计算机应用研究2023，Vol.40Issue(12)：3606-3613,8.DOI:10.19734/j.issn.1001-3695.2023.03.0144
基于注意力时空卷积和A2C的虚拟机主动容错优先迁移决策
Active fault-tolerant priority migration decision model for virtual machines based on attentional spatio-temporal convolution and A2C
摘要
Abstract
This paper proposed an active fault-tolerant priority migration decision model for virtual machines based on atten-tional spatio-temporal convolution and A2C(AST-A2C).This method improved reliability and adapted to volatile workloads in resource-constrained edge cloud computing environments.Firstly,it used a long short-term memory network(LSTM)with an attention mechanism to extract the temporal features in each host.It combined temporal features and interaction information be-tween multiple hosts to build graph networks.Then it used graph attention network(GAT)to extract association information between different hosts in the network,and the association information would be used to encode the fault information of hosts.Next,it designed the A2C module to dynamically build models and continuously generate improved decisions,this module could combine fault code information and scheduling decision information for priority migration decision.It constructed adaptive loss functions to optimize scheduling decisions to meet different user QoS requirements and application settings.Experimental results show that the proposed model outperforms the state-of-the-art baseline in terms of fault detection,energy consumption,and de-lay sensitivity.It is ideal for improving the reliability of edge cloud computing.关键词
虚拟机调度/时空卷积/A2C/主动容错/优先迁移Key words
virtual machine scheduling/spatio-temporal convolution/A2C/active fault-tolerant/priority migration分类
信息技术与安全科学引用本文复制引用
党伟超,武婷玉..基于注意力时空卷积和A2C的虚拟机主动容错优先迁移决策[J].计算机应用研究,2023,40(12):3606-3613,8.基金项目
太原科技大学博士科研启动基金资助项目(20202063) （20202063）
太原科技大学研究生教育创新项目(SY2022063) （SY2022063）
山西省高等学校教学改革创新项目(J20220717) （J20220717）
