Open AccessDecentralised grid scheduling approach based on multi‐agent reinforcement learning and gossip mechanism
Author(s) -
Wu Jun,
Xu Xin
Publication year - 2018
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2018.0001
Subject(s) - distributed computing , reinforcement learning , computer science , gossip , scheduling (production processes) , adaptability , scalability , grid , job scheduler , dynamic priority scheduling , artificial intelligence , computer network , engineering , psychology , social psychology , ecology , operations management , geometry , mathematics , quality of service , database , queue , biology
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.