Multi-Agent DRL-Based Large-Scale Heterogeneous Task Offloading for Dynamic IoT Systems
Xiao He, Shanchen Pang, Haiyuan Gui, Kuijie Zhang, Nuanlai Wang, Xue Zhai
Abstract
In dynamic IoT system, the device may generate multiple heterogeneous computational tasks, that require CPU and GPU co-processing, in each period. Furthermore, different heterogeneous computing tasks have specific requirements for GPU resource types. Realizing real-time scheduling and processing of large-scale hybrid computing tasks with high heterogeneity and dense quantity has become an urgent problem. First, we propose a cloud-based task processing framework that uses multi-level feedback queues to ensure the fairness of large-scale task parallel computing. Second, we decoupled the original problem into a series of mixed-integer nonlinear programming problems using Lyapunov optimization, aiming to reduce the solution complexity of the real-time scheduling problem. Finally, we propose a multi-agent reinforcement learning algorithm, employing long and short-term memory networks with parameter resetting, to generate task offloading decisions in near real-time based on partially knowable future information. Through extensive simulation experiments, we have demonstrated that our algorithm can reduce the average task processing time by approximately 19.95% and enhance the task processing capability of the IoT system by roughly 12.43%, especially in large-scale hybrid computing task systems.