Knowledge-Defined Edge Computing Networks Assisted Long-Term Optimization of Computation Offloading and Resource Allocation Strategy

With the proliferation of devices connected to the Internet of Things (IoT), the complexity of network management has increased. To intelligently manage large-scale networks, we propose a Knowledge-Defined Edge Computing Networks (KDECN) architecture. Edge Nodes (ENs) deployed in the KDECN architecture are responsible for collecting and preprocessing the relevant information uploaded by User Devices (UDs), and provide computation resources for UDs. Futhermore, since multiple UDs share system computation resources, one computing decision will affect the subsequent decision-making of other UDs. Thus, accurately predicting the demands for UD task requests is a key challenge to maximize long-term execution utility. To this end, we deploy the LSTM-based Task Request Demand Prediction (TRDP) method on the management plane of KDECN architecture to predict the task request quantity of UDs in each future time slot. In order to maximize long-term execution utility of the system, we propose a Deep Reinforcement Learning (DRL)-based Long-term Computation Offloading and computation Resource Allocation (L-CORA) algorithm. Specifically, the proposed L-CORA algorithm makes computing decisions based on the prediction of the offloading task quantity and the personalized demands of UDs to ensure the long-term quality of computing service. Extensive experiments with Shanghai real-world datasets to prove that the KDECN-based L-CORA algorithm effectively improves the average utility of the system.