000			02883nam a2200373 i 4500
001			CR9781108377119
003			UkCbUP
005			20240730160824.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			170420s2021||||enk o ||1 0|eng|d
020			_a9781108377119 (ebook)
020			_z9781108421485 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aTK5105.5485 _b.H4 2021
082	0	0	_a004.6 _223
100	1		_aHe, Ting _c(Associate professor of computer science and engineering), _eauthor. _975073
245	1	0	_aNetwork tomography : _bidentifiability, measurement design, and network state inference / _cTing He, Liang Ma, Ananthram Swami, Don Towsley.
264		1	_aCambridge : _bCambridge University Press, _c2021.
300			_a1 online resource (xi, 231 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 28 May 2021).
505	0		_aPreliminaries -- Fundamental conditions for additive network tomography -- Monitor placement for additive network tomography -- Measurement path construction for additive network tomography -- Fundamental conditions for Boolean network tomography -- Measurement design for Boolean network tomography -- Stochastic network tomography using unicast measurements -- Stochastic network tomography using multicast measurements -- Other applications and miscellaneous techniques.
520			_aProviding the first truly comprehensive overview of Network Tomography - a novel network monitoring approach that makes use of inference techniques to reconstruct the internal network state from external vantage points - this rigorous yet accessible treatment of the fundamental theory and algorithms of network tomography covers the most prominent results demonstrated on real-world data, including identifiability conditions, measurement design algorithms, and network state inference algorithms, alongside practical tools for applying these techniques to real-world network management. It describes the main types of mathematical problems, along with their solutions and properties, and emphasizes the actions that can be taken to improve the accuracy of network tomography. With proofs and derivations introduced in an accessible language for easy understanding, this is an essential resource for professional engineers, academic researchers, and graduate students in network management and network science.
650		0	_aComputer networks _xMonitoring. _97466
700	1		_aMa, Liang _c(Research scientist), _eauthor. _975074
700	1		_aTowsley, Don, _eauthor. _975075
700	1		_aSwami, Ananthram, _eauthor. _930056
776	0	8	_iPrint version: _z9781108421485
856	4	0	_uhttps://doi.org/10.1017/9781108377119
942			_cEBK
999			_c84347 _d84347