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020 _a9783031799921
_9978-3-031-79992-1
024 7 _a10.1007/978-3-031-79992-1
_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 _aJiang, Libin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983816
245 1 0 _aScheduling and Congestion Control for Wireless and Processing Networks
_h[electronic resource] /
_cby Libin Jiang, Jean Walrand.
250 _a1st ed. 2010.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2010.
300 _aXI, 144 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 _aIntroduction -- Overview -- Scheduling in Wireless Networks -- Utility Maximization in Wireless Networks -- Distributed CSMA Scheduling with Collisions -- Stochastic Processing networks.
520 _aIn this book, we consider the problem of achieving the maximum throughput and utility in a class of networks with resource-sharing constraints. This is a classical problem of great importance. In the context of wireless networks, we first propose a fully distributed scheduling algorithm that achieves the maximum throughput. Inspired by CSMA (Carrier Sense Multiple Access), which is widely deployed in today's wireless networks, our algorithm is simple, asynchronous, and easy to implement. Second, using a novel maximal-entropy technique, we combine the CSMA scheduling algorithm with congestion control to approach the maximum utility. Also, we further show that CSMA scheduling is a modular MAC-layer algorithm that can work with other protocols in the transport layer and network layer. Third, for wireless networks where packet collisions are unavoidable, we establish a general analytical model and extend the above algorithms to that case. Stochastic Processing Networks (SPNs) model manufacturing, communication, and service systems. In manufacturing networks, for example, tasks require parts and resources to produce other parts. SPNs are more general than queueing networks and pose novel challenges to throughput-optimum scheduling. We proposes a "deficit maximum weight" (DMW) algorithm to achieve throughput optimality and maximize the net utility of the production in SPNs. Table of Contents: Introduction / Overview / Scheduling in Wireless Networks / Utility Maximization in Wireless Networks / Distributed CSMA Scheduling with Collisions / Stochastic Processing networks.
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 _aWalrand, Jean.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983823
710 2 _aSpringerLink (Online service)
_983824
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031799914
776 0 8 _iPrinted edition:
_z9783031799938
830 0 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
_983825
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79992-1
912 _aZDB-2-SXSC
942 _cEBK
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