Construction and efficiency analysis of an embedded system-based verification platform for edge computing.

With the profound convergence and advancement of the Internet of Things, big data analytics, and artificial intelligence technologies, edge computing-a novel computing paradigm-has garnered significant attention. While edge computing simulation platforms offer convenience for simulations and tests, the disparity between them and real-world environments remains a notable concern. These platforms often struggle to precisely mimic the interactive behaviors and physical attributes of actual devices. Moreover, they face constraints in real-time responsiveness and scalability, thus limiting their ability to truly reflect practical application scenarios. To address these obstacles, our study introduces an innovative physical verification platform for edge computing, grounded in embedded devices. This platform seamlessly integrates KubeEdge and Serverless technological frameworks, facilitating dynamic resource allocation and efficient utilization. Additionally, by leveraging the robust infrastructure and cloud services provided by Alibaba Cloud, we have significantly bolstered the system's stability and scalability. To ensure a comprehensive assessment of our architecture's performance, we have established a realistic edge computing testing environment, utilizing embedded devices like Raspberry Pi. Through rigorous experimental validations involving offloading strategies, we have observed impressive outcomes. The refined offloading approach exhibits outstanding results in critical metrics, including latency, energy consumption, and load balancing. This not only underscores the soundness and reliability of our platform design but also illustrates its versatility for deployment in a broad spectrum of application contexts.