| Summary: | 碩士 === 國立臺灣大學 === 資訊管理學研究所 === 107 === Owing to new technological advances, most of the industries have implemented some large-scale and complicated systems as their infrastructure. Because of the improvement of Big Data Analysis and the numerous data produced by monitoring these systems, the concept of Artificial Intelligence for IT Operations (AIOps) was born. The main purposes of AIOps are to efficiently manage resources, and predict abnormal of the system and automatically deal with emergencies. As a result, introducing AIOps into IT infrastructure is an urgent demand for many companies. In our work, we collected monitoring data of bank’s IaaS platform and focused on the methods to implement AIOps in the bank.
In the past, many research implemented statistical models and machine learning models to predict virtual resource utilization and abnormal situation. In our work, we applied deep learning models, Convolutional Neural Networks(CNN), Long Short-Term Memory(LSTM) and the combination of these two models, and experiment different length of features to improve the performance of the prediction. According to the result of the experiments, the combination of LSTM and CNN is the most effective model to predict utilization and irregular situation among the algorithm used in the previous research.
Our research has favorable results which are able to predict resource usage and unusual patterns. By achieving these goals, our work could help the bank to manage the resource more efficiently, reduce the possibility of the interruption of the services as well as the cost of the maintenance, and also ensure the stability of the systems.
|
|---|
