Distributed Intelligence for Distributed Systems Resilience: A Meta-Analysis of Artificial Intelligence Driven Self-Healing Systems - Kalyan Chakravarthy Thatikonda, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
This paper presents a meta-analysis of artificial intelligence applications for autonomous self-healing in distributed systems during cloud infrastructure failures. Previous research has established that distributed systems experience availability zone outages and network partitions with increasing frequency as system complexity grows (Chen et al., 2022; Gunawi et al., 2023). While traditional resilience approaches have focused on redundancy and manual recovery procedures (Zhang, 2021; Verma et al., 2024), they frequently fail to address the inherent uncertainty in complex failure propagation patterns. Building upon recent advancements in distributed anomaly detection (Liu & Johnson, 2023) and multi-agent systems (Patel et al., 2024), our work synthesizes findings from these domains to develop an integrated framework for autonomous failure management. We systematically review the literature on cloud failure patterns across major providers from 2020-2024, identifying critical gaps in current detection and remediation capabilities. Our contribution extends existing research in three significant ways: First, we establish a taxonomy of distributed system failures that integrates causal relationships identified in previous studies. Second, we demonstrate how recent advances in causal inference models can be adapted to distributed systems for improved root cause analysis during complex outages, addressing limitations identified in prior diagnostic frameworks (Sharma & Wong, 2023). Third, we propose an architectural reference model that incorporates reinforcement learning techniques for recovery orchestration, building upon preliminary work in this area (Martinez et al., 2024) while addressing challenges in coordination during partial connectivity. The proposed framework provides a foundation for future research in AI-driven resilience engineering and offers implementation guidance for enhancing self-healing capabilities in mission-critical distributed systems.
Keywords: Artificial Intelligence; Distributed Systems; Cloud Computing; Self-Healing; Resilience Engineering; Availability Zones; Anomaly Detection; Autonomous Recovery; Reinforcement Learning; Causal Inference