Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A taxonomy and survey of grid resource management systems for distributed computing
Software—Practice & Experience
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
When the Herd Is Smart: Aggregate Behavior in the Selection of Job Request
IEEE Transactions on Parallel and Distributed Systems
Adaptive Computing on the Grid Using AppLeS
IEEE Transactions on Parallel and Distributed Systems
Scheduling Resources in Multi-User, Heterogeneous, Computing Environments with SmartNet
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
A Federated Model for Scheduling in Wide-Area Systems
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
Condor-G: A Computation Management Agent for Multi-Institutional Grids
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
Multi-agent learning and coordination algorithms for distributed dynamic resource allocation
Multi-agent learning and coordination algorithms for distributed dynamic resource allocation
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
A self-organizing flock of Condors
Journal of Parallel and Distributed Computing
Learning the task allocation game
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A reinforcement learning approach to dynamic resource allocation
Engineering Applications of Artificial Intelligence
Markets vs auctions: Approaches to distributed combinatorial resource scheduling
Multiagent and Grid Systems - Smart Grid Technologies & Market Models
An ant algorithm for balanced job scheduling in grids
Future Generation Computer Systems
A new mechanism for resource monitoring in Grid computing
Future Generation Computer Systems
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Model-based simulation and performance evaluation of grid scheduling strategies
Future Generation Computer Systems
Grid broker selection strategies using aggregated resource information
Future Generation Computer Systems
Online resource allocation using decompositional reinforcement learning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
A multi-agent learning approach to online distributed resource allocation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Computational models and heuristic methods for Grid scheduling problems
Future Generation Computer Systems
Grid Scheduling Based on Collaborative Random Early Detection Strategies
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
Value-function reinforcement learning in Markov games
Cognitive Systems Research
PFRF: An adaptive data replication algorithm based on star-topology data grids
Future Generation Computer Systems
Information Sciences: an International Journal
A new approach to the job scheduling problem in computational grids
Cluster Computing
A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling
Future Generation Computer Systems
Extending goal-oriented parallel computer job scheduling policies to heterogeneous systems
The Journal of Supercomputing
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Grid computing utilizes distributed heterogeneous resources to support large-scale or complicated computing tasks, and an appropriate resource scheduling algorithm is fundamentally important for the success of Grid applications. Due to the complex and dynamic properties of Grid environments, traditional model-based methods may result in poor scheduling performance in practice. Scalability and adaptability are among the key objectives of Grid job scheduling. In this paper, a novel multi-agent reinforcement learning method, called ordinal sharing learning (OSL) method, is proposed for job scheduling problems, especially, for realizing load balancing in Grids. The approach circumvents the scalability problem by using an ordinal distributed learning strategy, and realizes multi-agent coordination based on an information-sharing mechanism with limited communication. Simulation results show that the OSL method can achieve the goal of load balancing effectively, and its performance is even comparable to some centralized scheduling algorithm in most cases. The convergence property and adaptability of the proposed method are also illustrated.