Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Grid resource management: state of the art and future trends
Grid resource management: state of the art and future trends
DI-GRUBER: A Distributed Approach to Grid Resource Brokering
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Multicriteria Optimization
Software—Practice & Experience
Using evolutionary computation and local search to solve multi-objective flexible job shop problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Adaptive parallel job scheduling with resource admissible allocation on two-level hierarchical grids
Future Generation Computer Systems
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Grid computing emerges as an infrastructure for large-scale data processing, resource sharing, and scientific computing. Job scheduling at the Grid level is challenging in that Grid schedulers do not have control over the computing resources across multiple domains. This makes many traditional algorithms developed on parallel systems not suitable in the Grid case. In this paper we propose a Grid scheduling algorithm using multi-attribute utility theory and multiobjective optimization. It attempts to make optimal decisions based on the available set of objectives. By comparing to a deadline-and-budget algorithm with three objectives, we show that the proposed Multi-Objective Optimization (MOO) scheduling algorithm is capable of obtaining a broader set of non-dominated solutions, and can obtain solutions of higher quality, that is proximity to the Pareto front of optimal solutions.