Joint DNN Partition and Resource Allocation for Task Offloading in Edge-Cloud-Assisted IoT Environments

Multiaccess edge computing (MEC) is a promising approach to enhancing IoT devices running AI-based services. Especially, the edge-cloud architecture acts as a strong supporter of the resource-limited IoT devices. How to optimize the system resources efficiently to improve the service performance is the key issue in this scenario. Motivated by this, in this article, we focus on a multi-base station (BS) and multiservice edge-cloud-assisted IoT environment, where both the BSs (with edge servers deployed) and the cloud can assist the IoT devices to process multitype deep learning (DL) tasks via task offloading. DNN partition mechanism and both the communication and computing resources allocation are utilized to enable a collaborative optimization to minimize the processing delay of all the DL tasks in the system. Due to the mixed-integer nonlinear programming (MINLP) characteristic of our optimization problem, we propose an algorithm that decomposes the original problem into two subproblems, solves them separately, and then obtains the near-optimal solution efficiently. Extensive simulations are conducted by varying five different crucial parameters. The superiority of our scheme is demonstrated in comparisons with several other schemes proposed by existing works. Our scheme can achieve a notable 28.3% delay reduction on average.

Automated summary

In this article, a collaborative optimization approach is proposed for multi-base station and multiservice edge-cloud-assisted IoT environment, aiming to minimize the processing delay of all deep learning tasks. By utilizing task offloading, DNN partition mechanism, and resource allocation, the proposed scheme achieves a notable average delay reduction of 28.3% compared to existing works, as demonstrated through extensive simulations.