A DRL based Real-time Computing Load Scheduling Method

In this paper, we propose a real-time computing load scheduling method based on deep reinforcement learning (DRL). Firstly, we formulate the problem as a multi-agent competition for limited resources where every agent acts in its own interest. Secondly, we study to design a decentralized algorithm for load scheduling, so that data access servers can independently determine their load scheduling strategy. Finally, a series of simulation results show that our proposal significantly outperforms existing solutions.