SaaS software performance issue diagnosis using independent component analysis and restricted Boltzmann machine

摘要

SaaS software performance issue diagnosis aims to classify the type of the performance records. Deep classification method has gained much attention as a way to construct hierarchical representations from a small amount of labeled data. However, there are few researches on how to solve the classification problem of performance issues by using the deep classification method. In addition, shallow classification methods exist some problems, such as the training sample is large and the ability to fit complex functions is weak. In this article, we proposed a deep performance issue classification method based on Independent Component Analysis (ICA) and Restricted Boltzmann Machine (RBM). ICA is used to extract the features, after this process, the classification feature is obtained as RBM input, and the extracted information about performance issue is transformed into identifiable information for the classifier via visible structure of input; Hidden layer for RBM is built to realize the data transmission between hidden structure, keeping the key information; And the classification algorithm is implemented to solve our performance issue diagnosis problem of SaaS software. Experiments show that the performance of our approach is superior to the classical shallow classification algorithm, and it also meet the efficiency requirement.