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Books > Computing & IT > Computer communications & networking
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Data-Intensive Workflow Management (Paperback)
Loot Price: R1,747
Discovery Miles 17 470
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Data-Intensive Workflow Management (Paperback)
Series: Synthesis Lectures on Data Management
Expected to ship within 10 - 15 working days
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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.
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