G communication services.However,the satelliteterrestrial resources exhibit multi-dimensional heterogeneity and inherent conflicts,and the rapid topology variations caused by the high-speed motion of low earth orbit(LEO)satellites lead to the difficulty of maintaining a stable mapping of satellite-terrestrial resources.This dynamic nature ultimately reduces the overall resource utilization efficiency.In this paper,we propose a heterogeneous graph cooperative representation approach for satellite-terrestrial resources and a joint optimization method of transmissioncomputation resources.Firstly,we construct a heterogeneous graph that achieves mapping between multidimensional resources,dynamic topology,and conflict constraints through typed nodes and edges,where resource cooperativeness is explicitly encoded.Secondly,an STIN transmission-computation model is constructed,and an optimization problem is formulated to jointly resolve conflicts between four objectives.Finally,the proposed many-objective double deep Q-network(DDQN)algorithm achieves the cooperative strategy optimization of task transmissioncomputation scheduling globally.Simulation experiments show that the proposed algorithm improves the overall resource utilization by up to 11.7%under various access points(APs)and user sizes.Meanwhile,the performance is more stable compared with five algorithms,including deep Q-network(DQN),and a Lyapunov-based optimization method(LyaOpt). 展开更多
关键词 deep reinforcement learning many-objective optimization multi-dimensional resource optimization satellite-terrestrial integrated networks
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Key Mechanisms on Resource Optimization Allocation in Minority Game Based on Reinforcement Learning 认领 引用
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作者 Changyan Di Tianyi Wang +1 位作者 Qingguo Zhou Jinqiang Wang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2025年第2期721-731,共11页
The emergence of coordinated and consistent macro behavior among self-interested individuals competing for limited resources represents a central inquiry in comprehending market mechanisms and collective behavior.Trad... The emergence of coordinated and consistent macro behavior among self-interested individuals competing for limited resources represents a central inquiry in comprehending market mechanisms and collective behavior.Traditional economics tackles this challenge through a mathematical and theoretical lens,assuming individuals are entirely rational and markets tend to stabilize through the price mechanism.Our paper addresses this issue from an econophysics standpoint,employing reinforcement learning to construct a multi-agent system modeled on minority games.Our study has undertaken a comparative analysis from both collective and individual perspectives,affirming the pivotal roles of reward feedback and individual memory in addressing the aforementioned challenge.Reward feedback serves as the guiding force for the evolution of collective behavior,propelling it towards an overall increase in rewards.Individuals,drawing insights from their own rewards through accumulated learning,gain information about the collective state and adjust their behavior accordingly.Furthermore,we apply information theory to present a formalized equation for the evolution of collective behavior.Our research supplements existing conclusions regarding the mechanisms of a free market and,at a micro level,unveils the dynamic evolution of individual behavior in synchronization with the collective. 展开更多
关键词 minority game optimization of resource allocation multi-agent system reinforcement learning
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Cross-domain resources optimization for hybrid edge computing networks:Federated DRL approach 认领 引用 被引量：1
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作者 Xiaoqin Song Quan Chen +1 位作者 Shumo Wang Tiecheng Song 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期1797-1808,共12页
Due to the dynamic nature of service requests and the uneven distribution of services in the Internet of Vehicles(IoV),Multi-access Edge Computing(MEC)networks with pre-installed servers are often susceptible to insuf... Due to the dynamic nature of service requests and the uneven distribution of services in the Internet of Vehicles(IoV),Multi-access Edge Computing(MEC)networks with pre-installed servers are often susceptible to insufficient computing power at certain times or in certain areas.In addition,Vehicular Users(VUs)need to share their observations for centralized neural network training,resulting in additional communication overhead.In this paper,we present a hybrid MEC server architecture,where fixed Road Side Units(RSUs)and Mobile Edge Servers(MESs)cooperate to provide computation offloading services to VUs.We propose a distributed federated learning and Deep Reinforcement Learning(DRL)based algorithm,namely Federated Dueling Double Deep Q-Network(FD3QN),with the objective of minimizing the weighted sum of service latency and energy consumption.Horizontal federated learning is incorporated into the Dueling Double Deep Q-Network(D3QN)to allocate cross-domain resources after the offload decision process.A client-server framework with federated aggregation is used to maintain the global model.The proposed FD3 QN algorithm can jointly optimize power,sub-band,and computational resources.Simulation results show that the proposed algorithm outperforms baselines in terms of system cost and exhibits better robustness in uncertain IoV environments. 展开更多
