000 06101nam a2200685 i 4500
001 8715841
003 IEEE
005 20200413152932.0
006 m eo d
007 cr bn |||m|||a
008 190529s2019 caua fob 000 0 eng d
020 _a9781681735580
_qelectronic
020 _z9781681735597
_qhardcover
020 _z9781681735573
_qpaperback
024 7 _a10.2200/S00915ED1V01Y201904DTM060
_2doi
035 _a(CaBNVSL)thg00979011
035 _a(OCoLC)1102007651
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQA76.585
_b.D426 2019eb
082 0 4 _a004.67/82
_223
100 1 _aDe Oliveira, Daniel C. M.,
_eauthor.
245 1 0 _aData-intensive workflow management :
_bfor clouds and data-intensive and scalable computing environments /
_cDaniel C.M. de Oliveira, Ji Liu, Esther Pacitti.
264 1 _a[San Rafael, California] :
_bMorgan & Claypool,
_c[2019]
300 _a1 PDF (xvii, 161 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on data management,
_x2153-5426 ;
_v#60
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
504 _aIncludes bibliographical references (pages 133-160).
505 0 _a1. Overview -- 1.1. Motivating examples -- 1.2. The life cycle of cloud and disc workflows -- 1.3. Structure of the book
505 8 _a2. Background knowledge -- 2.1. Key concepts -- 2.2. Distributed environments used for executing workflows -- 2.3. Conclusion
505 8 _a3. 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 _a4. 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 _a5. 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 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _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.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on May 29, 2019).
650 0 _aCloud computing.
650 0 _aDatabase management.
653 _ascientific workflows
653 _acloud computing
653 _aData-Intensive Scalable Computing
653 _adata provenance
653 _aApache Spark
700 1 _aLiu, Ji,
_eauthor.
700 1 _aPacitti, Esther,
_eauthor.
776 0 8 _iPrint version:
_z9781681735597
_z9781681735573
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on data management ;
_v#60.
856 4 0 _3Abstract with links to full text
_uhttps://doi.org/10.2200/S00915ED1V01Y201904DTM060
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=8715841
999 _c562412
_d562412