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| 001 | 978-3-319-73767-6 | ||
| 003 | DE-He213 | ||
| 005 | 20220801221232.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 180131s2018 sz | s |||| 0|eng d | ||
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_a9783319737676 _9978-3-319-73767-6 |
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_a10.1007/978-3-319-73767-6 _2doi |
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_aModeling and Simulation in HPC and Cloud Systems _h[electronic resource] / _cedited by Joanna Kołodziej, Florin Pop, Ciprian Dobre. |
| 250 | _a1st ed. 2018. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
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| 300 |
_aXX, 155 p. 35 illus., 23 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aStudies in Big Data, _x2197-6511 ; _v36 |
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| 505 | 0 | _aEvaluating 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 | _aThis 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. . | ||
| 650 | 0 |
_aComputational intelligence. _97716 |
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_aBig data. _94174 |
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_aComputational Intelligence. _97716 |
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_aBig Data. _94174 |
| 700 | 1 |
_aKołodziej, Joanna. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _954862 |
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| 700 | 1 |
_aPop, Florin. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _954863 |
|
| 700 | 1 |
_aDobre, Ciprian. _eeditor. _0(orcid)0000-0003-4638-7725 _1https://orcid.org/0000-0003-4638-7725 _4edt _4http://id.loc.gov/vocabulary/relators/edt _954864 |
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_aSpringerLink (Online service) _954865 |
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| 773 | 0 | _tSpringer Nature eBook | |
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_iPrinted edition: _z9783319737669 |
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_iPrinted edition: _z9783319737683 |
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_iPrinted edition: _z9783319892580 |
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_aStudies in Big Data, _x2197-6511 ; _v36 _954866 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-73767-6 |
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