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Título: Quality of service routing using decentralized learning
Autores: Heidari, Fariba.
Fecha: 2009
Publicador: McGill University - MCGILL
Fuente:
Tipo: Electronic Thesis or Dissertation
Tema: Computer networks -- Quality control.
MPLS standard.
Telecommunication -- Traffic.
Network performance (Telecommunication)
Online algorithms.
Heuristic algorithms.
Descripción: This thesis presents several decentralized, learning-based algorithms for on-line routing of bandwidth guaranteed paths. The presented routing algorithms do not need any a-priori knowledge of traffic demand; they use only their locally observed events and update their routing policy using learning schemes. The employed learning algorithms are either learning automata or the multi-armed bandit algorithms. We investigate the asymptotic behavior of the proposed routing algorithms and prove the convergence of one of them to the user equilibrium. Discrete event simulation results show the merit of these algorithms in terms of improving the resource utilization and increasing the network admissibility compared with shortest path routing.
We investigate the performance degradation due to decentralized routing as opposed to centralized optimal routing policies in practical scenarios. The system optimal and the Nash bargaining solutions are two centralized benchmarks used in this study. We provide nonlinear programming formulations of these problems along with a distributed recursive approach to compute the solutions. An on-line partially-decentralized control architecture is also proposed to achieve the system optimal and the Nash bargaining solution performances. Numerical results in some practical scenarios with well engineered networks, where the network resources and traffic demand are well matched, indicate that decentralized learning techniques provide efficient, stable and scalable approaches for routing the bandwidth guaranteed paths.
In the context of on-line learning, we propose a new algorithm to track the best action-selection policy when it abruptly changes over time. The proposed algorithm employs change detection mechanisms to detect the sudden changes and restarts the learning process on the detection of an abrupt change. The performance analysis of this study reveals that when all the changes are detectable by the change detection mechanism, the proposed tracking the best action-selection policy algorithm is rate optimal. On-line routing of bandwidth guaranteed paths with the potential occurrence of network shocks such as significant changes in the traffic demand is one of the applications of the devised algorithm. Simulation results show the merit of the proposed algorithm in tracking the optimal routing policy when it abruptly changes.
Idioma: en