A Self-Healing and Fault-Tolerant Cloud-Based Digital Twin Processing Management Model

Abstract

Digital twins (DTs), integral to cloud platforms, bridge physical and virtual worlds, fostering collaboration among stakeholders in manufacturing and processing. However, the cloud platforms face challenges such as service outages, vulnerabilities, and resource contention, hindering critical DT application development. The existing research works have limited focus on reliability and fault tolerance in DT processing. In this context, this article proposed a novel self-healing and fault-tolerant cloud-based digital twin processing management (SF-DTM) model. It employs collaborative DT tasks resource requirement estimation unit that utilizes newly devised federated learning with cosine similarity integration. Furthermore, SF-DTM incorporates a self-healing fault-tolerance strategy employing a frequent sequence fault-prone pattern analytics unit for deciding the most admissible virtual machine (VM) allocation. The implementation and evaluation of the SF-DTM model using real traces demonstrates its effectiveness and resilience, revealing improved availability, higher mean time between failure, and lower mean time to repair compared with non-SF-DTM approaches, enhancing collaborative DT application management. SF-DTM improved the services availability up to 13.2% over non-SF-DTM-based DT processing.