Autonomous Resilience in Distributed Systems through AIBased Failure Prediction and Dynamic Recovery

Abstract

Resilience is more than recovery. A resilient system anticipates trouble, absorbs disruption
while continuing to deliver its most important functions, responds with the recovery that best
fits the situation, recovers fully, and learns for next time. Most self-healing designs address
only part of this cycle, usually detection and restart. This article proposes a framework for
autonomous resilience in distributed systems that uses artificial intelligence (AI) across the
whole cycle. Failure prediction anticipates disruptions; AI-assisted incident management
monitors and triages; a dynamic recovery planner chooses among recovery modes, including
restart, rerouting, recomputation from lineage, and graceful degradation, according to
predicted impact; a risk-governed workflow keeps high-impact choices under human control;
and post-incident learning updates models, which are released without downtime. The
framework runs on high-volume event streaming and fault-tolerant distributed data
processing, and quantifies resilience as the loss of service over time rather than as uptime
alone. Using a design-oriented approach grounded in eleven studies published between 2003
and 2023, the article maps the evidence base, links resilience capabilities to AI functions,
defines dynamic recovery modes, proposes resilience measures, and illustrates the design
with a public health data platform during an outbreak. It argues that autonomous resilience
means choosing the right recovery for the moment, not merely recovering fast.