東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Intelligent distributed fault and pe...

Li, Hongjun.

Linked to FindBook

Google Book

Amazon

博客來

Intelligent distributed fault and performance management for communication networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Intelligent distributed fault and performance management for communication networks./
Author:	Li, Hongjun.
Description:	186 p.
Notes:	Chair: John S. Baras.
Contained By:	Dissertation Abstracts International63-06B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3055593
ISBN:	0493711813

Intelligent distributed fault and performance management for communication networks.
Li, Hongjun.

Intelligent distributed fault and performance management for communication networks. - 186 p.

Chair: John S. Baras.

Thesis (Ph.D.)--University of Maryland College Park, 2002.

This dissertation is devoted to the design of an intelligent, distributed fault and performance management system for communication networks. The architecture is based on a distributed agent paradigm, with belief networks as the framework for knowledge representation and evidence propagation.

ISBN: 0493711813Subjects--Topical Terms:

626642
Computer Science.

Intelligent distributed fault and performance management for communication networks.
LDR:03399nam 2200325 a 45 001 926765
005 20110422
008 110422s2002 eng d
020 $a 0493711813
035 $a (UnM)AAI3055593
035 $a AAI3055593
040 $a UnM $c UnM
100 1 $a Li, Hongjun. $3 1250349
245 1 0 $a Intelligent distributed fault and performance management for communication networks.
300 $a 186 p.
500 $a Chair: John S. Baras.
500 $a Source: Dissertation Abstracts International, Volume: 63-06, Section: B, page: 2952.
502 $a Thesis (Ph.D.)--University of Maryland College Park, 2002.
520 $a This dissertation is devoted to the design of an intelligent, distributed fault and performance management system for communication networks. The architecture is based on a distributed agent paradigm, with belief networks as the framework for knowledge representation and evidence propagation.
520 $a The dissertation consists of four major parts. First, we choose the mobile code technology to help implement a distributed, extensible framework for supporting adaptive, dynamic network monitoring and control. The focus of our work is on three aspects. First, the design of the standard infrastructure, or Virtual Machine, based on which agents could be created, deployed, managed and initiated to run. Second, the collection API for our delegated agents to collect data from network elements. Third, the callback mechanism through which the functionality of the delegated agents or even the native software could be extended. We propose three system designs based on such ideas.
520 $a Second, we propose a distributed framework for intelligent fault management purpose. The managed network is divided into several domains and for each domain, there is an intelligent agent attached to it, which is responsible for this domain's fault management tasks. Belief network are embedded in such an agent as the probabilistic fault models, based on which evidence propagation and decision making processes are carried out.
520 $a Third, we address the problem of parameter learning for belief networks with fixed structure. Based on the idea of Expectation-Maximization (EM), we derive a uniform learning algorithm under incomplete observations. Further, we study the rate of convergence via the derivation of Jacobian matrices of our algorithm and provide a guideline for choosing step size. Our simulation results show that the learned values are relatively close to the true values. This algorithm is suitable for both batch and on-line mode.
520 $a Finally, when using belief networks as the fault models, we identify two fundamental questions: When can I say that I get the right diagnosis and stop? If right diagnosis has not been obtained yet, which test should I choose next? The first question is tackled by the notion of right diagnosis via intervention, and we solve the second problem based on a dynamic decision theoretic strategy. Simulation shows that our strategy works well for the diagnosis purpose. This framework is general, scalable, flexible and robust.
590 $a School code: 0117.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0544
690 $a 0984
710 2 0 $a University of Maryland College Park. $3 1249734
773 0 $t Dissertation Abstracts International $g 63-06B.
790 $a 0117
790 1 0 $a Baras, John S., $e advisor
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3055593