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001 978-3-031-22155-2
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020 _a9783031221552
_9978-3-031-22155-2
024 7 _a10.1007/978-3-031-22155-2
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _aTaheri, Javid.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
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