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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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Main Authors: Ahmad, Zulfiqar, Jehangiri, Ali Imran, Ala’anzy, Mohammed Alaa, Othman, Mohamed, Umar, Arif Iqbal
Format: Article
Language:English
Published: Multidisciplinary Digital Publishing Institute 2021
Online Access:http://psasir.upm.edu.my/id/eprint/97315/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/97315/
https://www.mdpi.com/1424-8220/21/21/7238
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spelling my.upm.eprints.973152022-09-05T08:50:56Z http://psasir.upm.edu.my/id/eprint/97315/ Fault-tolerant and data-intensive resource scheduling and management for scientific applications in cloud computing Ahmad, Zulfiqar Jehangiri, Ali Imran Ala’anzy, Mohammed Alaa Othman, Mohamed Umar, Arif Iqbal 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. Multidisciplinary Digital Publishing Institute 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/97315/1/ABSTRACT.pdf Ahmad, Zulfiqar and Jehangiri, Ali Imran and Ala’anzy, Mohammed Alaa and Othman, Mohamed and Umar, Arif Iqbal (2021) Fault-tolerant and data-intensive resource scheduling and management for scientific applications in cloud computing. Sensors, 21 (21). pp. 1-19. ISSN 1424-8220 https://www.mdpi.com/1424-8220/21/21/7238 10.3390/s21217238
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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 Ahmad, Zulfiqar
Jehangiri, Ali Imran
Ala’anzy, Mohammed Alaa
Othman, Mohamed
Umar, Arif Iqbal
spellingShingle Ahmad, Zulfiqar
Jehangiri, Ali Imran
Ala’anzy, Mohammed Alaa
Othman, Mohamed
Umar, Arif Iqbal
Fault-tolerant and data-intensive resource scheduling and management for scientific applications in cloud computing
author_facet Ahmad, Zulfiqar
Jehangiri, Ali Imran
Ala’anzy, Mohammed Alaa
Othman, Mohamed
Umar, Arif Iqbal
author_sort Ahmad, Zulfiqar
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 Multidisciplinary Digital Publishing Institute
publishDate 2021
url http://psasir.upm.edu.my/id/eprint/97315/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/97315/
https://www.mdpi.com/1424-8220/21/21/7238
_version_ 1744355323785773056
score 13.222552

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