Anomaly Detection for Nodes Under the Cloud Computing Environment

2021 ◽  
Vol 12 (1) ◽  
pp. 30-48
Author(s):  
Yang Lei ◽  
Ying Jiang

Due to the services diversity and dynamic deployment, the anomalies will occur on nodes under cloud computing environment. If a single node generates an anomaly, the associated nodes are affected by the abnormal node, which will result in anomaly propagation and nodes failure. In this paper, a method of anomaly detection for nodes under the cloud computing environment is proposed. Firstly, the node monitoring model is established by the agents deployed on each node. Secondly, the comprehensive score is used to identify abnormal data. The anomaly of the single node is judged by the time window-based method. Then, the status of directly associated nodes is detected through normalized mutual information and the status of indirectly associated nodes is detected through the node attributes in the case of a single node anomaly. Finally, other associated nodes affected by the abnormal node are detected. The experimental results showed that the method in this paper can detect the anomalies of single node and associated node under the cloud computing environment effectively.

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jin Gao ◽  
Jiaquan Liu ◽  
Sihua Guo ◽  
Qi Zhang ◽  
Xinyang Wang

Aiming at problems such as slow training speed, poor prediction effect, and unstable detection results of traditional anomaly detection algorithms, a data mining method for anomaly detection based on the deep variational dimensionality reduction model and MapReduce (DMAD-DVDMR) in cloud computing environment is proposed. First of all, the data are preprocessed by a dimensionality reduction model based on deep variational learning and based on ensuring complete data information as much as possible, the dimensionality of the data is reduced, and the computational pressure is reduced. Secondly, the data set stored on the Hadoop Distributed File System (HDFS) is logically divided into several data blocks, and the data blocks are processed in parallel through the principle of MapReduce, so the k-distance and LOF value of each data point can only be calculated in each block. Thirdly, based on stochastic gradient descent, the concept of k-neighboring distance is redefined, thus avoiding the situation where there are greater than or equal to k-repeated points and infinite local density in the data set. Finally, compared with CNN, DeepAnt, and SVM-IDS algorithms, the accuracy of the scheme is increased by 10.3%, 18.0%, and 17.2%, respectively. The experimental data set verifies the effectiveness and scalability of the proposed DMAD-DVDMR algorithm.


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