000 | 04079nam a22005775i 4500 | ||
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001 | 978-3-031-02382-8 | ||
003 | DE-He213 | ||
005 | 20240730163523.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2021 sz | s |||| 0|eng d | ||
020 |
_a9783031023828 _9978-3-031-02382-8 |
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024 | 7 |
_a10.1007/978-3-031-02382-8 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aCOM004000 _2bisacsh |
|
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_aUYQ _2thema |
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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. |
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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 |
||
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 |
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650 | 0 |
_aCooperating objects (Computer systems). _96195 |
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650 | 0 |
_aProgramming languages (Electronic computers). _97503 |
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650 | 0 |
_aTelecommunication. _910437 |
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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 |
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700 | 1 |
_aLakshman, T.V. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _979012 |
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710 | 2 |
_aSpringerLink (Online service) _979013 |
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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 | ||
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