AI-Driven Adaptive Distributed Caching for Enterprise Microservice Integration: A Validation Gated Learning Framework for Dynamic TTL, Admission, and Eviction Control

Abstract

Enterprise microservices require distributed caches, but existing policies (one expiration policy and one eviction policy) are set ahead of time and often fail to adapt to dynamic access patterns, data mutation, and changing tenant requirements. This paper proposes VGACO, a Validation Gated Adaptive Cache Orchestration framework that dynamically learns and recommends caching policies: admission, dynamic TTL, refresh-ahead, tiering, and eviction, at a service-layer cache and a shared distributed cache layer in Kubernetes. Recommendations are validated against deterministic gates covering calibrated confidence, staleness, SLO compliance, cost, tenant fairness, security, and drift before canary promotion to production. A static fallback cache policy is always available. The controller interacts with service-owned data through cache contracts and versioned domain events. A reproducible experimental protocol compares five configurations: no cache, static, rule-based, validation-gated, and ungated. The framework positions learned caching control as a governed, reversible infrastructure for enterprise integration.