Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing

Cloud computing is a fully fledged, matured and flexible computing paradigm that provides services to scientific and business applications in a subscription-based environment. Scientific applications such as Montage and CyberShake are organized scientific workflows with data and compute-intensive ta...

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Autores principales:	Zulfiqar Ahmad, Ali Imran Jehangiri, Mohammed Alaa Ala’anzy, Mohamed Othman, Arif Iqbal Umar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	scientific workflows scheduling fault-tolerant Montage clustering Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/733cf09fd21b4c4796bfe5c9a84896d2
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spelling	oai:doaj.org-article:733cf09fd21b4c4796bfe5c9a84896d22021-11-11T19:12:35ZFault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing10.3390/s212172381424-8220https://doaj.org/article/733cf09fd21b4c4796bfe5c9a84896d22021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7238https://doaj.org/toc/1424-8220Cloud computing is a fully fledged, matured and flexible computing paradigm that provides services to scientific and business applications in a subscription-based environment. Scientific applications such as Montage and CyberShake are organized scientific workflows with data and compute-intensive tasks and also have some special characteristics. These characteristics include the tasks of scientific workflows that are executed in terms of integration, disintegration, pipeline, and parallelism, and thus require special attention to task management and data-oriented resource scheduling and management. The tasks executed during pipeline are considered as bottleneck executions, the failure of which result in the wholly futile execution, which requires a fault-tolerant-aware execution. The tasks executed during parallelism require similar instances of cloud resources, and thus, cluster-based execution may upgrade the system performance in terms of make-span and execution cost. Therefore, this research work presents a cluster-based, fault-tolerant and data-intensive (CFD) scheduling for scientific applications in cloud environments. The CFD strategy addresses the data intensiveness of tasks of scientific workflows with cluster-based, fault-tolerant mechanisms. The Montage scientific workflow is considered as a simulation and the results of the CFD strategy were compared with three well-known heuristic scheduling policies: (a) MCT, (b) Max-min, and (c) Min-min. The simulation results showed that the CFD strategy reduced the make-span by 14.28%, 20.37%, and 11.77%, respectively, as compared with the existing three policies. Similarly, the CFD reduces the execution cost by 1.27%, 5.3%, and 2.21%, respectively, as compared with the existing three policies. In case of the CFD strategy, the SLA is not violated with regard to time and cost constraints, whereas it is violated by the existing policies numerous times.Zulfiqar AhmadAli Imran JehangiriMohammed Alaa Ala’anzyMohamed OthmanArif Iqbal UmarMDPI AGarticlescientific workflowsschedulingfault-tolerantMontageclusteringChemical technologyTP1-1185ENSensors, Vol 21, Iss 7238, p 7238 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	scientific workflows scheduling fault-tolerant Montage clustering Chemical technology TP1-1185
spellingShingle	scientific workflows scheduling fault-tolerant Montage clustering Chemical technology TP1-1185 Zulfiqar Ahmad Ali Imran Jehangiri Mohammed Alaa Ala’anzy Mohamed Othman Arif Iqbal Umar Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing
description	Cloud computing is a fully fledged, matured and flexible computing paradigm that provides services to scientific and business applications in a subscription-based environment. Scientific applications such as Montage and CyberShake are organized scientific workflows with data and compute-intensive tasks and also have some special characteristics. These characteristics include the tasks of scientific workflows that are executed in terms of integration, disintegration, pipeline, and parallelism, and thus require special attention to task management and data-oriented resource scheduling and management. The tasks executed during pipeline are considered as bottleneck executions, the failure of which result in the wholly futile execution, which requires a fault-tolerant-aware execution. The tasks executed during parallelism require similar instances of cloud resources, and thus, cluster-based execution may upgrade the system performance in terms of make-span and execution cost. Therefore, this research work presents a cluster-based, fault-tolerant and data-intensive (CFD) scheduling for scientific applications in cloud environments. The CFD strategy addresses the data intensiveness of tasks of scientific workflows with cluster-based, fault-tolerant mechanisms. The Montage scientific workflow is considered as a simulation and the results of the CFD strategy were compared with three well-known heuristic scheduling policies: (a) MCT, (b) Max-min, and (c) Min-min. The simulation results showed that the CFD strategy reduced the make-span by 14.28%, 20.37%, and 11.77%, respectively, as compared with the existing three policies. Similarly, the CFD reduces the execution cost by 1.27%, 5.3%, and 2.21%, respectively, as compared with the existing three policies. In case of the CFD strategy, the SLA is not violated with regard to time and cost constraints, whereas it is violated by the existing policies numerous times.
format	article
author	Zulfiqar Ahmad Ali Imran Jehangiri Mohammed Alaa Ala’anzy Mohamed Othman Arif Iqbal Umar
author_facet	Zulfiqar Ahmad Ali Imran Jehangiri Mohammed Alaa Ala’anzy Mohamed Othman Arif Iqbal Umar
author_sort	Zulfiqar Ahmad
title	Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing
title_short	Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing
title_full	Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing
title_fullStr	Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing
title_full_unstemmed	Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing
title_sort	fault-tolerant and data-intensive resource scheduling and management for scientific applications in cloud computing
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/733cf09fd21b4c4796bfe5c9a84896d2
work_keys_str_mv	AT zulfiqarahmad faulttolerantanddataintensiveresourceschedulingandmanagementforscientificapplicationsincloudcomputing AT aliimranjehangiri faulttolerantanddataintensiveresourceschedulingandmanagementforscientificapplicationsincloudcomputing AT mohammedalaaalaanzy faulttolerantanddataintensiveresourceschedulingandmanagementforscientificapplicationsincloudcomputing AT mohamedothman faulttolerantanddataintensiveresourceschedulingandmanagementforscientificapplicationsincloudcomputing AT arifiqbalumar faulttolerantanddataintensiveresourceschedulingandmanagementforscientificapplicationsincloudcomputing
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Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing

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