CAJ | 학술논문

针对部署在云环境下的云应用资源配置优化问题，提出了通过感知服务质量变化自适应配置云应用资源的策略。设计了一种基于采集到的历史运行指标数据生成贝叶斯网络概率推理模型，并利用预定义应用服务质量目标（Service Level Objectives，SLO）找到资源超配或不足的虚拟设备，生成资源优化方案。在仿真环境下验证测试结果显示，开启自适应配置算法的云应用请求响应时间指标明显优于未开启的云应用。
침대부서재운배경하적운응용자원배치우화문제，제출료통과감지복무질량변화자괄응배치운응용자원적책략。설계료일충기우채집도적역사운행지표수거생성패협사망락개솔추리모형，병이용예정의응용복무질량목표（Service Level Objectives，SLO）조도자원초배혹불족적허의설비，생성자원우화방안。재방진배경하험증측시결과현시，개계자괄응배치산법적운응용청구향응시간지표명현우우미개계적운응용。
It focuses on the problem of resources configuration optimization for cloud applications running in cloud envi-ronment, proposes a service level objective aware cloud application resources auto-adaptive configuration technique. It presents methods to generate Bayesian network based probabilistic reasoning model by the collected historical metrics data, and uses predefined application quality of service level objectives to find virtual devices in the resource over-provisioning or under-provisioning state, hence generates optimization strategy accordingly. Emulation test environment results show that the request-response time of cloud application with the adaptive reconfiguration optimization algorithm open is better than others.