Adaptive Scheduling Based on Intelligent Agents in Edge-Cloud Computing Environments
JongBeom Lim · 網際網路技術學刊 · 2024
Scheduling in cloud computing environments has been extended to support the Internet of Things (IoT) applications, which require additional quality of services such as energy consumption and real-time properties. To this end, edge-cloud computing environments are prevalently deployed by encompassing the fog management layer. However, traditional scheduling techniques for cloud tasks have limited capabilities to support real-time properties required for IoT applications. In this paper, we propose a deep learning-based dynamic cloud scheduling technique using intelligent agents, which intelligently adapt to users’ requirements and selective quality of services based on distributed learning in edge-cloud computing environments. The proposed cloud task scheduling method is composed of two logical components: distributed learning management (learning distribution and aggregation) and intelligence management of multi-agents, which are independent of each other. The performance results show that the self-employed agents intelligently adapt to their environments and perform hyperparameter learning for efficient and effective task scheduling in edge-cloud computing environments.