Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the id...Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the idea of resource virtualization and the analysis of the resource status transition, the resource allocation process and the necessity of resource reconstruction are presented, l^esource reconstruction algorithms are designed to determine the resource reconstruction types, and it is shown that they can achieve the goal of resource on-demand allocation through three methodologies： resource combination, resource split, and resource random adjustment. The effects that the resource users have on the resource reconstruction results, the deviation between resources and requirements, and the uniformity of resource distribution are studied by three experiments. The experiments show that resource reconstruction has a close relationship with resource requirements, but it is not the same with current distribution of resources. The algorithms can complete the resource adjustment with a lower cost and form the logic resources to match the demands of resource users easily.展开更多
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
基金supported by National High Technology Research and Development Program of China (863 Program)(No. 2007AA010305)the Excellent Doctor Degree Dissertation Fund of Xi an University of Technology (No. 102-211007)
文摘Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the idea of resource virtualization and the analysis of the resource status transition, the resource allocation process and the necessity of resource reconstruction are presented, l^esource reconstruction algorithms are designed to determine the resource reconstruction types, and it is shown that they can achieve the goal of resource on-demand allocation through three methodologies： resource combination, resource split, and resource random adjustment. The effects that the resource users have on the resource reconstruction results, the deviation between resources and requirements, and the uniformity of resource distribution are studied by three experiments. The experiments show that resource reconstruction has a close relationship with resource requirements, but it is not the same with current distribution of resources. The algorithms can complete the resource adjustment with a lower cost and form the logic resources to match the demands of resource users easily.
基金supported by the Natural Science Foundation of Jiangsu Province,China(No.BK20211227)the National Natural Science Foundation of China(No.62273356)。
文摘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.
