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001 978-3-031-02382-8
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008 220601s2021 sz | s |||| 0|eng d
020 _a9783031023828
_9978-3-031-02382-8
024 7 _a10.1007/978-3-031-02382-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aPoularakis, Konstantinos.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979010
245 1 0 _aModeling and Optimization in Software-Defined Networks
_h[electronic resource] /
_cby Konstantinos Poularakis, Leandros Tassiulas, T.V. Lakshman.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXIV, 160 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
505 0 _aPreface -- Acknowledgments -- Introduction -- SDN Control Plane Optimization -- SDN Data Plane Optimization -- Future Research Directions -- Bibliography -- Authors' Biographies.
520 _aThis book provides a quick reference and insights into modeling and optimization of software-defined networks (SDNs). It covers various algorithms and approaches that have been developed for optimizations related to the control plane, the considerable research related to data plane optimization, and topics that have significant potential for research and advances to the state-of-the-art in SDN. Over the past ten years, network programmability has transitioned from research concepts to more mainstream technology through the advent of technologies amenable to programmability such as service chaining, virtual network functions, and programmability of the data plane. However, the rapid development in SDN technologies has been the key driver behind its evolution. The logically centralized abstraction of network states enabled by SDN facilitates programmability and use of sophisticated optimization and control algorithms for enhancing network performance, policy management, and security.Furthermore, the centralized aggregation of network telemetry facilitates use of data-driven machine learning-based methods. To fully unleash the power of this new SDN paradigm, though, various architectural design, deployment, and operations questions need to be addressed. Associated with these are various modeling, resource allocation, and optimization opportunities.The book covers these opportunities and associated challenges, which represent a ``call to arms'' for the SDN community to develop new modeling and optimization methods that will complement or improve on the current norms.
650 0 _aArtificial intelligence.
_93407
650 0 _aCooperating objects (Computer systems).
_96195
650 0 _aProgramming languages (Electronic computers).
_97503
650 0 _aTelecommunication.
_910437
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aCyber-Physical Systems.
_932475
650 2 4 _aProgramming Language.
_939403
650 2 4 _aCommunications Engineering, Networks.
_931570
700 1 _aTassiulas, Leandros.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979011
700 1 _aLakshman, T.V.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979012
710 2 _aSpringerLink (Online service)
_979013
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031002465
776 0 8 _iPrinted edition:
_z9783031012549
776 0 8 _iPrinted edition:
_z9783031035104
830 0 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
_979014
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02382-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c84697
_d84697