Decentralised grid scheduling approach based on multi-agent reinforcement learning and gossip mechanism
| Autor: | Jun Wu, Xin Xu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
multi-agent systems
learning (artificial intelligence) resource allocation scheduling grid computing decentralised grid scheduling approach multiagent reinforcement learning gossip mechanism resource allocation approaches decentralised job scheduling timely model information autonomous coordination GRL method decentralised scheduling architecture GRL-based schedulers grid job scheduling gossip-based reinforcement learning method Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765 |
| Zdroj: | CAAI Transactions on Intelligence Technology (2018) |
| Druh dokumentu: | article |
| ISSN: | 2468-2322 |
| DOI: | 10.1049/trit.2018.0001 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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