Latency Determinism Breakdown in AI-Optimized Distributed Systems

Abstract

AI-optimized distributed database systems are redefining query execution and resource management by introducing adaptive, learning-based optimization strategies that improve performance under dynamic workloads. Existing research has focused primarily on efficiency gains, with limited attention to the resulting loss of latency determinism in distributed environments. A critical gap exists in understanding how AI-driven decision variability interacts with systemlevel and network-level factors to influence execution predictability. This study addresses this gap by analyzing latency variability across multiple optimization strategies and identifying the key factors that contribute to determinism breakdown. The article presents a structured evaluation framework and demonstrates how increasing optimization intelligence leads to greater execution path diversity and latency fluctuations. The findings highlight the trade-off between performance and predictability and emphasize the need for determinism-aware optimization approaches. The work provides practical insights for designing stable, high-performance distributed database systems in real-world enterprise deployments.