國立虎尾科技大學 |

Data-intensive workflow management : = for clouds and data-intensive and scalable computing environments /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data-intensive workflow management :/ Daniel C.M. de Oliveira, Ji Liu, Esther Pacitti.
其他題名:	for clouds and data-intensive and scalable computing environments /
作者:	De Oliveira, Daniel C. M.,
其他作者:	Liu, Ji,
面頁冊數:	1 PDF (xvii, 161 pages) :illustrations. :
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Cloud computing. -
電子資源:	https://doi.org/10.2200/S00915ED1V01Y201904DTM060
電子資源:	https://ieeexplore.ieee.org/servlet/opac?bknumber=8715841
ISBN:	9781681735580

Data-intensive workflow management : = for clouds and data-intensive and scalable computing environments /
De Oliveira, Daniel C. M.,

Data-intensive workflow management :for clouds and data-intensive and scalable computing environments /Daniel C.M. de Oliveira, Ji Liu, Esther Pacitti. - 1 PDF (xvii, 161 pages) :illustrations. - Synthesis lectures on data management,#602153-5426 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 133-160).

1. Overview -- 1.1. Motivating examples -- 1.2. The life cycle of cloud and disc workflows -- 1.3. Structure of the book

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astronomy, and engineering. Such workflows usually present a considerable number of activities and activations (i.e., tasks associated with activities) and may need a long time for execution. Due to the continuous need to store and process data efficiently (making them data-intensive workflows), high-performance computing environments allied to parallelization techniques are used to run these workflows. At the beginning of the 2010s, cloud technologies emerged as a promising environment to run scientific workflows. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines. More recently, Data-Intensive Scalable Computing (DISC) frameworks (e.g., Apache Spark and Hadoop) and environments emerged and are being used to execute data-intensive workflows. DISC environments are composed of processors and disks in large-commodity computing clusters connected using high-speed communications switches and networks. The main advantage of DISC frameworks is that they support and grant efficient in-memory data management for large-scale applications, such as data-intensive workflows. However, the execution of workflows in cloud and DISC environments raise many challenges such as scheduling workflow activities and activations, managing produced data, collecting provenance data, etc. Several existing approaches deal with the challenges mentioned earlier. This way, there is a real need for understanding how to manage these workflows and various big data platforms that have been developed and introduced. As such, this book can help researchers understand how linking workflow management with Data-Intensive Scalable Computing can help in understanding and analyzing scientific big data. In this book, we aim to identify and distill the body of work on workflow management in clouds and DISC environments. We start by discussing the basic principles of data-intensive scientific workflows. Next, we present two workflows that are executed in a single site and multi-site clouds taking advantage of provenance. Afterward, we go towards workflow management in DISC environments, and we present, in detail, solutions that enable the optimized execution of the workflow using frameworks such as Apache Spark and its extensions.

Mode of access: World Wide Web.

ISBN: 9781681735580

Standard No.: 10.2200/S00915ED1V01Y201904DTM060doiSubjects--Topical Terms:

716205
Cloud computing.
Subjects--Index Terms:

scientific workflows

LC Class. No.: QA76.585 / .D426 2019eb

Dewey Class. No.: 004.67/82

Data-intensive workflow management : = for clouds and data-intensive and scalable computing environments /
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245 1 0 $a Data-intensive workflow management : $b for clouds and data-intensive and scalable computing environments / $c Daniel C.M. de Oliveira, Ji Liu, Esther Pacitti.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c [2019]
300 $a 1 PDF (xvii, 161 pages) : $b illustrations.
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505 0 $a 1. Overview -- 1.1. Motivating examples -- 1.2. The life cycle of cloud and disc workflows -- 1.3. Structure of the book
505 8 $a 2. Background knowledge -- 2.1. Key concepts -- 2.2. Distributed environments used for executing workflows -- 2.3. Conclusion
505 8 $a 3. Workflow execution in a single-site cloud -- 3.1. Bibliographic and historical notes -- 3.2. Multi-objective cost model -- 3.3. Single-site virtual machine provisioning (SSVP) -- 3.4. Sgreedy scheduling algorithm -- 3.5. Evaluating SSVP and SGreedy -- 3.6. Conclusion
505 8 $a 4. Workflow execution in a multi-site cloud -- 4.1. Overview of workflow execution in a multi-site cloud -- 4.2. Fine-grained workflow execution -- 4.3. Coarse-grained workflow execution with multiple objectives -- 4.4. Conclusion
505 8 $a 5. Workflow execution in disc environments -- 5.1. Bibliographic and historical notes -- 5.2. Fine tuning of spark parameters -- 5.3. Provenance management in Apache Spark -- 5.4. Scheduling Spark workflows in DISC environments -- 5.5. Conclusion -- 6. Conclusion.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 3 $a Workflows may be defined as abstractions used to model the coherent flow of activities in the context of an in silico scientific experiment. They are employed in many domains of science such as bioinformatics, astronomy, and engineering. Such workflows usually present a considerable number of activities and activations (i.e., tasks associated with activities) and may need a long time for execution. Due to the continuous need to store and process data efficiently (making them data-intensive workflows), high-performance computing environments allied to parallelization techniques are used to run these workflows. At the beginning of the 2010s, cloud technologies emerged as a promising environment to run scientific workflows. By using clouds, scientists have expanded beyond single parallel computers to hundreds or even thousands of virtual machines. More recently, Data-Intensive Scalable Computing (DISC) frameworks (e.g., Apache Spark and Hadoop) and environments emerged and are being used to execute data-intensive workflows. DISC environments are composed of processors and disks in large-commodity computing clusters connected using high-speed communications switches and networks. The main advantage of DISC frameworks is that they support and grant efficient in-memory data management for large-scale applications, such as data-intensive workflows. However, the execution of workflows in cloud and DISC environments raise many challenges such as scheduling workflow activities and activations, managing produced data, collecting provenance data, etc. Several existing approaches deal with the challenges mentioned earlier. This way, there is a real need for understanding how to manage these workflows and various big data platforms that have been developed and introduced. As such, this book can help researchers understand how linking workflow management with Data-Intensive Scalable Computing can help in understanding and analyzing scientific big data. In this book, we aim to identify and distill the body of work on workflow management in clouds and DISC environments. We start by discussing the basic principles of data-intensive scientific workflows. Next, we present two workflows that are executed in a single site and multi-site clouds taking advantage of provenance. Afterward, we go towards workflow management in DISC environments, and we present, in detail, solutions that enable the optimized execution of the workflow using frameworks such as Apache Spark and its extensions.
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