关键词 Internet of vehicles Multi-access edge computing Cross-domain resources optimization Federated learning Dueling double deep Q-network
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Predefined-Time Distributed Optimization for Resource Allocation Problems With Time-Varying Objective Function and Constraints 认领 引用
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作者 Haotian Wu Yang Liu +1 位作者 Mahmoud Abdel-Aty Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第11期2353-2355,共3页
Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,... Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach. 展开更多
关键词 resource allocation distributed optimization time varying objective function optimal resource allocationthe distributed control protocol time varying constraints predefined time convergence
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Computational Offloading and Resource Allocation for Internet of Vehicles Based on UAV-Assisted Mobile Edge Computing System 认领 引用
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作者 Fang Yujie Li Meng +3 位作者 Si Pengbo Yang Ruizhe Sun Enchang Zhang Yanhua 《China Communications》 SCIE EI CSCD 2025年第9期333-351,共19页
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ... As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant. 展开更多
关键词 computational offloading Internet of Vehicles mobile edge computing resource optimization unmanned aerial vehicle
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Distributed Throughput and Energy Efficient Resource Optimization When D2D and Massive MIMO Coexist 认领 引用
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作者 Abi Abate Dejen Yihenew Wondie Anna Forster 《Journal of Communications and Information Networks》 EI CSCD 2022年第3期278-295,共18页
Fifth generation(5G)cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements,which are not achieved by single network technology.The future cellular networks require e... Fifth generation(5G)cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements,which are not achieved by single network technology.The future cellular networks require efficient resource allocation and power control schemes that meet throughput and energy efficiency requirements when multiple technologies coexist and share network resources.In this paper,we optimize the throughput and energy efficiency(EE)performance for the coexistence of two technologies that have been identified for the future cellular networks,namely,massive multiple-input multiple-output(MIMO)and network-assisted device-to-device(D2D)communications.In such a hybrid network,the co/cross-tier interferences between cellular and D2D communications caused by spectrum sharing is a significant challenge.To this end,we formulate the average sum rate and EE optimization problem as mixed-integer non-linear programming(MINLP).We develop distributed resource allocation algorithms based on matching theory to alleviate interferences and optimize network performance.It is shown in this paper that the proposed algorithms converge to a stable matching and terminate after finite iterations.Matlab simulation results show that the proposed algorithms achieved more than 88%of the average transmission rate and 86%of the energy efficiency performance of the optimal matching with lower complexity. 展开更多
关键词 device-to-device(D2D) massive MIMO communication interference management resource optimization
ANNDRA-IoT:A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments 认领 引用
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作者 Abdullah M.Alqahtani Kamran Ahmad Awan +1 位作者 Abdulaziz Almaleh Osama Aletri 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期3155-3179,共25页
Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-ba... Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing conditions.Designed to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real time.The training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent performance.The simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods. 展开更多
关键词 Internet of things resource optimization deep learning optimal resource allocation neural network efficiency
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The Cloud Manufacturing Resource Scheduling Optimization Method Based on Game Theory 认领 引用 被引量：2
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作者 Xiaoxuan Yang Zhou Fang 《Journal on Artificial Intelligence》 2022年第4期229-243,共15页
