000			03031nam a2200361Ia 4500
000			03958nam a22003855i 4500
001			978-3-031-22155-2
003			DE-He213
005			20240319120807.0
007			cr nn 008mamaa
008			230513s2023 sz | s |||| 0|eng d
020			_a9783031221552 _9978-3-031-22155-2
082			_a4.6
100			_aTaheri, Javid. _928911
245			_aEdge Intelligence _cby Javid Taheri, Schahram Dustdar, Albert Zomaya, Shuiguang Deng. _h[electronic resource] :
250			_a1st ed. 2023.
260			_aCham _bSpringer International Publishing _c2023
300			_aXIV, 247 p. 57 illus., 40 illus. in color. _bonline resource.
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			_aComputer Communication Networks. _928912
650			_aComputer networks . _928913
650			_aElectronic digital computers _928914
650			_aMachine learning. _928915
650			_aMachine Learning. _928916
650			_aSoftware engineering. _928917
650			_aSoftware Engineering. _928918
650			_aSystem Performance and Evaluation. _928919
700			_aDeng, Shuiguang. _928920
700			_aDustdar, Schahram. _928921
700			_aZomaya, Albert. _928922
856			_uhttps://doi.org/10.1007/978-3-031-22155-2
942			_cEBK _2ddc
999			_c15003 _d15003