智能配置与负载感知调度的融合主机虚拟资源优化OA

Addressing the issues of inaccurate virtualized resource allocation and low scheduling efficiency caused by the coexistence of heterogeneous applications and frequent dynamic loads in edge computing platforms,a joint optimization method for virtualized resources of a converged host based on intelligent configuration and load-aware scheduling was proposed,using a train-related converged host as a typical application scenario.Firstly,by quantitatively analyzing the resource demand characteristics of typical applications of the converged host,a resource allocation prediction model based on random forest modeling and binary search method was constructed to achieve precise and forward-looking allocation of virtualized resources.Secondly,in response to dynamic load changes,an improved genetic algorithm was designed.This algorithm mapped virtualized applications to physical CPU cores and dynamically adjusted the scheduling strategy by combining a multi-objective fitness function that considers resource utilization and application performance.Experimental results showed that,compared to the traditional dominant resource fairness(DRF)algorithm,the resource allocation prediction model proposed provided configuration parameters superior to those initialized manually.Meanwhile,the improved genetic algorithm simultaneously increased the average CPU utilization from 13.5%to 22.07%,representing a relative increase of 63.5%.The objective function value increased from 0.035 to 0.204,a approximately 4.83-fold increase,reducing the total server resource consumption by 44%,and effectively saved hardware costs and energy consumption.The study provides a general method for resource optimization in edge computing platforms under highly dynamic scenarios,and verifies its feasibility using the train converged host as an example.It has universal reference value for the construction of intelligent edge systems.