A novel multi-agent reinforcement learning approach for job scheduling in Grid computing

Future Generation Computer Systems

Volumn 27, Issue 5, 2011, Pages 430-439

  Wu, Jun ^a   Xu, Xin ^a   Zhang, Pengcheng ^a   Liu, Chunming ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Coordination; Grid computing; Job scheduling; Load balancing; Multi agent reinforcement learning; Resource allocation

Indexed keywords

CENTRALIZED SCHEDULING; COMPUTING-TASK; CONVERGENCE PROPERTIES; COORDINATION; DISTRIBUTED LEARNING; DYNAMIC PROPERTY; GRID APPLICATIONS; GRID ENVIRONMENTS; HETEROGENEOUS RESOURCES; JOB SCHEDULING; JOB SCHEDULING PROBLEM; KEY OBJECTIVE; LIMITED COMMUNICATION; LOAD BALANCING; MODEL-BASED METHOD; MULTI-AGENT COORDINATIONS; MULTI-AGENT REINFORCEMENT LEARNING; RESOURCE SCHEDULING ALGORITHMS; SCALABILITY PROBLEMS; SCHEDULING PERFORMANCE; SHARING MECHANISM; SIMULATION RESULT;

PARALLEL ARCHITECTURES; REINFORCEMENT LEARNING; RESOURCE ALLOCATION; SCALABILITY; SCHEDULING ALGORITHMS;

GRID COMPUTING;

EID: 79951858262     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2010.10.009     Document Type: Article

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