A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Abstract

Recent innovations in the Internet of Things (IoT) have given rise to IoT applications that require quick response times and low latency. Fog computing has proven to be an effective platform for handling IoT applications. It is a significant challenge to deploy fog computing resources effectively because of the heterogeneity of IoT tasks and their delay sensitivity. To take advantage of idle resources in IoT devices, this paper presents an edge computing concept that offloads edge tasks to nearby IoT devices. The IoT-assisted edge computing should meet two conditions, edge services should exploit the computing resources of IoT devices effectively and edge tasks offloaded to IoT devices do not interfere with local IoT tasks. Two main phases are included in the proposed method: virtualization of edge nodes, and task scheduling based on deep reinforcement learning. The first phase offers a layered edge framework. In the second phase, we applied deep reinforcement learning (DRL) to schedule tasks taking into account the diversity of tasks and the heterogeneity of available resources. According to simulation results, our proposed task scheduling method achieves higher levels of task satisfaction and success than existing methods.