Self-Constrained Resource Allocation Procedures for Parallel Task Graph Scheduling on Shared Computing Grids

Tchimou N'Takpé 1 Frédéric Suter 1, 2, 3
1 ALGORILLE - Algorithms for the Grid
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 GRAIL - Grid Research and Innovation Laboratory
3 REMAP - Regularity and massive parallel computing
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : Two of the main characteristics of computation grids are their heterogeneity and the sharing of resources between different users. This is the cost of the tremendous computing power offered by such platforms. Scheduling several applications concurrently in such an environment is thus challenging. In this paper we propose a first step towards the scheduling of multiple parallel task graphs~(PTG), a class of applications that can benefit of large and powerful platforms, by focusing on the allocation process. We consider the application of a resource constraint on the schedule and determine the number of processors allocated to the different tasks of a PTG while respecting that constraint. We present two different allocation procedures and validate them in simulation over a wide range of scenarios with regard to their respect of the resource constraint and their impact on the completion time of the scheduled applications. We find that our procedures provide a guarantee on the resource usage for a low cost in terms of execution time.
Keywords : Scheduling Grid computing PTG Allocation
Type de document :
Communication dans un congrès
19th IASTED International Conference on Parallel and Distributed Computing and Systems - PDCS 2007, Nov 2007, Cambridge, Massachusetts, United States. 2007
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Dernière modification le : samedi 21 avril 2018 - 01:27:10
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Tchimou N'Takpé, Frédéric Suter. Self-Constrained Resource Allocation Procedures for Parallel Task Graph Scheduling on Shared Computing Grids. 19th IASTED International Conference on Parallel and Distributed Computing and Systems - PDCS 2007, Nov 2007, Cambridge, Massachusetts, United States. 2007. 〈inria-00179735〉

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