Adaptive Scheduling Based on Intelligent Agents in Edge-Cloud Computing Environments

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, edgecloud 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 edgecloud computing environments. The proposed cloud task scheduling method is composed of two logical components: distributed learning management (learning distribution and computing environments.