In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment,this paper proposes to use load balancing,service cost and service quality as optimization goals for res... In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment,this paper proposes to use load balancing,service cost and service quality as optimization goals for resource scheduling,however,resource providers have resource utilization requirements for cloud manufacturing platforms.In the process of resource optimization scheduling,the interests of all parties have conflicts of interest,which makes it impossible to obtain better optimization results for resource scheduling.Therefore,amultithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling.The cloud manufacturing platform first calculates the expected value reduction plan for each round of global optimization,using the negotiation algorithm based on the Stackelberg game,the cloud manufacturing platformnegotiates andmediateswith the participants’agents,to maximize self-interest by constantly changing one’s own plan,iteratively find multiple sets of locally optimized negotiation plans and return to the cloud manufacturing platform.Through multiple rounds of negotiation and calculation,we finally get a target expected value reduction plan that takes into account the benefits of the resource provider and the overall benefits of the completion of the manufacturing task.Finally,through experimental simulation and comparative analysis,the validity and rationality of the model are verified. 展开更多
关键词 Cloud manufacturing resource scheduling optimal allocation of resources conflict of interest stackelberg game
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Water resources optimization and eco-environmental protection in Qaidam Basin 认领 引用
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作者 FANG Chuang-lin~1, BAO Chao~2 (1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China 2. Dept. of Geography, Peking University, Beijing 100871, China) 《Journal of Geographical Sciences》 2001年第2期231-238,共8页
In order to realize sustainable development of the arid area of Northwest China, rational water resources exploitation and optimization are primary prerequisites. Based on the essential principle of sustainable develo... In order to realize sustainable development of the arid area of Northwest China, rational water resources exploitation and optimization are primary prerequisites. Based on the essential principle of sustainable development, this paper puts forward a general idea on water resources optimization and eco-environmental protection in Qaidam Basin, and identifies the competitive multiple targets of water resources optimization. By some qualitative methods such as Input-output Model & AHP Model and some quantitative methods such as System Dynamics Model & Produce Function Model, some standard plans of water resources optimization come into being. According to the Multiple Targets Decision by the Closest Value Model, the best plan of water resources optimization, eco-environmental protection and sustainable development in Qaidam Basin is finally decided. 展开更多
关键词 water resources optimization Multiple Targets Decision by the Closest Value Model eco-environmental protection Qaidam Basin
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Advancing electric vehicle ecosystems:a survey of generative artificial intelligence and distributed machine learning applications 认领 引用
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作者 Seyed Mahmoud Sajjadi Mohammadabadi Aidin Karimi Moghaddam +4 位作者 Mahmoudreza Entezami Mirali Seyedrezaei Dorsa Charkhian Behzad Moghaddami Mohammad Sassani 《Global Energy Interconnection》 EI CSCD 2026年第2期315-336,共22页
The growing popularity of Electric Vehicles(EVs)necessitates advanced systems capable of managing the increasing complexity of EV-generated data.However,the exponential expansion of data streams poses significant chal... The growing popularity of Electric Vehicles(EVs)necessitates advanced systems capable of managing the increasing complexity of EV-generated data.However,the exponential expansion of data streams poses significant challenges to existing network infrastructure,potentially limiting EV performance and scalability.This survey investigates the synergistic potential of Generative Artificial Intelligence(GenAI)and Distributed Machine Learning(DML)to address key challenges and enhance EV efficiency across diverse domains.DML facilitates collaborative learning across decentralized devices,enabling optimized resource allocation,strengthened privacy,and improved EV operations without data centralization.Meanwhile,GenAI techniques,such as Generative Adversarial Networks(GANs)and Variational Autoencoders(VAEs),offer transformative capabilities,including synthetic data generation for energy forecasting,data compression for efficient transmission,and resource-efficient task offloading.This paper explores the applications of GenAI and DML in several key areas of the EV ecosystem.These include battery lifecycle management,energy optimization,fault detection,and workload balancing.Furthermore,it highlights the primary advantages and challenges of implementing these technologies,such as addressing computational demands,algorithmic complexity,and mitigating biases in generated content.By advancing the integration of GenAI and DML,this study lays a foundation for a more sustainable,intelligent,and efficient transportation future. 展开更多
关键词 Generative artificial intelligence Electric vehicles Distributed machine learning Resource optimization Energy forecasting Fault detection ChatGPT Optimization
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Efficient Resource Allocation in Cloud IaaS: A Multi-Objective Strategy for Minimizing Workflow Makespan and Cloud Resource Costs 认领 引用
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作者 Jean Edgard Gnimassoun Dagou Dangui Augustin Sylvain Legrand Koffi Akanza Konan Ricky N’dri 《Open Journal of Applied Sciences》 2025年第1期147-167,共21页
The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tas... The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times. 展开更多
