Open AccessSliding mode learning based congestion control for DiffServ networks
Author(s) -
Tuan Do Manh,
Jin Jiong,
Wang Hai,
Man Zhihong
Publication year - 2016
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2015.0948
Subject(s) - network congestion , scheme (mathematics) , computer science , differentiated services , controller (irrigation) , control theory (sociology) , fluid queue , sliding mode control , computer network , robust control , control (management) , distributed computing , quality of service , control system , engineering , mathematics , artificial intelligence , nonlinear system , network packet , mathematical analysis , physics , queueing theory , electrical engineering , quantum mechanics , agronomy , biology
In this study, a robust sliding mode learning control scheme is proposed to address the congestion control problem in differentiated services (DiffServ) networks. A validated non‐linear network model is based on fluid flow theory corresponding to two important services, namely, the premium traffic and the ordinary traffic. The proposed congestion controller is able to efficiently cope with both the physical network resource constraints and unknown time delays associated with networking systems. Numerical results are presented to illustrate the effectiveness of the proposed control scheme.