多用户多边缘服务器的低碳算网技术研究OA
Research on Low-Carbon Computational Power Network Technology for Multi-User and Multi-Edge Server System
[目的]算力网络背景下,本文对多用户多服务器的边缘计算系统进行了能耗方面的研究,通过在多个边缘服务器间进行算力任务卸载和动态资源调度,以进一步实现边缘计算系统的能耗优化.[方法]首先建立了包含多用户和多服务器的边缘计算体系与调度系统,分析了影响系统能耗的关键因素,并按照实际算力需求对终端传输功率和边缘服务器频率进行优化,在此基础上设计了多边缘服务器间算力任务动态调度策略,避免由于负载不均导致的能耗偏高问题.[结果]通过理论推导证明了方法的正确性,基于仿真模型和策略设计,验证了本文中方法能够在保障服务质量的同时,实现算力资源间的共享和能耗水平的降低.[局限]本文中方法主要从计算和数据传输两个角度进行分析,对相关模型进行了合理的简化分析,在当前基础上综合考虑网络、应用以及数据安全等维度,并进行落地实践,能够进一步提升方法的应用价值.[结论]基于算力网络对资源的统一纳管和调度能力,在边缘计算等场景中进行合理的资源管理、算力卸载和任务调度,能够有效提升面向业务的服务质量保障和面向底层资源的能耗优化.
[Objective]Under the background of the computational power network,this paper studies the energy consumption of multi-user and multi-server edge computing systems,and further realiz-es the energy consumption optimization of edge computing systems by offloading computing tasks and dynamic resource scheduling among multiple edge servers.[Methods]First,an edge computing system and scheduling system including multi-user and multi-server are established,the key factors affecting the energy consumption of the system are analyzed,and the terminal transmission power and the clock frequency of the edge server are optimized according to the actual computing power requirements.On this basis,a dynamic scheduling strategy for computing tasks among multiple edge servers is designed to avoid the problem of energy waste caused by the uneven load.[Results]The correctness of the method is proved by theoretical derivation.Based on the simulation model and strategy design,it is verified that the method in this paper can realize the sharing of computing resources and reduce the energy consumption level while ensuring service quality.[Limitations]In this paper,the method is mainly analyzed from the perspectives of calculation and data transmission,the relevant model is reasonably simplified and ana-lyzed,and the dimensions of network,application and data security are comprehensively considered on the cur-rent basis,and the implementation practice is carried out,which can further improve the application value of the method.[Conclusions]Based on the unified management and scheduling capabilities of resources in the compu-tational power network,reasonable resource management,computing task offloading,and task scheduling are car-ried out in edge computing and other scenarios,which can effectively improve service-oriented QoS assurance and energy consumption optimization for underlying resources.
沈林江;崔超;徐胜霞;仇树卿;许俊东;耿晓巧
浪潮通信信息系统有限公司,算力网络研究院,山东济南 250100山东信息职业技术学院,电子与通信系,山东潍坊 261061
算力网络边缘计算能耗优化
computational power networkedge computingenergy-efficiency optimization
《数据与计算发展前沿》 2024 (005)
91-101 / 11
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