Cai, Z.; Li, X.; Ruiz García, R. (2019). Resource Provisioning for Task-Batch Based Workflows with Deadlines in Public Clouds. IEEE Transactions on Cloud Computing. 7(3):814-826. https://doi.org/10.1109/TCC.2017.2663426
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/157279
Title:
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Resource Provisioning for Task-Batch Based Workflows with Deadlines in Public Clouds
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Author:
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Cai, Zhicheng
Li, Xiaoping
Ruiz García, Rubén
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UPV Unit:
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Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
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Issued date:
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Abstract:
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[EN] To meet the dynamic workload requirements in widespread task-batch based workflow applications, it is important to design algorithms for DAG-based platforms (such as Dryad, Spark and Pegasus) to rent virtual machines ...[+]
[EN] To meet the dynamic workload requirements in widespread task-batch based workflow applications, it is important to design algorithms for DAG-based platforms (such as Dryad, Spark and Pegasus) to rent virtual machines from public clouds dynamically. In terms of depths and functionalities, tasks of different task-batches are merged into task-units. A unit-aware deadline division method is investigated for properly dividing workflow deadlines to task deadlines so as to minimize the utilization of rented intervals. A rule-based task scheduling method is presented for allocating tasks to time slots of rented Virtual Machines (VMs) with a task right shifting operation and a weighted priority composite rule. A Unit-aware Rule-based Heuristic (URH) is proposed for elastically provisioning VMs to task-batch based workflows to minimize the rental cost in DAG-based cloud platforms. Effectiveness of the proposed URH methods is verified by comparing them against two adapted existing algorithms for similar problems on some realistic workflows.
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Subjects:
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Cloud computing
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Workflow scheduling
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Resource provisioning
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Task-batch
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Pricing model
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Copyrigths:
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Reserva de todos los derechos
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Source:
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IEEE Transactions on Cloud Computing. (eissn:
2168-7161
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DOI:
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10.1109/TCC.2017.2663426
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Publisher:
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Institute of Electrical and Electronics Engineers (IEEE)
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Publisher version:
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https://doi.org/10.1109/TCC.2017.2663426
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Project ID:
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info:eu-repo/grantAgreement/NSFC//61572127/
info:eu-repo/grantAgreement/Natural Science Foundation of Jiangsu Province//BK20160846/
info:eu-repo/grantAgreement/Jiangsu Key Laboratory of Image and Video Understanding for Social Safety//30916014107/
info:eu-repo/grantAgreement/NSFC//61602243/
info:eu-repo/grantAgreement/MINECO//DPI2015-65895-R/ES/OPTIMIZATION OF SCHEDULING PROBLEMS IN CONTAINER YARDS/
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Thanks:
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The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No.61602243 and 61572127), the Natural Science ...[+]
The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No.61602243 and 61572127), the Natural Science Foundation of Jiangsu Province (Grant No.BK20160846), the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Grant No. 30916014107). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD" (DPI2015-65895-R) financed by FEDER funds.
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Type:
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Artículo
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