Decentralised grid scheduling approach based on multi‐agent reinforcement learning and gossip mechanism. Issue 1 (29th March 2018)

• Record Type: Journal Article
• Authors: Wu, Jun; Xu, Xin
• Is Part Of: CAAI transactions on intelligence technology. Volume 3:Issue 1(2018)
• Page Start: 8
• Page End: 17
• Publication Date: 2018-03-29
• Subjects: 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; C6170K Knowledge engineering techniques -- C6190G Grid computing; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2018.0001
• Languages: English
• ISSNs: 2468-6557

Abstract

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.