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Joint Cooperative Task Offloading and Computing Resource Allocation for Low Earth Orbit Satellites 认领 引用
1
作者 Zhang Yuexia Zhang Siyu Zheng Hui 《China Communications》 SCIE EI CSCD 2025年第10期88-100,共13页
Multispectral low earth orbit(LEO)satel-lites are characterized by a large volume of captured data and high spatial resolution,which can provide rich image information and data support for a vari-ety of fields,but it ... Multispectral low earth orbit(LEO)satel-lites are characterized by a large volume of captured data and high spatial resolution,which can provide rich image information and data support for a vari-ety of fields,but it is difficult for them to satisfy low-delay and low-energy consumed task processing re-quirements due to their limited computing resources.To address the above problems,this paper presents the LEO satellites cooperative task offloading and computing resource allocation(LEOC-TC)algorithm.Firstly,a LEO satellites cooperative task offloading system was designed so that the multispectral LEO satellites in the system could leave their tasks locally or offload them to other LEO satellites with servers for processing,thus providing high-quality information-processing services for multispectral LEO satellites.Secondly,an optimization problem with the objective of minimizing the weighted sum of the total task pro-cessing delay and total energy consumed for multi-spectral LEO satellite is established,and the optimiza-tion problem is split into an offloading ratio subprob-lem and a computing resource subproblem.Finally,Bernoulli mapping tuna swarm optimization algorithm is used to solve the above two sub-problems separately in order to satisfy the demand of low delay and low energy consumed by the system.Simulation results show that the total task processing cost of the LEOCTC algorithm can be reduced by 63.32%,66.67%,and 80.72%compared to the random offloading ratio algorithm,the average resource offloading algorithm,and the local computing algorithm,respectively. 展开更多
关键词 computing resource allocation interstellar collaboration low earth orbit satellites task offloading
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Switching Delay Aware Computing Resource Allocation in Virtualized Base Station 认领 引用
2
作者 Mingjin Gao He Chen +2 位作者 Yonghui Li Yiqing Zhou Jinglin Shi 《China Communications》 SCIE CSCD 2016年第11期226-233,共8页
In centralized cellular network architecture,the concept of virtualized Base Station(VBS) becomes attracting since it enables all base stations(BSs) to share computing resources in a dynamic manner. This can significa... In centralized cellular network architecture,the concept of virtualized Base Station(VBS) becomes attracting since it enables all base stations(BSs) to share computing resources in a dynamic manner. This can significantly improve the utilization efficiency of computing resources. In this paper,we study the computing resource allocation strategy for one VBS by considering the non-negligible effect of delay introduced by switches. Specifically,we formulate the VBS's sum computing rate maximization as a set optimization problem. To address this problem,we firstly propose a computing resource schedule algorithm,namely,weight before one-step-greedy(WBOSG),which has linear computation complexity and considerable performance. Then,OSG retreat(OSG-R) algorithm is developed to further improve the system performance at the expense of computational complexity. Simulation results under practical setting are provided to validate the proposed two algorithms. 展开更多
关键词 virtualized base station parallel computing computing resource allocation C-RAN
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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks 认领 引用
3
作者 Tong Minglei Li Song +1 位作者 Han Wanjiang Wang Xiaoxiang 《China Communications》 SCIE CSCD 2024年第3期230-246,共17页
Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal ... Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes. 展开更多
关键词 computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision
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Joint Allocation of Wireless Resource and Computing Capability in MEC-Enabled Vehicular Network 认领 引用 被引量：13
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作者 Yanzhao Hou Chengrui Wang +3 位作者 Min Zhu Xiaodong Xu Xiaofeng Tao Xunchao Wu 《China Communications》 SCIE EI CSCD 2021年第6期64-76,共13页
In MEC-enabled vehicular network with limited wireless resource and computation resource,stringent delay and high reliability requirements are challenging issues.In order to reduce the total delay in the network as we... In MEC-enabled vehicular network with limited wireless resource and computation resource,stringent delay and high reliability requirements are challenging issues.In order to reduce the total delay in the network as well as ensure the reliability of Vehicular UE(VUE),a Joint Allocation of Wireless resource and MEC Computing resource(JAWC)algorithm is proposed.The JAWC algorithm includes two steps:V2X links clustering and MEC computation resource scheduling.In the V2X links clustering,a Spectral Radius based Interference Cancellation scheme(SR-IC)is proposed to obtain the optimal resource allocation matrix.By converting the calculation of SINR into the calculation of matrix maximum row sum,the accumulated interference of VUE can be constrained and the the SINR calculation complexity can be effectively reduced.In the MEC computation resource scheduling,by transforming the original optimization problem into a convex problem,the optimal task offloading proportion of VUE and MEC computation resource allocation can be obtained.The simulation further demonstrates that the JAWC algorithm can significantly reduce the total delay as well as ensure the communication reliability of VUE in the MEC-enabled vehicular network. 展开更多
关键词 vehicular network delay optimization wireless resource allocation matrix spectral radius MEC computation resource allocation
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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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Adaptive computational resource allocation for sensor networks 认领 引用
6
作者 王典洪 费娥 阎毓杰 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2008年第1期129-134,共6页
