Determine network survivability using heuristic models
Approved for public release; distribution in unlimited. === Contemporary large-scale networked systems have improved the efficiency and effectiveness of our way of life. However, such benefit is accompanied by elevated risks of intrusion and compromises. Incorporating survivability capabilities into...
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ndltd-nps.edu-oai-calhoun.nps.edu-10945-11292017-05-24T16:06:46Z Determine network survivability using heuristic models Chua, Eng Hong Xie, Geoffrey Lundy, Bert Computer Science Computer networks Reliability Fault Tolerance Network Reliability Survivability Server Placement Approved for public release; distribution in unlimited. Contemporary large-scale networked systems have improved the efficiency and effectiveness of our way of life. However, such benefit is accompanied by elevated risks of intrusion and compromises. Incorporating survivability capabilities into systems is one of the ways to mitigate these risks. The Server Agent-based Active network Management (SAAM) project was initiated as part of the next generation Internet project to address the increasing multi-media Internet service demands. Its objective is to provide a consistent and dedicated quality of service to the users. SAAM monitors the network traffic conditions in a region and responds to routing requests from the routers in that region with optimal routes. Mobility has been incorporated to SAAM server to prevent a single point of failure from bringing down the entire SAAM server and its service. With mobility, it is very important to select a good SAAM server locality from the client's point of view. The choice of the server must be a node where connection to the client is most survivable. In order to do that, a general metric is defined to measure the connection survivability of each of the potential server hosts. However, due to the complexity of the network, the computation of the metric becomes very complex too. This thesis develops heuristic solutions of polynomial complexity to find the hosting server node. In doing so, it minimizes the time and computer power required. Defence Science & Technology Agency (Singapore) 2012-03-14T17:30:40Z 2012-03-14T17:30:40Z 2003-03 Thesis http://hdl.handle.net/10945/1129 Copyright is reserved by the copyright owner. xiv, 99 p. : ill. (some col.) ; application/pdf Monterey, California. Naval Postgraduate School |
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Computer networks Reliability Fault Tolerance Network Reliability Survivability Server Placement |
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Computer networks Reliability Fault Tolerance Network Reliability Survivability Server Placement Chua, Eng Hong Determine network survivability using heuristic models |
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Approved for public release; distribution in unlimited. === Contemporary large-scale networked systems have improved the efficiency and effectiveness of our way of life. However, such benefit is accompanied by elevated risks of intrusion and compromises. Incorporating survivability capabilities into systems is one of the ways to mitigate these risks. The Server Agent-based Active network Management (SAAM) project was initiated as part of the next generation Internet project to address the increasing multi-media Internet service demands. Its objective is to provide a consistent and dedicated quality of service to the users. SAAM monitors the network traffic conditions in a region and responds to routing requests from the routers in that region with optimal routes. Mobility has been incorporated to SAAM server to prevent a single point of failure from bringing down the entire SAAM server and its service. With mobility, it is very important to select a good SAAM server locality from the client's point of view. The choice of the server must be a node where connection to the client is most survivable. In order to do that, a general metric is defined to measure the connection survivability of each of the potential server hosts. However, due to the complexity of the network, the computation of the metric becomes very complex too. This thesis develops heuristic solutions of polynomial complexity to find the hosting server node. In doing so, it minimizes the time and computer power required. === Defence Science & Technology Agency (Singapore) |
author2 |
Xie, Geoffrey |
author_facet |
Xie, Geoffrey Chua, Eng Hong |
author |
Chua, Eng Hong |
author_sort |
Chua, Eng Hong |
title |
Determine network survivability using heuristic models |
title_short |
Determine network survivability using heuristic models |
title_full |
Determine network survivability using heuristic models |
title_fullStr |
Determine network survivability using heuristic models |
title_full_unstemmed |
Determine network survivability using heuristic models |
title_sort |
determine network survivability using heuristic models |
publisher |
Monterey, California. Naval Postgraduate School |
publishDate |
2012 |
url |
http://hdl.handle.net/10945/1129 |
work_keys_str_mv |
AT chuaenghong determinenetworksurvivabilityusingheuristicmodels |
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1718452409160695808 |