MicroMILTS: Fault Location for Microservices Based Mutual Information and LSTM Autoencoder

Abstract

Driven by the development of cloud computing and artificial intelligence, architecture has dramatically improved in terms of flexibility and scalability in software development. Therefore, it is increasingly being used to build large-scale applications for agile development. However, along with the technology heterogeneity, the dynamics of running instances, and the complexity of service dependencies, fault localization is extraordinarily difficult. In this paper, we present MicroMILTS, a microservice fault location method based on mutual information and an LSTM Autoencoder. MicroMILTS first uses BIRCH for anomaly detection based on the analysis of the performance metrics data correlated to microservice anomalies. Once anomalies are detected, a service dependency property graph is constructed based on the real-time microservice invocation relationships and the reconstructed deviations of performance metrics with the LSTM Autoencoder. Next, MicroMILTS dynamically updates the weight of each node in the service dependency property graph. Then, a PageRank-based random walk is applied for further ranking root causes. Finally, a Sock-shop microservice system is built on the Huawei Cloud to evaluate the performance of MicroMILTS. The experiment shows that MicroMILTS achieves a good root cause location result, with 90.4 % in precision and 91.6% in mean average precision, outperforming state-of-the-art methods.