An Intelligent Framework for Self-Healing Distributed Systems Using Predictive Analytics and Automated Recovery

Abstract

Self-healing distributed systems are often described as fully autonomous: they detect a problem and fix it without people. In practice, many faults are best handled by a partnership, in which analytics predicts what is about to go wrong, recommends what to do, and either acts automatically or supports an operator in acting quickly. Health care has long experience with this kind of partnership through clinical decision support, including its main pitfall, alert fatigue. This article proposes an intelligent self-healing framework built on streaming predictive analytics and decision support. System events and metrics flow through a high-volume event streaming platform and are processed in real time. Predictive analytics forecasts when resources will be exhausted, detects anomalies in time series, and explains each prediction. A decision support layer turns predictions into ranked recovery recommendations. A risk-governed workflow executes low-risk recommendations automatically and routes the rest to operators with the evidence they need. Predictive models are updated without downtime. Using a design-oriented approach grounded in eleven studies published between 2007 and 2023, the article maps the evidence base, defines the predictive analytics the framework uses, describes the architecture, and adapts lessons from clinical decision support to recovery. It illustrates the design with a hospital's electronic health record platform. It argues that self-healing works best as a partnership in which analytics predicts, advises, and acts where safe, and people decide where judgment is needed.