000 | 06101nam a2200685 i 4500 | ||
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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 |
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020 |
_z9781681735597 _qhardcover |
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020 |
_z9781681735573 _qpaperback |
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024 | 7 |
_a10.2200/S00915ED1V01Y201904DTM060 _2doi |
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035 | _a(CaBNVSL)thg00979011 | ||
035 | _a(OCoLC)1102007651 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aQA76.585 _b.D426 2019eb |
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082 | 0 | 4 |
_a004.67/82 _223 |
100 | 1 |
_aDe Oliveira, Daniel C. M., _eauthor. |
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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] |
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300 |
_a1 PDF (xvii, 161 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 |
_aSynthesis lectures on data management, _x2153-5426 ; _v#60 |
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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. |
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700 | 1 |
_aPacitti, Esther, _eauthor. |
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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 |