Scheduling Concurrent Bag-of-Tasks Applications on Heterogeneous Platforms

Scheduling problems are already difficult on traditional parallel machines, and they become extremely challenging on heterogeneous clusters. In this paper, we deal with the problem of scheduling multiple applications, made of collections of independent and identical tasks, on a heterogeneous master-...

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Vydané v:IEEE transactions on computers Ročník 59; číslo 2; s. 202 - 217
Hlavní autori: Benoit, A., Marchal, L., Pineau, J.-F., Robert, Y., Vivien, F.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.02.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Predmet:
Algorithms
Bandwidth
Computational modeling
Computer Science
Computer simulation
Data mining
Data models
Distributed, Parallel, and Cluster Computing
evaluation
Heuristic
Mathematical models
measurement
modeling
On-line systems
Online
online computation
parallelism and concurrency
Platforms
Schedules
Scheduling
Scheduling and task partitioning
simulation of multiple-processor systems
Steady-state
Studies
Tasks
Throughput
evaluation
Measurement
Parallelism and concurrency
Scheduling and task partitioning
modeling
Index Terms
simulation of multiple-processor systems
Online computation
ISSN:0018-9340, 1557-9956
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Popis
Shrnutí:Scheduling problems are already difficult on traditional parallel machines, and they become extremely challenging on heterogeneous clusters. In this paper, we deal with the problem of scheduling multiple applications, made of collections of independent and identical tasks, on a heterogeneous master-worker platform. The applications are submitted online, which means that there is no a priori (static) knowledge of the workload distribution at the beginning of the execution. The objective is to minimize the maximum stretch, i.e., the maximum ratio between the actual time an application has spent in the system and the time this application would have spent if executed alone. On the theoretical side, we design an optimal algorithm for the offline version of the problem (when all release dates and application characteristics are known beforehand). We also introduce a heuristic for the general case of online applications. On the practical side, we have conducted extensive simulations and MPI experiments, showing that we are able to deal with very large problem instances in a few seconds. Also, the solution that we compute totally outperforms classical heuristics from the literature, thereby fully assessing the usefulness of our approach.
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ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2009.117