OPTIMIZATION OF THE APPLICATION-LEVEL CHECKPOINTING INTERVAL IN STATEFUL MICROSERVICES

Abstract

This paper addresses the complex scientific and applied problem of enhancing the operational readiness of stateful microservice architectures. The foundation of the proposed approach is the fine-grained optimization of the chronological frequency of checkpoint generation directly at the application abstraction level. The study aims to formulate a mathematical model and subsequently empirically verify the critical state fixation interval that guarantees a strict balance between context rehydration kinetics and associated infrastructural overhead. An interdisciplinary methodological framework is employed, integrating systems analysis methods, classical rollback recovery theory, and modern chaos engineering tools deployed within a Kubernetes ecosystem. The destructive impact of a hyperactive caching strategy is proven. An aggressive fixation periodicity of 5 seconds induces anomalous CPU load (5.77%) and provokes the exponential accumulation of objects in the garbage collector memory (5.81 MB), degrading the aggregate recovery time to 0.795 s. Conversely, the experimental basis confirms that a calibrated window of 10 - 15 seconds radically neutralizes the intensity of background perturbations. This balanced mode not only ensures a minimal latent period of application recovery (0.503 - 0.563 s) but also strictly limits the amnesia interval - the time gap between the last successful checkpoint and the moment of failure - restricting the volume of event replay to strictly manageable limits (34 - 47 events). The derived regularities form the theoretical foundation for designing highly resilient systems with deterministic cold start characteristics, completely eliminating the probability of operational degradation under standard load conditions.