关键词 Cloud Infrastructure Multi-Objective Scheduling Resource Cost Optimization Resource Utilization Scientific Workflows
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A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning 认领 引用 被引量：8
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作者 Azka Amin Xihua Liu +3 位作者 Imran Khan Peerapong Uthansakul Masoud Forsat Seyed Sajad Mirjavadi 《Computers, Materials & Continua》 SCIE EI 2020年第11期1487-1505,共19页
One of the most effective technology for the 5G mobile communications is Device-to-device(D2D)communication which is also called terminal pass-through technology.It can directly communicate between devices under the c... One of the most effective technology for the 5G mobile communications is Device-to-device(D2D)communication which is also called terminal pass-through technology.It can directly communicate between devices under the control of a base station and does not require a base station to forward it.The advantages of applying D2D communication technology to cellular networks are:It can increase the communication system capacity,improve the system spectrum efficiency,increase the data transmission rate,and reduce the base station load.Aiming at the problem of co-channel interference between the D2D and cellular users,this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning,which creates multi-agent learners from multiple D2D users,and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users.The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted.The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users.Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms. 展开更多
关键词 5G D2D communications power allocation algorithm resource optimization
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Optimization and Integration of Water Resources and Guarantee of Water Supply Safety in Southern Cities and Towns of Huangshan City 认领 引用
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作者 ZHENGJianmin 《外文科技期刊数据库（文摘版）自然科学》 2022年第1期125-129,共5页
According to the "Special Water Supply Plan for Southern Cities and Towns in Huangshan City (2017-2030)", Fengle Reservoir and Yuetan Reservoir are the two major water supply sources for southern cities and ... According to the "Special Water Supply Plan for Southern Cities and Towns in Huangshan City (2017-2030)", Fengle Reservoir and Yuetan Reservoir are the two major water supply sources for southern cities and towns (Tunxi District, Huangshan Hi-tech Zone, Xiuning County, Huizhou District and Shexian County). This topic focuses on giving full play to the basic role of the two reservoirs in ensuring regional water supply safety and ecological safety. Therefore, our idea of optimal integration of water resources is also carried out within the regional scope of the entire southern cities and towns. It is elaborated and analyzed from the perspectives of water supply status, water resources status and allocation, water supply demand and planning, water resources integration, and other issues and suggestions are put forward 展开更多
关键词 town cluster in the south of Huangshan city optimization and integration of water resources water supply safety guarantee
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Computation offloading and resource allocation for UAV-assisted IoT based on blockchain and mobile edge computing 认领 引用 被引量：1
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作者 ZHAO Chengze LI Meng +3 位作者 SUN Enchang HUO Ru LI Yu ZHANG Yanhua 《High Technology Letters》 EI CAS 2022年第1期80-90,共11页
Recently,Internet of Things(IoT)have been applied widely and improved the quality of the daily life.However,the lightweight IoT devices can hardly implement complicated applications since they usually have limited com... Recently,Internet of Things(IoT)have been applied widely and improved the quality of the daily life.However,the lightweight IoT devices can hardly implement complicated applications since they usually have limited computing resource and just can execute some simple computation tasks.Moreover,data transmission and interaction in IoT is another crucial issue when the IoT devices are deployed at remote areas without manual operation.Mobile edge computing(MEC)and unmanned aerial vehicle(UAV)provide significant solutions to these problems.In addition,in order to ensure the security and privacy of data,blockchain has been attracted great attention from both academia and industry.Therefore,an UAV-assisted IoT system integrated with MEC and blockchain is pro-posed.The optimization problem in the proposed architecture is formulated to achieve the optimal trade-off between energy consumption and computation latency through jointly considering computa-tion offloading decision,spectrum resource allocation and computing resource allocation.Consider-ing this complicated optimization problem,the non-convex mixed integer problem can be transformed into a convex problem,and a distributed algorithm based on alternating direction multiplier method(ADMM)is proposed.Simulation results demonstrate the validity of this scheme. 展开更多
关键词 Internet of Things(IoT) unmanned aerial vehicle(UAV) mobile edge compu-ting(MEC) blockchain alternating direction multiplier method(ADMM) resource optimization
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