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

TL;DR

This paper introduces SF-DTM, a novel cloud-based digital twin management model that enhances fault tolerance and self-healing capabilities, significantly improving service availability and resilience in digital twin applications.

Contribution

The paper proposes a new SF-DTM model integrating federated learning with cosine similarity and fault pattern analytics for improved reliability and fault tolerance in digital twin processing.

Findings

Improved service availability by up to 13.2%.

Higher Mean Time Between Failures (MTBF).

Lower Mean Time To Repair (MTTR).

Abstract

Digital twins, integral to cloud platforms, bridge physical and virtual worlds, fostering collaboration among stakeholders in manufacturing and processing. However, the cloud platforms face challenges like service outages, vulnerabilities, and resource contention, hindering critical digital twin application development. The existing research works have limited focus on reliability and fault tolerance in digital twin processing. In this context, this paper proposed a novel Self-healing and Faulttolerant cloud-based Digital Twin processing Management (SF-DTM) model. It employs collaborative digital twin tasks resource requirement estimation unit which utilizes newly devised Federated learning with cosine Similarity integration (SimiFed). Further, SF-DTM incorporates a self-healing fault-tolerance strategy employing a frequent sequence fault-prone pattern analytics unit for deciding the most admissible VM allocation. The implementation and evaluation of SF-DTM model using real traces demonstrates its effectiveness and resilience, revealing improved availability, higher Mean Time Between Failure (MTBF), and lower Mean Time To Repair (MTTR) 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.