面向新型电力系统的最小服务延迟的负载分配算法研究

Research on load distribution algorithm for minimum service delay in new power system

边缘计算依托其在网络边缘的技术和部署优势,能够实时采集终端设备的运行情况,并按需完成数据云台的上传,有效缓解了云端处理器的工作压力,降低数据实时回传的延时。然而,资源有限的单一边缘节点的固有特性可能依然无法满足大规模接入的新型电力业务的延迟要求。对此文中研究了满足较低服务延迟的工作负载分配策略。考虑到网络时延和计算时延,构建了一种基于多个边缘计算节点的工作负载分配模型,以最小化新型电力系统场景下的服务时延。此外,为进一步降低多种新型电力业务终端的服务时延,制定了一种工作负载分配机制。在初始化阶段,通过任务平衡算法来改善边缘节点间的工作负载平衡。为了有效保证资源分配的合理性,提出了一种改进的粒子群优化算法,实现边缘节点的CPU资源的合理分配。基于半定规划算法,将网络资源做到更有效地分配。最终通过仿真实验,表明了所提的分配策略更加有效。

Relying on its technology and deployment advantages at the edge of the network,edge computing can collect the running status of terminal equipment in real time,and complete the upload of data to the cloud platform on demand,effectively alleviating the working pressure of the cloud processor and reducing the delay of real-time data return.However,the inherent characteristics of a single edge node with limited resources may still be unable to meet the delay requirements of large-scale accessed new power businesses.In this regard,this paper studies the workload distribution strategy that meets the lower service delay.Considering the network delay and calculation delay,a workload distribution model based on multiple edge computing nodes is constructed to minimize the service delay in the new power system scenario.In addition,in order to further reduce the service delay of multi-service terminals,a workload distribution mechanism has been developed.In the initialization phase,the task balancing algorithm is used to improve the workload balance among edge nodes.In order to effectively ensure the rationality of resource allocation,an improved particle swarm optimization algorithm is adopted to realize the reasonable allocation of CPU resources of edge nodes.Based on the semi-definite planning algorithm,network resources are allocated more effectively.Finally,the simulation experiment shows that the allocation strategy proposed in this paper is more effective.