EdgeCPS-AI Knowledge Sharing Model for Supporting Computing Partition Services.

The rapid development of the AI industry had led to the widespread utilization of numerous edge devices in the real world. However, AI services still heavily rely on high-performance computing systems, which means that these devices are not being effectively utilized. To address these issues, EdgeCPS technology has emerged. EdgeCPS is a technology that supports the seamless execution of various services, including AI applications, through the interconnection of edge devices and edge servers, as well as resource and function augmentation. In order to appropriately utilize edge devices in an EdgeCPS environment, the paper proposes an EdgeCPS-AI knowledge sharing model for supporting computing partition services. The proposed EdgeCPS-AI knowledge is represented as a graph, which systematically structures AI service-related information that arises on virtual and physical edge devices. This knowledge provides not only partitioned AI weighted models in order to support the characteristics of EdgeCPS, but also enables flexible provision of various information required for AI services. Thus, this approach can facilitate the reconstruction of AI services according to service requirements and maximize the utilization of edge devices. To achieve this, a microservice-based computing partition AI service is devised and experimentally proven by constructing a Kubernetes system using 8 heterogeneous edge devices and a graph DB system consisting of 2,762 nodes. Experimental results show that performing computing partition services on edge devices is significantly more efficient in terms of overall resource consumption, with up to 85.57% improvement, as compared to performing the same tasks on high-performance computing systems.