云计算环境下关联节点的异常判断

doi:10.11896/jsjkx.191200186

计算机科学 ›› 2021, Vol. 48 ›› Issue (1): 295-300.doi: 10.11896/jsjkx.191200186

云计算环境下关联节点的异常判断

雷阳, 姜瑛

云南省计算机技术应用重点实验室昆明 650500
昆明理工大学信息工程与自动化学院昆明 650500

收稿日期:2019-12-31 修回日期:2020-06-13 出版日期:2021-01-15 发布日期:2021-01-15
通讯作者: 姜瑛(jy_910@163.com)
作者简介:201015700@qq.com
基金资助:
国家自然科学基金(61462049,61063006,60703116);云南省应用基础研究计划重点项目基金(2017FA033)

Anomaly Judgment of Directly Associated Nodes Under Cloud Computing Environment

LEI Yang, JIANG Ying

Yunnan Key Lab of Computer Technology Application,Kunming 650500,China
Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China

Received:2019-12-31 Revised:2020-06-13 Online:2021-01-15 Published:2021-01-15
About author:LEI Yang,born in 1992,postgraduate,is a member of China Computer Federation.His main research interests include cloud computing and big data analysis.
JIANG Ying,born in 1974,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.Her main research interests include software quality assurance and testing,cloud computing,big data analysis and intelligent software engineering.
Supported by:
National Natural Science Foundation of China(61462049,61063006,60703116) and Key Project of Yunnan Applied Basic Research(2017FA033).

摘要/Abstract

摘要： 当前,越来越多的用户选择将服务部署到云计算环境中。然而,云计算服务的多样性以及部署环境的动态性,会导致云计算节点出现异常。传统的节点异常检测方法只针对异常的单一节点,忽略了异常节点对关联节点的影响,从而造成异常传播和关联节点失效等问题。文中提出了一种云计算环境下关联节点的异常判断方法。首先,将Agent部署在各节点上,并通过Agent以特定时间间隔采集节点运行数据,根据节点之间的关联关系建立节点关系图;其次,使用运行数据训练异常检测模型,计算运行数据的权值和综合评分,通过基于滑动时间窗口的方法判断单一节点是否出现异常;最后,在单一节点出现异常的情况下,使用基于标准互信息的方法找出受异常节点影响的其他关联节点。在搭建的云计算平台上,通过模拟各类异常情况,并观察注入异常下节点的状态,验证了文中单一节点异常判断方法和关联节点判断方法的有效性。实验结果表明,该方法在判断单一节点异常的正确率和特异度时都优于其他方法,且在多节点结构下可以准确找到关联的异常节点,具有较高的准确率和稳定性。

关键词: 标准互信息, 单一节点, 关联节点, 异常判断, 云计算环境

Abstract: Currently,more and more users deploy their services on cloud computing environment.Due to the services diversity and dynamic deployment,the anomalies will be occurred on nodes under cloud computing environment.The impact of anomaly nodes on associated nodes are usually neglected in the traditional node anomaly detection methods,which will result in anomaly propagation and nodes failure.In this paper,a method of anomaly judgment for directly associated nodes under cloud computing environment is proposed.At first,the Agent is deployed on each node and the running data of nodes are collected through the Agent at specific time intervals.The node relationship graph is established based on the relationship between the nodes.Secondly,the anomaly detection model is trained by the running data.Then the weights and comprehensive scores of the running data is calculated.The anomaly of the single node is judged by the sliding time window-based method.Finally,other nodes affected by the anomaly nodes are found through the normalized mutual information in the case of a single node anomaly.In this paper,the relevant experiments are carried out on the cloud computing platform.In order to simulate all kinds of anomaly situations,the anomaly conditions are injected during the experiment and the state of nodes under the injection anomaly is observed.The validity of single node and directly associated node of anomaly judgment method was verified by experiments.The experimental results showed that the accuracy and specificity of the method in this paper are better than other methods about single node anomaly judgment.Under the multi-node structure,the method of this paper could find the directly associated anomaly node with the higheraccuracy and stability.

Key words: Anomaly judgment, Cloud computing environment, Directly associated nodes, Normalized mutual information, Single node

中图分类号:

TP311

雷阳, 姜瑛. 云计算环境下关联节点的异常判断[J]. 计算机科学, 2021, 48(1): 295-300. https://doi.org/10.11896/jsjkx.191200186

LEI Yang, JIANG Ying. Anomaly Judgment of Directly Associated Nodes Under Cloud Computing Environment[J]. Computer Science, 2021, 48(1): 295-300. https://doi.org/10.11896/jsjkx.191200186

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云计算环境下关联节点的异常判断

Anomaly Judgment of Directly Associated Nodes Under Cloud Computing Environment

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