GRID '02 Proceedings of the Third International Workshop on Grid Computing
A computational economy for grid computing and its implementation in the Nimrod-G resource broker
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
On Quality of Service Optimization with Discrete QoS Options
RTAS '99 Proceedings of the Fifth IEEE Real-Time Technology and Applications Symposium
Supporting QoS-Based Discovery in Service-Oriented Grids
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Scalable Resource Allocation for Multi-Processor QoS Optimization
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Integrated Resource Management and Scheduling with Multi-Resource Constraints
RTSS '04 Proceedings of the 25th IEEE International Real-Time Systems Symposium
A Cross-Layer Optimization Framework for Multicast in Multi-hop Wireless Networks
WICON '05 Proceedings of the First International Conference on Wireless Internet
End-to-end quality of service for high-end applications
Computer Communications
IEEE Journal on Selected Areas in Communications
Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids
Journal of Grid Computing
The deployment and evaluation of a bioinformatics grid platform - The HUST_Bio_Grid
Computers and Electrical Engineering
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The paper presents a multi-level scheduling algorithm for global optimization in grid computing. This algorithm provides a global optimization through a cross-layer optimization realized by decomposing the optimization problem in different sub-problems each of them corresponding to one among the grid layers such as application layer, collective layer and fabric layer. The QoS of an abstraction level is a utility function that assigns at every level a different value and that depends on the kind of task that is executed on the grid. The global QoS is given by processing of the utility function values of the three different levels, using the Lagrangian method. Multi-level QoS scheduling algorithm is evaluated in terms of system efficiency and their economic efficiency, respectively. Economic efficiency includes user utility, service provider's revenue and grid global utility. System efficiency includes execution success ratio and resource allocation ratio.