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008			220601s2019 sz | o |||| 0|eng d
020			_a9783031018725
024	7		_a10.1007/978-3-031-01872-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.585 _b2019 EB
100	1		_aOliveira, Daniel de _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686319
245	1	0	_aData-Intensive Workflow Management _cby Daniel Oliveira, Ji Liu, Esther Pacitti
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XVII, 161 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Management _x2153-5426
505	0		_aPreface -- Acknowledgments -- Overview -- Background Knowledge -- Workflow Execution in a Single-Site Cloud -- Workflow Execution in a Multi-Site Cloud -- Workflow Execution in DISC Environments -- Conclusion -- Bibliography -- Authors' Biographies .
520			_aWorkflows 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.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9666069 _aInformática en la nube
700	1		_aLiu, Ji _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686320
700	1		_aPacitti, Esther _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686186
776	0	8	_iPrinted edition: _z9783031000997
776	0	8	_iPrinted edition: _z9783031007446
776	0	8	_iPrinted edition: _z9783031030000
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01872-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI