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Journal of the ACM (JACM)
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Journal of the ACM (JACM)
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Cluster Computing
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IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
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HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
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HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
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HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
Real-Time System Design and Analysis
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Deadline Scheduling with Priority for Client-Server Systems on the Grid
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
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Journal of Parallel and Distributed Computing
A general distributed scalable peer to peer scheduler for mixed tasks in grids
HiPC'07 Proceedings of the 14th international conference on High performance computing
Dynamic Job-Clustering with Different Computing Priorities for Computational Resource Allocation
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
QoS and preemption aware scheduling in federated and virtualized Grid computing environments
Journal of Parallel and Distributed Computing
A grid broker pricing mechanism for temporal and budget guarantees
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
Energy-efficient deadline scheduling for heterogeneous systems
Journal of Parallel and Distributed Computing
The ISQoS grid broker for temporal and budget guarantees
GECON'12 Proceedings of the 9th international conference on Economics of Grids, Clouds, Systems, and Services
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We consider non-preemptively scheduling a bag of independent mixed tasks (hard, firm and soft) in computational grids. Based upon task type, we construct a novel generalized distributed scheduler (GDS) for scheduling tasks with different priorities and deadlines. GDS is scalable and does not require knowledge of the global state of the system. It is composed of several phases: a multiple attribute ranking phase, a shuffling phase, and a task-resource matched peer to peer dispatching phase. Results of exhaustive simulation demonstrate that with respect to the number of high-priority tasks meeting deadlines, GDS outperforms existing approaches by 10%-25% without degrading schedulability of other tasks. Indeed, with respect to the total number of schedulable tasks meeting deadlines, GDS is slightly better. Thus, GDS not only maximizes the number of mission-critical tasks meeting deadlines, but it does so without degrading the overall performance. The results have been further confirmed by examining each component phase of GDS. Given that fully known global information is time intensive to obtain, the performance of GDS is significant. GDS is highly scalable both in terms of processors and number of tasks-indeed it provides superior performance over existing algorithms as the number of tasks increase. Also, GDS incorporates a shuffle phase that moves hard tasks ahead improving their temporal fault tolerance. Furthermore, since GDS can handle mixed task types, it paves the way to open the grid to make it amenable for commercialization. The complexity of GDS is O(n^2m) where n is the number of tasks and m the number of machines.