To efficiently utilize the limited computational resource in real-time sensor networks, this paper focuses on the challenge of computational resource allocation in sensor networks and provides a solution with the meth... To efficiently utilize the limited computational resource in real-time sensor networks, this paper focuses on the challenge of computational resource allocation in sensor networks and provides a solution with the method of economics. It designs a microeconomic system in which the applications distribute their computational resource consumption across sensor networks by virtue of mobile agent. Further, it proposes the market-based computational resource allocation policy named MCRA which satisfies the uniform consumption of computational energy in network and the optimal division of the single computational capacity for multiple tasks. The simulation in the scenario of target tracing demonstrates that MCRA realizes an efficient allocation of computational resources according to the priority of tasks, achieves the superior allocation performance and equilibrium performance compared to traditional allocation policies, and ultimately prolongs the system lifetime. 展开更多
关键词 sensor networks computational resource allocation market mechanism mobile agent Nash equilibrium
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Zoning Search With Adaptive Resource Allocating Method for Balanced and Imbalanced Multimodal Multi-Objective Optimization 认领 引用 被引量：7
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作者 Qinqin Fan Okan K.Ersoy 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第6期1163-1176,共14页
Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs... Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs to each search subspace may be wasteful when computational resources are limited,especially on imbalanced problems.To alleviate the above-mentioned issue,a zoning search with adaptive resource allocating(ZS-ARA)method is proposed in the current study.In the proposed ZS-ARA,the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem complexity.Moreover,the computational resources can be automatically allocated among all the subspaces.The ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems(MMOPs),namely,balanced and imbalanced MMOPs.The results indicate that,similarly to the ZS,the ZS-ARA achieves high performance with the balanced MMOPs.Also,it can greatly assist a“regular”algorithm in improving its performance on the imbalanced MMOPs,and is capable of allocating the limited computational resources dynamically. 展开更多
关键词 Computational resource allocation decision space decomposition evolutionary computation multimodal multi-objective optimization
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Prediction based dynamic resource allocation method for edge computing first networking 认领 引用
8
作者 Zhang Luying Liu Xiaokai +2 位作者 Li Zhao Xu Fangmin Zhao Chenglin 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2023年第3期78-87,共10页
Aiming at the factory with high-complex and multi-terminal in the industrial Internet of things(IIoT),a hierarchical edge networking collaboration(HENC)framework based on the cloud-edge collaboration and computing fir... Aiming at the factory with high-complex and multi-terminal in the industrial Internet of things(IIoT),a hierarchical edge networking collaboration(HENC)framework based on the cloud-edge collaboration and computing first networking(CFN)is proposed to improve the capability of task processing with fixed computing resources on the edge effectively.To optimize the delay and energy consumption in HENC,a multi-objective optimization(MOO)problem is formulated.Furthermore,to improve the efficiency and reliability of the system,a resource prediction model based on ridge regression(RR)is proposed to forecast the task size of the next time slot,and an emergency-aware(EA)computing resource allocation algorithm is proposed to reallocate tasks in edge CFN.Based on the simulation result,the EA algorithm is superior to the greedy resource allocation in time delay,energy consumption,quality of service(QoS)especially with limited computing resources. 展开更多
关键词 cloud-edge collaboration computing first networking(CFN) computing resource allocation multi-objective optimization(MOO)
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Computational resource configuration analysis and optimization methods for unmanned system considering intended functionality safety 认领 引用
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作者 Zhiwei CHEN Luogeng ZHANG +2 位作者 Jiayun CHU Xiaotong FANG Hongyan DUI 《Frontiers of Engineering Management》 CSCD 2025年第4期1196-1219,共24页
With the rapid expansion of unmanned system capabilities,integrating and sharing computing resources has become essential.In addition to enhancing resource utilization efficiency,this architecture may also introduce c... With the rapid expansion of unmanned system capabilities,integrating and sharing computing resources has become essential.In addition to enhancing resource utilization efficiency,this architecture may also introduce conflicts related to resource competition.Therefore,effective resource-sharing configurations are crucial to ensure the Safety of the Intended Functionality(SOTIF).This paper proposes a computing resource configuration analysis and optimization methods for SOTIF.First,four SOTIF requirements are explored using the computing resource-sharing architecture for unmanned systems,encompassing computing time,computing power,energy consumption restrictions,and mutual exclusion and correlation.Secondly,the computing resource configuration model and its SOTIF constraints are formalized based on the graph and set theories.Subsequently,this study divides the design process of computing resource configuration schemes into resource selection and allocation.It introduces a resource selection optimization method based on Forward Checking and a resource allocation optimization method based on NSGA-II.Finally,a typical unmanned driving scenario is considered as an example,and the optimal resource selection and allocation schemes are sequentially determined using the proposed method on the computing platform. 展开更多
关键词 safety analysis unmanned system safety of the intended functionality computational resource allocation optimization.
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