000 | 04194nam a22005655i 4500 | ||
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001 | 978-3-031-22155-2 | ||
003 | DE-He213 | ||
005 | 20240423125254.0 | ||
007 | cr nn 008mamaa | ||
008 | 230513s2023 sz | s |||| 0|eng d | ||
020 |
_a9783031221552 _9978-3-031-22155-2 |
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024 | 7 |
_a10.1007/978-3-031-22155-2 _2doi |
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050 | 4 | _aTK5105.5-5105.9 | |
072 | 7 |
_aUKN _2bicssc |
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_aCOM043000 _2bisacsh |
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_aUKN _2thema |
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_a004.6 _223 |
100 | 1 |
_aTaheri, Javid. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aEdge Intelligence _h[electronic resource] : _bFrom Theory to Practice / _cby Javid Taheri, Schahram Dustdar, Albert Zomaya, Shuiguang Deng. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2023. |
|
300 |
_aXIV, 247 p. 57 illus., 40 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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505 | 0 | _a1. Distributed Computing Continuum Systems -- 2. Containerized Edge Computing Platforms -- 3. AI/ML for Service Life Cycle at Edge -- 4. AI/ML for Computation Offloading -- 5. AI/ML Data Pipelines for Edge-Cloud Architectures -- 6. AI/ML on Edge -- 7. AI/ML for Service-Level Objectives. . | |
520 | _aThis graduate-level textbook is ideally suited for lecturing the most relevant topics of Edge Computing and its ties to Artificial Intelligence (AI) and Machine Learning (ML) approaches. It starts from basics and gradually advances, step-by-step, to ways AI/ML concepts can help or benefit from Edge Computing platforms. The book is structured into seven chapters; each comes with its own dedicated set of teaching materials (practical skills, demonstration videos, questions, lab assignments, etc.). Chapter 1 opens the book and comprehensively introduces the concept of distributed computing continuum systems that led to the creation of Edge Computing. Chapter 2 motivates the use of container technologies and how they are used to implement programmable edge computing platforms. Chapter 3 introduces ways to employ AI/ML approaches to optimize service lifecycles at the edge. Chapter 4 goes deeper in the use of AI/ML and introduces ways to optimize spreading computational tasks along edge computing platforms. Chapter 5 introduces AI/ML pipelines to efficiently process generated data on the edge. Chapter 6 introduces ways to implement AI/ML systems on the edge and ways to deal with their training and inferencing procedures considering the limited resources available at the edge-nodes. Chapter 7 motivates the creation of a new orchestrator independent object model to descriptive objects (nodes, applications, etc.) and requirements (SLAs) for underlying edge platforms. To provide hands-on experience to students and step-by-step improve their technical capabilities, seven sets of Tutorials-and-Labs (TaLs) are also designed. Codes and Instructions for each TaL is provided on the book website, and accompanied by videos to facilitate their learning process. | ||
650 | 0 | _aComputer networks . | |
650 | 0 | _aSoftware engineering. | |
650 | 0 | _aMachine learning. | |
650 | 0 |
_aElectronic digital computers _xEvaluation. |
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650 | 1 | 4 | _aComputer Communication Networks. |
650 | 2 | 4 | _aSoftware Engineering. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aSystem Performance and Evaluation. |
700 | 1 |
_aDustdar, Schahram. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
700 | 1 |
_aZomaya, Albert. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
700 | 1 |
_aDeng, Shuiguang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031221545 |
776 | 0 | 8 |
_iPrinted edition: _z9783031221569 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-22155-2 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c176139 _d176139 |