Modeling and Simulation in HPC and Cloud Systems (Record no. 79444)

000 -LEADER
fixed length control field 04630nam a22005415i 4500
001 - CONTROL NUMBER
control field 978-3-319-73767-6
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801221232.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180131s2018 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319737676
-- 978-3-319-73767-6
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
245 10 - TITLE STATEMENT
Title Modeling and Simulation in HPC and Cloud Systems
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2018.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XX, 155 p. 35 illus., 23 illus. in color.
490 1# - SERIES STATEMENT
Series statement Studies in Big Data,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Evaluating Distributed Systems and Applications through Accurate Models and Simulations -- Scheduling Data-Intensive Workloads in Large-Scale Distributed Systems: Trends and Challenges -- Design Patterns and Algorithmic Skeletons: A Brief Concordance -- Evaluation of Cloud Systems -- Science Gateways in HPC: Usability meets Efficiency and Effectiveness -- MobEmu: A Framework to Support Decentralized Ad-Hoc Networking -- Virtualisation Model For Processing of the Sensitive Mobile Data -- Analysis of selected cryptographic services for processing batch tasks in Cloud Computing systems.
520 ## - SUMMARY, ETC.
Summary, etc This book consists of eight chapters, five of which provide a summary of the tutorials and workshops organised as part of the cHiPSet Summer School: High-Performance Modelling and Simulation for Big Data Applications Cost Action on “New Trends in Modelling and Simulation in HPC Systems,” which was held in Bucharest (Romania) on September 21–23, 2016. As such it offers a solid foundation for the development of new-generation data-intensive intelligent systems. Modelling and simulation (MS) in the big data era is widely considered the essential tool in science and engineering to substantiate the prediction and analysis of complex systems and natural phenomena. MS offers suitable abstractions to manage the complexity of analysing big data in various scientific and engineering domains. Unfortunately, big data problems are not always easily amenable to efficient MS over HPC (high performance computing). Further, MS communities may lack the detailed expertise required to exploit the full potential of HPC solutions, and HPC architects may not be fully aware of specific MS requirements. The main goal of the Summer School was to improve the participants’ practical skills and knowledge of the novel HPC-driven models and technologies for big data applications. The trainers, who are also the authors of this book, explained how to design, construct, and utilise the complex MS tools that capture many of the HPC modelling needs, from scalability to fault tolerance and beyond. In the final three chapters, the book presents the first outcomes of the school: new ideas and novel results of the research on security aspects in clouds, first prototypes of the complex virtual models of data in big data streams and a data-intensive computing framework for opportunistic networks. It is a valuable reference resource for those wanting to start working in HPC and big data systems, as well as for advanced researchers and practitioners. .
700 1# - AUTHOR 2
Author 2 Kołodziej, Joanna.
700 1# - AUTHOR 2
Author 2 Pop, Florin.
700 1# - AUTHOR 2
Author 2 Dobre, Ciprian.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-319-73767-6
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Koha item type eBooks
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-- Springer International Publishing :
-- Imprint: Springer,
-- 2018.
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-- text
-- txt
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Big data.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Big Data.
700 1# - AUTHOR 2
-- (orcid)0000-0003-4638-7725
-- https://orcid.org/0000-0003-4638-7725
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-- 2197-6511 ;
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-- ZDB-2-ENG
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