Efficient resource management applied to master-worker applications

E. Heymann; M. A. Senar; E. Luque; M. Livny

doi:10.1016/j.jpdc.2003.10.002

Efficient resource management applied to master-worker applications

E. Heymann, M. A. Senar, E. Luque, M. Livny

Research output: Contribution to journal › Article › Research › peer-review

6 Citations (Scopus)

Abstract

We investigate the problem arising in scheduling parallel applications that follow a master-worker paradigm in order to maximize both resource efficiency and application performance. Based on the results obtained in a previous simulation study, we have derived a self-adjusting strategy that can be used to dynamically adjust the number of processors allocated to the application. The effectiveness of the proposed strategy has been assessed in two different scenarios: first, we implemented and tested this strategy on a cluster of homogeneous workstations. Secondly, we extended the self-adjusting strategy to be applied on heterogeneous clusters. We assessed the effectiveness of our strategy using an image-thinning application as a practical example of master-worker application. © 2003 Elsevier Inc. All rights reserved.

Original language	English
Pages (from-to)	767-773
Journal	Journal of Parallel and Distributed Computing
Volume	64
Issue number	6
DOIs	https://doi.org/10.1016/j.jpdc.2003.10.002
Publication status	Published - 1 Jan 2004

Keywords

Image thinning
Master-worker applications
Non-dedicated clusters
Resource management
Scheduling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jpdc.2003.10.002

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title = "Efficient resource management applied to master-worker applications",

abstract = "We investigate the problem arising in scheduling parallel applications that follow a master-worker paradigm in order to maximize both resource efficiency and application performance. Based on the results obtained in a previous simulation study, we have derived a self-adjusting strategy that can be used to dynamically adjust the number of processors allocated to the application. The effectiveness of the proposed strategy has been assessed in two different scenarios: first, we implemented and tested this strategy on a cluster of homogeneous workstations. Secondly, we extended the self-adjusting strategy to be applied on heterogeneous clusters. We assessed the effectiveness of our strategy using an image-thinning application as a practical example of master-worker application. {\textcopyright} 2003 Elsevier Inc. All rights reserved.",

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AU - Senar, M. A.

AU - Luque, E.

AU - Livny, M.

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N2 - We investigate the problem arising in scheduling parallel applications that follow a master-worker paradigm in order to maximize both resource efficiency and application performance. Based on the results obtained in a previous simulation study, we have derived a self-adjusting strategy that can be used to dynamically adjust the number of processors allocated to the application. The effectiveness of the proposed strategy has been assessed in two different scenarios: first, we implemented and tested this strategy on a cluster of homogeneous workstations. Secondly, we extended the self-adjusting strategy to be applied on heterogeneous clusters. We assessed the effectiveness of our strategy using an image-thinning application as a practical example of master-worker application. © 2003 Elsevier Inc. All rights reserved.

AB - We investigate the problem arising in scheduling parallel applications that follow a master-worker paradigm in order to maximize both resource efficiency and application performance. Based on the results obtained in a previous simulation study, we have derived a self-adjusting strategy that can be used to dynamically adjust the number of processors allocated to the application. The effectiveness of the proposed strategy has been assessed in two different scenarios: first, we implemented and tested this strategy on a cluster of homogeneous workstations. Secondly, we extended the self-adjusting strategy to be applied on heterogeneous clusters. We assessed the effectiveness of our strategy using an image-thinning application as a practical example of master-worker application. © 2003 Elsevier Inc. All rights reserved.

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KW - Non-dedicated clusters

KW - Resource management

KW - Scheduling

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DO - 10.1016/j.jpdc.2003.10.002

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EP - 773

JO - Journal of Parallel and Distributed Computing

JF - Journal of Parallel and Distributed Computing

IS - 6

ER -

Efficient resource management applied to master-worker applications

Abstract

Keywords

UN SDGs

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