Journal of Parallel and Distributed Computing
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
On QoS-Based Scheduling of a Meta-Task with Multiple QoS Demands in Heterogeneous Computing
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Performance Optimization for Data Intensive Grid Applications
AMS '01 Proceedings of the Third Annual International Workshop on Active Middleware Services
User Preference Driven Multiobjective Resource Management in Grid Environments
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Sub Optimal Scheduling in a Grid Using Genetic Algorithms
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Solving large-scale quadratic assignment problems
Solving large-scale quadratic assignment problems
Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Heuristic scheduling for bag-of-tasks applications in combination with QoS in the computational grid
Future Generation Computer Systems - Special issue: Advanced grid technologies
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Meta-schedulers for grid computing based on multi-objective swarm algorithms
Applied Soft Computing
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Job scheduling on computational grids is a key problem in large scale grid-based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate dependable jobs originated from large scale applications on hierarchy based grid computing platforms with heterogeneous resources. In contrast to satisfying multi objectives of different levels, which is NP-hard in most formulations, a set of cooperative multi-objective genetic algorithm (MOGA) is presented. Using this scheme, the application level generates multiple local schedules based on local nodes and objectives to a schedule pool, from which the system level can assemble a set of global schedules according to global objectives. The MOGA scheduling scheme is shown to perform well on the experimental scenario, which shows its flexibility and possible application to more complex job scheduling scenarios with multiple and diverse tasks and nodes.