Real-time detection algorithm of abnormal data for cloud platform based on microservice architecture

Rundong Gan; Wei Wei; Jie Tang; Qidi Hu; Xingchuan Wang

doi:10.1117/12.3034326

16 October 2024 Real-time detection algorithm of abnormal data for cloud platform based on microservice architecture

Rundong Gan, Wei Wei, Jie Tang, Qidi Hu, Xingchuan Wang

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 132916O (2024) https://doi.org/10.1117/12.3034326
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

Aiming at the traditional framework model of cloud platform with microservice architecture which usually only deals with single-source data and suffers from high latency and poor privacy, this paper proposes a real-time detection algorithm of anomalous data for cloud platform based on microservice architecture. On the cloud platform of microservice architecture, through real-time detection and analysis of point anomaly data, context anomaly data and collective anomaly data, potential problems can be discovered and solved in time, avoiding system failure or crash, thus guaranteeing the stable operation of the system. Microservice architecture splits the system into multiple independent microservices, each of which can be independently deployed, upgraded and extended, reducing the complexity of the system. At the same time, the real-time detection algorithm of abnormal data based on microservice architecture can be flexibly integrated into the existing monitoring system to realize unified monitoring management. Experimenting on a company's cloud platform monitoring dataset, we used manually labeled sample data and verified the accuracy and effectiveness of the method on this dataset. Eventually, we successfully achieved an anomaly detection accuracy of 97.57%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rundong Gan, Wei Wei, Jie Tang, Qidi Hu, and Xingchuan Wang "Real-time detection algorithm of abnormal data for cloud platform based on microservice architecture", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 132916O (16 October 2024); https://doi.org/10.1117/12.3034326

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KEYWORDS

Data storage

Clouds

Data modeling

Data acquisition

Data processing

Detection and tracking algorithms

Education and training

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