关键词: 深度神经网络, 大样本仿真, 资源预测, 集群管理

Abstract: With the development of artificial intelligence,operation experiments tend to be intelligent.Large-scale operation simulation is an important support for conducting intelligent operation experiments and an effective means to solve problems such as multiple variables and complex combinations in operation experiments.It has the characteristics of large sample size and high speed requirements.The high-speed operation of massive simulation samples depends on the efficient scheduling of high-perfor-mance hardware clusters,which faces the problems of large differences in computing resource requirements and difficult manual allocation.How to accurately predict and dynamically allocate the resources required for each sample is the key to improving the efficiency of large-scale simulation.This paper proposes a deep neural network(DNN)-based resource prediction model for large-scale operation simulation.The method firstly constructs a deep neural network in-loop simulation resource management architecture.Secondly,it constructs a deep neural network prediction model by extracting features and learning from combat simulation sample files.During the operation of large-scale simulation,it achieves accurate prediction and dynamic allocation of massive ope-ration simulation job resources by online predicting the computing resources required for each sample.Test results show that in a typical operation experiment simulation scenario with thousands of samples,theproposed prediction model reduces the completion time by 20.8% on 10 high-performance server nodes compared to traditional configuration methods.

Key words: Deep neural network, Large-scale simulation, Resource prediction, Cluster management