基于v-Informer的云平台资源负载预测方法  

Load Prediction Method of Cloud Resource Based on v-Informer

作　　者：尤文龙 邓莉[1,2] 李锐龙 谢雨欣 任正伟 YOU Wenlong;DENG Li;LI Ruilong;XIE Yuxin;REN Zhengwei(College of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China;Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System,Wuhan 430065,China)

机构地区：[1]武汉科技大学计算机科学与技术学院,武汉430065 [2]智能信息处理与实时工业系统湖北省重点实验室,武汉430065

出　　处：《计算机科学》2024年第12期147-156,共10页Computer Science

基　　金：国家自然科学基金(61902285)。

摘　　要：目前,云计算技术的使用非常广泛。随着用户量的增加,云计算资源的分配管理也越来越重要,而准确的负载预测是分配管理的重要依据。但由于云平台任务有多个负载特征,且特征的相关性变化趋势各不相同,因此难以从长期的历史数据中提取出有效的依赖信息。在Informer模型的基础上,提出了一种针对高动态云平台任务CPU长期负载预测方法v-Informer,该方法通过变分模态分解来分解负载序列中的变化趋势,引入多头自注意力机制捕获其中的长期依赖性和局部非线性关系,同时应用梯度集中技术改进优化器,减少计算开销。分别在微软云平台和谷歌云平台数据上进行实验,结果表明,与目前已有的CPU负载预测模型LSTM,Transformer,TCN和CEEMDAN-Informer相比,v-Informer在Google数据集上的预测误差分别减少了34%,19%,15%和6.5%;在微软数据集上的预测误差分别减少了32%,16%,12%和7%,具有较好的预测精度。Cloud computing technology is widely used at present.With the increase in the number of users,the allocation and management of cloud computing resources is becoming more and more important,and accurate load prediction is an important basis for allocation and management.Based on the Informer model,this paper proposes a long-term CPU load prediction method for high dynamic cloud platform tasks,called v-Informer.v-Informer decomposes the variation trend in the load sequence through va-riational mode decomposition,and introduces a multi-head self-attention mechanism to capture the long-term dependence and local nonlinear relationship.At the same time,the gradient concentration technique is used to improve the optimizer and reduce the computational cost.Experiments are carried out on the data of Microsoft and Google cloud platforms.The results show that,compared with the existing CPU load prediction models LSTM,Transformer,TCN and CEEMDAN-Informer,the prediction error of v-Informer is reduced by 34%,19%,15%and 6.5%respectively on the Google dataset.The prediction error on the Microsoft dataset is reduced by 32%,16%,12%and 7%respectively,with better prediction accuracy.

关 键 词：云平台 CPU负载 多步预测 模态分解 INFORMER 梯度收敛 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]