MADDPG-based task offloading and resource pricing in edge collaboration environment

Abstract

With the rapid advancement of fifth-generation communication technologies, the data produced by the Internet of Everything is growing exponentially. As mobile cloud computing struggles to keep up with the demands for massive data processing and low latency, mobile edge computing (MEC) has emerged as a solution. By shifting services from centralized cloud platforms to edge servers located closer to data sources, MEC achieves reduced latency, enhanced computing efficiency, and an improved user experience. This paper introduces a task offloading algorithm designed for a multi-base station cooperative mobile edge environment, addressing the challenges of task offloading and resource pricing. The system architecture includes a macro base station and several micro base stations, strategically deployed in a densely populated mobile device area. Each mobile device serves as an autonomous decision-making unit, offloading tasks to an optimal base station. We model the interactions between base stations and end-users using a Stackelberg game approach, with strategy optimization achieved through a multi-agent deep deterministic policy gradient algorithm. The proposed TO-SG-MADDPG algorithm intelligently coordinates the policies of multiple base stations and end-users by centralized training and distributed execution, resulting in globally optimal task offloading and resource pricing. The results demonstrate that the proposed algorithm not only reduces the task loss rate but also safeguards the interests of all stakeholders.