Decentralised grid scheduling approach based on multi‐agent reinforcement learning and gossip mechanism

As an important class of resource allocation approaches, decentralised job scheduling in large‐scale grids has to deal with the difficulties in acquiring timely model information and improving performance by autonomous coordination. In this study, a gossip‐based reinforcement learning (GRL) method is proposed for decentralised job scheduling in grids. In the GRL method, a decentralised scheduling architecture based on multi‐agent reinforcement learning is presented to improve the scalability and adaptability of job scheduling. A gossip mechanism is designed to realise autonomous coordination among the decentralised schedulers. Simulation results show that the proposed GRL‐based schedulers can complete the task of grid job scheduling effectively and achieve load balancing efficiently.