Authors: Dr. Anil Karadwal
Abstract: Distributed cloud–edge systems support modern applications by combining centralised cloud infrastructure with computing resources located near data sources. Despite their advantages, these environments remain vulnerable to hardware faults, software failures, network disruptions, resource contention, and changing workloads. Conventional fault-tolerance mechanisms often respond after a failure has occurred, potentially increasing service interruption and recovery costs. Artificial Intelligence (AI) and Digital Twin (DT) technology offer opportunities to anticipate system degradation, evaluate alternative responses, and support automated recovery. This paper proposes a conceptual framework integrating digital twins, AI-based fault prediction, adaptive resource management, and self-healing mechanisms for distributed cloud–edge environments. The framework comprises six components: real-time monitoring, digital representation, predictive analytics, risk-aware decision-making, self-healing execution, and continuous feedback. It also identifies challenges relating to data quality, prediction uncertainty, model drift, computational overhead, security, and coordination across distributed nodes. A proposed evaluation methodology outlines measurable indicators, including prediction performance, service availability, recovery time, false-alarm rate, and resource overhead. The paper contributes a structured design approach for future investigation of predictive and autonomous fault management. As a conceptual study, it does not claim experimental validation or quantified performance improvements. Its proposed framework requires implementation and empirical evaluation under representative cloud–edge workloads.
DOI: https://doi.org/10.5281/zenodo.23414685
