Optimal Resource Allocation for AIoT as a Service Under Various Service Scenarios and Architectures

Ren-Hung Hwang, Tsai-Ying Chou, Jia-You Lin, Didik Sudyana, Yuan-Cheng Lai, Ying-Dar Lin

Abstract: The integration of artificial intelligence (AI) with the Internet of Things (IoT) marks a significant advancement in sixth-generation (6G) networks. The complexity of these AIoT services has promoted an as-a-service model, where service providers offer tailored architectures to meet varied application needs. Despite the critical importance of optimizing both training and inference in service architectures, this aspect remains under-explored. Our study introduces service scenarios such as ‘no shared (NS)’, where tenants manage their data and models independently, ‘data shared (DS)’, where tenants provide data for collective training, and ‘parameter sharing (PS)’, where only model parameters are shared. We utilize a tandem queue model to simulate the communication and computing demands across cloud-edge-fog architectures. Our proposed Cost and Delay Resource Allocation (CDRA) method significantly reduces costs, with edge and fog-based training and inference lowering costs by up to 44% compared to cloud setups. The evaluation shows that the NS scenario is resource-intensive but offers high privacy, DS is cost-effective and improves model accuracy, and PS balances privacy with longer wait times. These findings provide service providers with a comprehensive comparison of service scenarios and architectures, offering guidance for strategic and economically sound decisions in the ever-evolving landscape of AIoT.

External IDs:dblp:journals/tnsm/HwangCLSLL25