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Evaluating replication for parallel jobs: an efficient approach

Many modern software applications rely on parallel job processing to exploit large resource pools available in cloud and grid infrastructures. The response time of a parallel job, made of many subtasks, is determined by the last subtask that finishes. Thus, a single laggard subtask or a failure, req...

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Detalles Bibliográficos
Autores Principales:	Qiu, Zhan, Pérez, Juan F.
Formato:	Artículo (Article)
Lenguaje:	Inglés (English)
Publicado:	IEEE 2015
Materias:	Time factors Reliability Correlation Program processors Computational modeling Absorption Servers
Acceso en línea:	https://repository.urosario.edu.co/handle/10336/27694 https://doi.org/10.1109/TPDS.2015.2496593

id	ir-10336-27694
recordtype	dspace
spelling	ir-10336-276942021-09-23T17:38:12Z Evaluating replication for parallel jobs: an efficient approach Evaluación de la replicación para trabajos paralelos: un enfoque eficiente Qiu, Zhan Pérez, Juan F. Time factors Reliability Correlation Program processors Computational modeling Absorption Servers Many modern software applications rely on parallel job processing to exploit large resource pools available in cloud and grid infrastructures. The response time of a parallel job, made of many subtasks, is determined by the last subtask that finishes. Thus, a single laggard subtask or a failure, requiring re-processing, may increase the response time substantially. To overcome these issues, we explore concurrent replication with canceling. This mechanism executes two job replicas concurrently, and retrieves the result of the first replica that completes, immediately canceling the other one. To analyze this mechanism we propose a stochastic model that considers replication at both job-level and task-level. We find that task-level replication achieves a much higher reliability and shorter response times than job-level replication. We also observe that the impact of replication depends on the system utilization, the subtask reliability, and the correlation among replica failures. Based on the model, we propose a resource-provisioning strategy that determines the minimum number of computing nodes needed to achieve a service-level objective (SLO) defined as a response-time percentile. This strategy is evaluated by considering realistic traffic patterns from a parallel cluster, where task-level replication shows the potential to reduce the resource requirements for tight response-time SLOs. 2015-10-30 2020-08-19T14:43:23Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion ISSN: 1045-9219 EISSN: 1558-2183 https://repository.urosario.edu.co/handle/10336/27694 https://doi.org/10.1109/TPDS.2015.2496593 eng info:eu-repo/semantics/restrictedAccess application/pdf IEEE IEEE Transactions on Parallel and Distributed Systems
institution	EdocUR - Universidad del Rosario
collection	DSpace
language	Inglés (English)
topic	Time factors Reliability Correlation Program processors Computational modeling Absorption Servers
spellingShingle	Time factors Reliability Correlation Program processors Computational modeling Absorption Servers Qiu, Zhan Pérez, Juan F. Evaluating replication for parallel jobs: an efficient approach
description	Many modern software applications rely on parallel job processing to exploit large resource pools available in cloud and grid infrastructures. The response time of a parallel job, made of many subtasks, is determined by the last subtask that finishes. Thus, a single laggard subtask or a failure, requiring re-processing, may increase the response time substantially. To overcome these issues, we explore concurrent replication with canceling. This mechanism executes two job replicas concurrently, and retrieves the result of the first replica that completes, immediately canceling the other one. To analyze this mechanism we propose a stochastic model that considers replication at both job-level and task-level. We find that task-level replication achieves a much higher reliability and shorter response times than job-level replication. We also observe that the impact of replication depends on the system utilization, the subtask reliability, and the correlation among replica failures. Based on the model, we propose a resource-provisioning strategy that determines the minimum number of computing nodes needed to achieve a service-level objective (SLO) defined as a response-time percentile. This strategy is evaluated by considering realistic traffic patterns from a parallel cluster, where task-level replication shows the potential to reduce the resource requirements for tight response-time SLOs.
format	Artículo (Article)
author	Qiu, Zhan Pérez, Juan F.
author_facet	Qiu, Zhan Pérez, Juan F.
author_sort	Qiu, Zhan
title	Evaluating replication for parallel jobs: an efficient approach
title_short	Evaluating replication for parallel jobs: an efficient approach
title_full	Evaluating replication for parallel jobs: an efficient approach
title_fullStr	Evaluating replication for parallel jobs: an efficient approach
title_full_unstemmed	Evaluating replication for parallel jobs: an efficient approach
title_sort	evaluating replication for parallel jobs: an efficient approach
publisher	IEEE
publishDate	2015
url	https://repository.urosario.edu.co/handle/10336/27694 https://doi.org/10.1109/TPDS.2015.2496593
_version_	1712098348429737984
score	12,139156

Evaluating replication for parallel jobs: an efficient approach

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