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