JUCS - Journal of Universal Computer Science 32(8): 1150-1172, doi: 10.3897/jucs.175609
A Proactive Fault Tolerance Approach for Saga Transactions in Microservices Using a Health Prediction Model
Jiangke Wu‡, Xiaojun Chen§, Rongxing Shi|‡ Northern Arizona University, Flagstaff, United States of America§ Zhejiang University, Hangzhou, China| Nanjing Institute of Technology, Nanjing, China
Corresponding author: Jiangke Wu ( jw2966@nau.edu ) © Jiangke Wu, Xiaojun Chen, Rongxing Shi. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Citation:
Wu J, Chen X, Shi R (2026) A Proactive Fault Tolerance Approach for Saga Transactions in Microservices Using a Health Prediction Model. JUCS - Journal of Universal Computer Science 32(8): 1150-1172. https://doi.org/10.3897/jucs.175609 |  |
Abstract
The Saga pattern guarantees eventual consistency in microservices but depends on passive post-failure compensation, resulting in resource inefficiency and possible data discrepancies. This study introduces the ”Proactive Saga Failure Avoidance (PSFA)” system, which incorporates deep learning-based health evaluation into the Saga orchestrator. The platform employs an LSTM model to monitor real-time service metrics, enabling the prediction of imminent failures prior to transaction execution. Upon detection of high risk, the transaction is proactively terminated to prevent erroneous computation and intricate rollbacks. Experimental findings obtained in a controlled environment indicate that PSFA detects failure precursors with an accuracy of 1.00 and a recall of 0.99. This method provides an innovative alternative to improve the reliability and efficiency of distributed transactions in microservice settings.
Keywords
Microservices, Distributed Transactions, Saga Pattern, Proactive Fault Tolerance, LSTM, AIOps, Predictive Maintenance