Semantic Degradation in Long- Lived Machine-Learned Data Pipelines

Abstract

The integration of adaptive routing mechanisms into distributed transaction systems introduces dynamic decision-making that significantly alters execution ordering and concurrency behavior. While such routing enhances system performance by improving load distribution and reducing latency, it simultaneously increases the likelihood of serialization failures due to disrupted transaction locality and unpredictable interleavings. This study systematically analyzes the interaction between adaptive routing and traditional concurrency control mechanisms, demonstrating that non-deterministic routing decisions lead to elevated conflict rates, particularly under high-load conditions. The results highlight a critical trade-off between performance optimization and consistency preservation, emphasizing that routing strategies must be designed with concurrency-awareness to maintain transactional integrity. The findings contribute to a deeper understanding of how intelligent routing layers influence system correctness and provide a foundation for developing more robust, hybrid transaction management frameworks.