AI-Driven Disaster Recovery in Distributed Cloud Systems

Abstract

AI-driven disaster recovery in distributed cloud systems represents a paradigm shift from reactive, manual failover procedures to proactive, intelligent orchestration capable of anticipating failures, automating remediation tasks, and optimizing resource utilization. In this expanded abstract, we delve into the motivations, core technical components, and key findings of this study. We begin by articulating the limitations of traditional disaster recovery approaches—manual runbooks and rule‑based automation—that often lead to excessive recovery times, human error, and inefficient resource allocation. Next, we describe our novel framework, which integrates large-scale data ingestion from heterogeneous cloud monitoring services, deep learning–based failure prediction models leveraging Long Short‑Term Memory (LSTM) networks, federated learning to enhance model generalization across multiple tenants, and an AI-enhanced orchestration engine that dynamically selects and sequences recovery workflows based on predicted failure impact, service-level objectives (SLOs), and cost constraints.

We detail how the monitoring module aggregates logs, metrics, and traces from AWS CloudWatch, Azure Monitor, and GCP Stackdriver into a unified time‑series database, where data normalization and feature engineering take place. The prediction engine employs LSTM models trained on months of historical data, achieving early warning of service degradation up to ten minutes in advance with high precision and recall. Federated learning across three simulated tenants further boosts predictive accuracy by 7%, while preserving tenant privacy. The orchestration engine maintains a library of declarative recovery playbooks—ranging from container redeployment and virtual machine failover to traffic rerouting—and applies an AI planner that reasons over predicted failure scenarios, workload forecasts, and real‑time cost metrics to choose the most effective recovery path. To foster operator trust and compliance, explainable AI techniques such as SHAP (SHapley Additive exPlanations) are embedded to generate human‑readable rationales for each automated decision.

Our evaluation employs a hybrid multi‑cloud testbed replicating real‑world application workloads: a microservices‑based e‑commerce platform subject to synthetic and chaotic failure injections (Chaos Monkey, Pumba). Compared to manual runbooks and rule‑based automation, our framework reduces the average Recovery Time Objective (RTO) by 46% (from 5.8 to 3.1 minutes), cuts resource overprovisioning during recovery by 32%, and decreases SLA violation rates from 15% to under 6%. Operator surveys indicate a 4.3/5 satisfaction with explainability features, underscoring the practical viability of AI-driven recovery. We conclude by discussing research directions: real‑time adaptation via reinforcement learning, integration with Infrastructure-as-Code pipelines for continuous validation, and advanced federated architectures for cross‑provider collaboration. This comprehensive study demonstrates that embedding AI throughout the DR lifecycle markedly enhances resilience, cost efficiency, and service continuity in distributed cloud environments.