000 04818nam a22005295i 4500
001 978-3-031-01872-5
003 DE-He213
005 20240730163439.0
007 cr nn 008mamaa
008 220601s2019 sz | s |||| 0|eng d
020 _a9783031018725
_9978-3-031-01872-5
024 7 _a10.1007/978-3-031-01872-5
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _ade Oliveira, Daniel C. M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978580
245 1 0 _aData-Intensive Workflow Management
_h[electronic resource] /
_cby Daniel C. M. de Oliveira, Ji Liu, Esther Pacitti.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXVII, 161 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aComputer networks .
_931572
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 1 4 _aComputer Communication Networks.
_978581
650 2 4 _aData Structures and Information Theory.
_931923
700 1 _aLiu, Ji.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978582
700 1 _aPacitti, Esther.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978583
710 2 _aSpringerLink (Online service)
_978584
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000997
776 0 8 _iPrinted edition:
_z9783031007446
776 0 8 _iPrinted edition:
_z9783031030000
830 0 _aSynthesis Lectures on Data Management,
_x2153-5426
_978585
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01872-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c84614
_d84614