000 04289nam a22006135i 4500
001 978-981-16-9690-9
003 DE-He213
005 20240423125443.0
007 cr nn 008mamaa
008 220517s2022 si | s |||| 0|eng d
020 _a9789811696909
_9978-981-16-9690-9
024 7 _a10.1007/978-981-16-9690-9
_2doi
050 4 _aTK7885-7895
050 4 _aTK5105.5-5105.9
072 7 _aUK
_2bicssc
072 7 _aCOM067000
_2bisacsh
072 7 _aUK
_2thema
082 0 4 _a621.39
_223
082 0 4 _a004.6
_223
100 1 _aTang, Guoming.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aGreenEdge: New Perspectives to Energy Management and Supply in Mobile Edge Computing
_h[electronic resource] /
_cby Guoming Tang, Deke Guo, Kui Wu.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aX, 114 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
505 0 _a1 Introduction -- 2 Investigating Low-Battery Anxiety of Mobile Users -- 3 User Energy and LBA Aware Mobile Video Streaming -- 4 Optimal Backup Power Allocation for 5G Base Stations -- 5 Reusing Backup Batteries for Power Demand Reshaping in 5G -- 6 Software-Defined Power Supply to Geo-Distributed Edge DCs -- 7 Conclusions and Future Work.
520 _aThe 5G technology has been commercialized worldwide and is expected to provide superior performance with enhanced mobile broadband, ultra-low latency transmission, and massive IoT connections. Meanwhile, the edge computing paradigm gets popular to provide distributed computing and storage resources in proximity to the users. As edge services and applications prosper, 5G and edge computing will be tightly coupled and continuously promote each other forward. Embracing this trend, however, mobile users, infrastructure providers, and service providers are all faced with the energy dilemma. On the user side, battery-powered mobile devices are much constrained by battery life, whereas mobile platforms and apps nowadays are usually power-hungry. At the infrastructure and service provider side, the energy cost of edge facilities accounts for a large proportion of operating expenses and has become a huge burden. This book provides a collection of most recent attempts to tackle the energy issues in mobile edge computing from new and promising perspectives. For example, the book investigates the pervasive low-battery anxiety among modern mobile users and quantifies the anxiety degree and likely behavior concerning the battery status. Based on the quantified model, a low-power video streaming solution is developed accordingly to save mobile devices' energy and alleviate users' low-battery anxiety. In addition to energy management for mobile users, the book also looks into potential opportunities to energy cost saving and carbon emission reduction at edge facilities, particularly the 5G base stations and geo-distributed edge datacenters.
650 0 _aComputer engineering.
650 0 _aComputer networks .
650 0 _aComputer science.
650 0 _aSocial sciences
_xData processing.
650 0 _aApplication software.
650 1 4 _aComputer Engineering and Networks.
650 2 4 _aTheory and Algorithms for Application Domains.
650 2 4 _aComputer Application in Social and Behavioral Sciences.
650 2 4 _aComputer and Information Systems Applications.
700 1 _aGuo, Deke.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aWu, Kui.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811696893
776 0 8 _iPrinted edition:
_z9789811696916
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-981-16-9690-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178114
_d178114