Learning automata with changing number of actions
IEEE Transactions on Systems, Man and Cybernetics
Learning automata: an introduction
Learning automata: an introduction
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Experiments with Scheduling Using Simulated Annealing in a Grid Environment
GRID '02 Proceedings of the Third International Workshop on Grid Computing
Sub optimal scheduling in a grid using genetic algorithms
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
A Routing Load Balancing Policy for Grid Computing Environments
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 01
An ant algorithm for balanced job scheduling in grids
Future Generation Computer Systems
An ACO Inspired Strategy to Improve Jobs Scheduling in a Grid Environment
ICA3PP '08 Proceedings of the 8th international conference on Algorithms and Architectures for Parallel Processing
A GA(TS) Hybrid Algorithm for Scheduling in Computational Grids
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Adaptive grid job scheduling with genetic algorithms
Future Generation Computer Systems
The impact of data replication on job scheduling performance in the Data Grid
Future Generation Computer Systems
Computational models and heuristic methods for Grid scheduling problems
Future Generation Computer Systems
Scheduling Jobs on Grid Processors
Algorithmica
Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm
Future Generation Computer Systems
Future Generation Computer Systems
On-line hierarchical job scheduling on grids with admissible allocation
Journal of Scheduling
A novel multi-agent reinforcement learning approach for job scheduling in Grid computing
Future Generation Computer Systems
Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids
Journal of Grid Computing
Finding minimum weight connected dominating set in stochastic graph based on learning automata
Information Sciences: an International Journal
An adaptive backbone formation algorithm for wireless sensor networks
Computer Communications
Mobility prediction in mobile wireless networks
Journal of Network and Computer Applications
A distributed resource discovery algorithm for P2P grids
Journal of Network and Computer Applications
An adaptive learning automata-based ranking function discovery algorithm
Journal of Intelligent Information Systems
An adaptive learning to rank algorithm: Learning automata approach
Decision Support Systems
LAAP: A Learning Automata-based Adaptive Polling Scheme for Clustered Wireless Ad-Hoc Networks
Wireless Personal Communications: An International Journal
Degree constrained minimum spanning tree problem: a learning automata approach
The Journal of Supercomputing
An energy-efficient topology construction algorithm for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Mobility-Based Backbone Formation in Wireless Mobile Ad-hoc Networks
Wireless Personal Communications: An International Journal
Energy-efficient backbone formation in wireless sensor networks
Computers and Electrical Engineering
Hi-index | 0.00 |
Job scheduling is one of the most challenging issues in Grid resource management that strongly affects the performance of the whole Grid environment. The major drawback of the existing Grid scheduling algorithms is that they are unable to adapt with the dynamicity of the resources and the network conditions. Furthermore, the network model that is used for resource information aggregation in most scheduling methods is centralized or semi-centralized. Therefore, these methods do not scale well as Grid size grows and do not perform well as the environmental conditions change with time. This paper proposes a learning automata-based job scheduling algorithm for Grids. In this method, the workload that is placed on each Grid node is proportional to its computational capacity and varies with time according to the Grid constraints. The performance of the proposed algorithm is evaluated through conducting several simulation experiments under different Grid scenarios. The obtained results are compared with those of several existing methods. Numerical results confirm the superiority of the proposed algorithm over the others in terms of makespan, flowtime, and load balancing.