Cascading Resilience Through Predictive Multi-Dimensional Safeguards: System Stability Architecture for Billion-Scale Concurrent Platforms

Abstract

Modern billion‑scale concurrent internet platforms suffer from explosive traffic bursts, multiplicative failure propagation, and resource contention spirals, while traditional static defense mechanisms and reactive stabilization strategies lag in prediction, lack integrated state awareness, and fail to prevent cascading failures. This paper proposes CoReliance, a cascading resilience architecture empowered by predictive multi‑dimensional safeguards for system stability in ultra‑large‑scale concurrent platforms. The framework integrates ensemble demand forecasting (TCN, seasonal decomposition, and causal feature fusion), state‑coupled dynamic rate‑limiting, reinforcement‑learned hierarchical degradation, multi‑modal fault detection, causal root‑cause localization, and closed‑loop autonomous recovery. It abandons isolated component defense and realizes proactive capacity pre‑positioning, real‑time adaptive regulation, progressive service degradation, and closed‑loop verifiable recovery. Validated in 12‑month production across two tier‑1 platforms with over 1.2 billion users, CoReliance lifts system availability from 97.08% to 99.87%, reduces mean time to recovery (MTTR) by 86.5% to 103 seconds, cuts unplanned outages by 94.3%, prevents 31 major incidents, and achieves 490% annual return on investment with a 1.8‑month payback period. This architecture provides end‑to‑end stability assurance for high‑concurrency social commerce, ride‑hailing, and similar large‑scale internet systems.