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Adaptive performance control for distributed scientific coupled models

Published: 17 June 2007 Publication History

Abstract

The PerCo performance control framework is capable of managing the distributed execution of scientific coupled models using migration, for example, in response to changes in an execution environment. PerCo monitors execution times and reacts according to an adaptive performance control strategy whenever serious changes of behaviour occur. A computationally cheap technique is used per model to smooth the series of monitored execution times and to provide a short-term forecast for future execution times on currently assigned resources. Where this short-term forecast fails to be achieved, the system analyses whether migration would improve matters. For models that are candidates for migration, more accurate but computationally expensive techniques are used to form a longer-term prediction of future execution times on various candidate resources. Based on the predicted gain, a migration decision is made taking account of the expected cost of migration. Experimental results for small real scientific coupled models show that the performance control strategy behaves effectively in scenarios in which the ambient load is varied during execution.

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Cited By

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  • (2011)Strategies for Rescheduling Tightly-Coupled Parallel Applications in Multi-Cluster GridsJournal of Grid Computing10.1007/s10723-010-9170-z9:3(379-403)Online publication date: 1-Sep-2011
  • (2009)Adaptive execution of jobs in computational grid environmentJournal of Computer Science and Technology10.1007/s11390-009-9267-724:5(925-938)Online publication date: 1-Sep-2009
  • (2009)An Adaptive Execution Scheme for Achieving Guaranteed Performance in Computational GridsJournal of Grid Computing10.1007/s10723-009-9120-98:1(109-131)Online publication date: 28-May-2009

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cover image ACM Conferences
ICS '07: Proceedings of the 21st annual international conference on Supercomputing
June 2007
315 pages
ISBN:9781595937681
DOI:10.1145/1274971
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 17 June 2007

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Author Tags

  1. PerCo
  2. adaptivity
  3. coupled models
  4. grid
  5. performance control

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ICS07
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ICS07: International Conference on Supercomputing
June 17 - 21, 2007
Washington, Seattle

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Overall Acceptance Rate 629 of 2,180 submissions, 29%

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Cited By

View all
  • (2011)Strategies for Rescheduling Tightly-Coupled Parallel Applications in Multi-Cluster GridsJournal of Grid Computing10.1007/s10723-010-9170-z9:3(379-403)Online publication date: 1-Sep-2011
  • (2009)Adaptive execution of jobs in computational grid environmentJournal of Computer Science and Technology10.1007/s11390-009-9267-724:5(925-938)Online publication date: 1-Sep-2009
  • (2009)An Adaptive Execution Scheme for Achieving Guaranteed Performance in Computational GridsJournal of Grid Computing10.1007/s10723-009-9120-98:1(109-131)Online publication date: 28-May-2009

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