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- W2807102723 abstract "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." @default.
- W2807102723 created "2018-06-13" @default.
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- W2807102723 date "2018-03-01" @default.
- W2807102723 modified "2023-10-16" @default.
- W2807102723 title "Decentralised grid scheduling approach based on multi‐agent reinforcement learning and gossip mechanism" @default.
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- W2807102723 doi "https://doi.org/10.1049/trit.2018.0001" @default.
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