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

Yuheng Liu
Additional contact information
Yuheng Liu: Xiaohongshu Inc., Xi’an, Shaanxi 710032, China

Innovation in Science and Technology, 2026, vol. 5, issue 1, 35-45

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.

Keywords: cascading resilience; predictive safeguard; system stability; billion scale concurrency; adaptive rate limiting; hierarchical degradation; autonomous recovery; ensemble forecasting (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.paradigmpress.org/ist/article/view/2073/1930 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bdz:inscte:v:5:y:2026:i:1:p:35-45

DOI: 10.63593/IST.2788-7030.2026.03.005

Access Statistics for this article

More articles in Innovation in Science and Technology from Paradigm Academic Press
Bibliographic data for series maintained by Editorial Office ().