Classifier systems and genetic algorithms
Artificial Intelligence
Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Effective Metacomputing using LSF MultiCluster
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
A grid service broker for scheduling distributed data-oriented applications on global grids
MGC '04 Proceedings of the 2nd workshop on Middleware for grid computing
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Development of scheduling strategies with Genetic Fuzzy systems
Applied Soft Computing
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A parallel solution for scheduling of real time applications on grid environments
Future Generation Computer Systems
Evolutionary Fuzzy Scheduler for Grid Computing
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Engineering Applications of Artificial Intelligence
Using particle swam optimization for QoS in ad-hoc multicast
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Computational models and heuristic methods for Grid scheduling problems
Future Generation Computer Systems
IEEE Transactions on Fuzzy Systems
On advantages of scheduling using genetic fuzzy systems
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
Alea: grid scheduling simulation environment
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm
Future Generation Computer Systems
A fuzzy rule-based meta-scheduler with evolutionary learning for grid computing
Engineering Applications of Artificial Intelligence
The importance of complete data sets for job scheduling simulations
JSSPP'10 Proceedings of the 15th international conference on Job scheduling strategies for parallel processing
Knowledge acquisition in fuzzy-rule-based systems with particle-swarm optimization
IEEE Transactions on Fuzzy Systems
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Scheduling parallelizable tasks to minimize make-span and weighted response time
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
In spite of the existence of a large diversity in literature related to scheduling algorithms in computational grids, there are only a few efficiently dealing with the inherent uncertainty and dynamism of resources and applications of these systems. Further, the need to meet both users and providers QoS requirements, such as tardiness or resource utilization, calls for new adaptive scheduling strategies that consider current and future status of the grid. Fuzzy Rule-Based Systems (FRBSs) are knowledge based systems that are recently emerging as an alternative for the development of grid scheduling middleware. Their main strength resides in their adaptability to changes in environment and their ability to model vagueness. However, since their performance strongly depends on the quality of their acquired knowledge, new automatic learning strategies are pursued. In this work, a FRBS meta-scheduler for scheduling jobs in computational grids is suggested which incorporates a novel knowledge acquisition method based on Swarm Intelligence. Simulations results show that the fuzzy meta-scheduler improves six classical queued-based and scheduled-based approaches present in today's production systems and it is able to easily adapt to changes in the grid conditions.