Adaptive Algorithms for Diagnosing Large-Scale Failures in Computer Networks

Diagnosing Large-Scale Failures in Computer Networks

In this paper, we propose an algorithm to efficiently diagnose large-scale clustered failures. The algorithm, Cluster-MAX-COVERAGE (CMC), is based on greedy approach. We address the challenge of determining faults with incomplete symptoms. CMC makes novel use of both positive and negative symptoms to output a hypothesis list with a low number of false negatives and false positives quickly. CMC ...

Control algorithms for large scale adaptive optics

of the Dissertation Control Algorithms for Large Scale Adaptive Optics

The application of DEA and queuing models to large scale computer networks

Decentralized Adaptive Control of Large-Scale Non-affine Nonlinear Time-Delay Systems Using Neural Networks

In this paper, a decentralized adaptive neural controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, non-affine subsystems and unknown nonlinear time-delay interconnections. The stability of the closed loop system is guaranteed through Lyapunov-Krasovskii stability analysis. Simulation results are provided to show the effectiveness of the proposed approache...

On-line search algorithms for fault diagnostics in large-scale computer communication networks

Se considera el problema de diagnosticar fallos en redes de comunicación de ordenadores con el objetivo de minimizar las pérdidas esperadas causadas por dichos fallos. Puesto que un fallo pudiera ser inadvertido en una inspección, es posible inspeccionar varias veces el mismo subsistema (módulo). Se desarrollan algoritmos “on-line” para este problema.