A Proactive Fault Tolerance Approach for Saga Transactions in Microservices Using a Health Prediction Model

Vollständiger Abstract

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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.