Network traffic recovery from link-load measurements using tensor triple decomposition strategy for third-order traffic tensors

Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics. We arrange the traffic data as a third-order tensor...

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Veröffentlicht in:Journal of computational and applied mathematics Jg. 447; S. 115901
Hauptverfasser: Ming, Zhenyu, Qin, Zhenzhi, Zhang, Liping, Xu, Yanwei, Qi, Liqun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2024
Schlagworte:
BB gradient algorithm
Network traffic recovery
Triple decomposition
65K05
49M29
Network traffic recovery
68M10
15A03
BB gradient algorithm
Triple decomposition
ISSN:0377-0427
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Zusammenfassung:Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics. We arrange the traffic data as a third-order tensor and utilize the triple decomposition technique proposed very recently by Qi et al. (2021). The studied model is a differentialble unconstrained minimization problem, which can be efficiently solved by a Barzilai–Borwein (BB) gradient algorithm. We prove that the generated iteration sequence can globally converge to a certain stationary point of the objective function. The numerical simulations on three open-source traffic datasets demonstrate the superiority of our method in comparison with other state-of-the-art algorithms.
ISSN:0377-0427
DOI:10.1016/j.cam.2024.115901