DNN-Driven Task Scheduling for High Performance Edge-Cloud Heterogeneous Systems

Integrating Artificial Intelligence (AI) into Internet of Things (IoT) devices has significantly increased computational demands and reduced latency and energy efficiency requirements on Cloud Servers (CSs). Edge Servers (ESs) address latency and energy efficiency requirements, but introduce the complexity of optimal task scheduling among servers. Existing solutions often prioritize energy or latency reduction while overlooking critical factors such as server utilization to optimize task scheduling. In this work, we propose a deep learning-driven task scheduling framework for heterogeneous systems based on the edge cloud to improve performance. A heterogeneous system comprising 40 IoT devices, one CS, and four ESs is evaluated with varying task sizes from 2 GiB to 10GiB. Each ES is employed with a Deep Neural Network (DNN) for optimizing task scheduling to minimize execution time, energy consumption, and CS utilization simultaneously. Comparative analysis demonstrates that our approach achieves up to 21.9% and 275.4% faster execution times compared to the Optimal Pairing Ratio (OPR) method and traditional cloud-only methods, respectively. Energy consumption is reduced by up to 12.8% over OPR and 112.1% over the cloud-only method, respectively. Additionally, CS utilization is reduced by 7.2% over OPR and 38.4% over the cloud-only method. This study can be further extended to improve the scalability of the edge-cloud heterogeneous system.