LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning
Microservice architecture is a high-cohesion and low-coupling software architecture. Its core idea is to split the application into a set of microservices with a single function and independent deployment. Due to their complexity and large scale, microservice systems are typically fragile and failur...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10720008/ |
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| author | Xu Liu Yuewen Liu Miaomiao Wei Peng Xu |
| author_facet | Xu Liu Yuewen Liu Miaomiao Wei Peng Xu |
| author_sort | Xu Liu |
| collection | DOAJ |
| description | Microservice architecture is a high-cohesion and low-coupling software architecture. Its core idea is to split the application into a set of microservices with a single function and independent deployment. Due to their complexity and large scale, microservice systems are typically fragile and failures are inevitable. Therefore, there is an urgent need for fast and accurate anomaly detection capabilities. However, the existing microservice anomaly detection methods do not pay attention to the multi-source data of the microservice system and thus have low accuracy. To address this limitation, we propose a Log-Metric Combined Microservice Anomaly Detection approach through Graph-based Deep Learning (termed as LMGD). First, we propose a time-aware LSTM prediction neural network to improve the accuracy of service dependency mining. Secondly, based on the service dependency graph, we propose an anomaly detection method based on log-metric fusion, which can more accurately describe the running status of microservices, thereby improving the accuracy of anomaly detection. The experimental outcomes demonstrate that compared with other state-of-the-art methods, our method improves recall and F1-score by 2.63% and 1.05%. |
| format | Article |
| id | doaj-art-451b7f1f858d4efaa1ba63459333982e |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-451b7f1f858d4efaa1ba63459333982e2025-08-20T02:36:59ZengIEEEIEEE Access2169-35362024-01-011218651018651910.1109/ACCESS.2024.348167610720008LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep LearningXu Liu0Yuewen Liu1https://orcid.org/0009-0005-7964-3432Miaomiao Wei2Peng Xu3https://orcid.org/0000-0003-3635-2342China Academy of Industrial Internet, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaMicroservice architecture is a high-cohesion and low-coupling software architecture. Its core idea is to split the application into a set of microservices with a single function and independent deployment. Due to their complexity and large scale, microservice systems are typically fragile and failures are inevitable. Therefore, there is an urgent need for fast and accurate anomaly detection capabilities. However, the existing microservice anomaly detection methods do not pay attention to the multi-source data of the microservice system and thus have low accuracy. To address this limitation, we propose a Log-Metric Combined Microservice Anomaly Detection approach through Graph-based Deep Learning (termed as LMGD). First, we propose a time-aware LSTM prediction neural network to improve the accuracy of service dependency mining. Secondly, based on the service dependency graph, we propose an anomaly detection method based on log-metric fusion, which can more accurately describe the running status of microservices, thereby improving the accuracy of anomaly detection. The experimental outcomes demonstrate that compared with other state-of-the-art methods, our method improves recall and F1-score by 2.63% and 1.05%.https://ieeexplore.ieee.org/document/10720008/Anomaly detectionlogmetricsdistributed systemsGCNGAT |
| spellingShingle | Xu Liu Yuewen Liu Miaomiao Wei Peng Xu LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning IEEE Access Anomaly detection log metrics distributed systems GCN GAT |
| title | LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning |
| title_full | LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning |
| title_fullStr | LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning |
| title_full_unstemmed | LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning |
| title_short | LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning |
| title_sort | lmgd log metric combined microservice anomaly detection through graph based deep learning |
| topic | Anomaly detection log metrics distributed systems GCN GAT |
| url | https://ieeexplore.ieee.org/document/10720008/ |
| work_keys_str_mv | AT xuliu lmgdlogmetriccombinedmicroserviceanomalydetectionthroughgraphbaseddeeplearning AT yuewenliu lmgdlogmetriccombinedmicroserviceanomalydetectionthroughgraphbaseddeeplearning AT miaomiaowei lmgdlogmetriccombinedmicroserviceanomalydetectionthroughgraphbaseddeeplearning AT pengxu lmgdlogmetriccombinedmicroserviceanomalydetectionthroughgraphbaseddeeplearning |