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

by De Oliveira, Daniel C. M.

Type Book

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Title Statements

Full title :Data-intensive workflow management : for clouds and data-intensive and scalable computing environments / Daniel C.M. de Oliveira, Ji Liu, Esther Pacitti.

Summary

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.

Edition Statements

Publication Date:© 2019

Publication Statements

Publisher(s):Morgan & Claypool publ
Place of Publication:San Rafael, Calif
Language:eng
Publication Date:© 2019

The Physical Object

Description:1 online resource (161 p. :) : ill.

Edition Identifiers

ISBN:1681735571, 1681735598, 9781681735573, 9781681735597
oclc:on1134640528
LC Classification:QA76.585
Dewey Decimal:004.67/82

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

2. Background knowledge -- 2.1. Key concepts -- 2.2. Distributed environments used for executing workflows -- 2.3. Conclusion

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

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

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

Subjects

Bases de données Bases de données -- Gestion Cloud computing Database management Flux de travail Flux de travail -- Informatique Gestion des données (systèmes d'informa… Infonuagique Informatique dans les nuages Workflow -- Data processing