## 2 Related Works
### Throughput Optimization Strategy Based on New Fog Architecture  
Due to the diverse nature of AIoT application scenarios, optimizing methods based solely on the traditional fog architecture must redesign their optimization strategies to cater to the unique characteristics of each scenario. This can pose difficulties for researchers and hinder technological evolution. Therefore, the applicability of the fog architecture was improved by introducing a computing framework that promoted the further development of subsequent research. Three main computing frameworks were combined with fog architecture to optimize system throughput: Fog Flow, Kafka-ML, and Elastic Fog. Fog Flow [16] is a cloud and edge computing platform that has been designed and implemented to meet the needs of AIoT service developers working in smart city scenarios. It enables developers to make elastic settings both in the cloud and at the edge to meet the requirements of different businesses. Additionally, Fog Flow supports standard interfaces that allow for the sharing and reuse of contextual data information between services. In contrast, Kafka-ML is an open-source framework that specializes in data streams for application scenarios. It can manage machine learning algorithms and AI pipelines deployed in scenarios and facilitate the deployment of DNNs in real-world applications. However, the traditional Kafka-ML cannot account for the deployment of hierarchical DNN models in a continuous structure from the cloud to the network edge. To address this problem, [PERSON] et al. [17] introduced fog computing into the Kafka-ML framework to support the deployment of distributed DNNs in a continuous structure from the cloud to the network edge. Experiment results demonstrated that the integration of fog computing with Kafka-ML can significantly enhance the system throughput and reduce the response time when compared to deploying DNNs solely in the cloud. To further optimize the deployment of fog computing in different AIoT application scenarios, a container-based solution is an effective option for massive AIoT services. Currently, the Kubernetes platform provides powerful and flexible features and has been leveraged to manage containerized applications in fog computing. An implementation of the Elastic Fog framework [18], based on the Kubernetes platform, collects real-time network traffic information and combines it with the Kubernetes scheduler to provide an elastic resource configuration of applications for each fog node. Furthermore, Kubernetes supports various efficient allocation strategies to make resource allocation decisions for applications. Experimental results demonstrate that, in comparison to the default mechanism in Kubernetes, the Elastic Fog framework significantly optimizes the system throughput and latency. Due to the fact that the joint optimization of virtual network function (VNF) embedding and flow scheduling faces several challenges of a differentiated QoS guarantee, the coupling and externality of VNF embedding, and route selection conflicts, in [19], a multi-timescale VNF embedding and flow scheduling algorithm named NEWS was proposed to maximize the throughput while reducing the VNF embedding cost and energy consumption. In [20], the issue that the lack of interconnection and collaboration between devices leaded to poor timeliness and security in IIoT resource scheduling was found, and an intelligent blockchain-enabled adaptive collaborative resource scheduling method was proposed. It introduced blockchain and AI technology to support dynamic resource scheduling in untrustworthy environments. In order to better guarantee diversified time-sensitive services for B5G/6G connected automated vehicles, literature [21] proposed a spectrum resource scheduling model for time-sensitive services and designed a two-tier joint resource scheduling method based on the age of information and bandwidth requirements under the macro base station and Road Side Unit.