LoboVault Home

Algorithms for self-healing networks


Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/10914

Algorithms for self-healing networks

Show full item record

Title: Algorithms for self-healing networks
Author: Trehan, Amitabh
Advisor(s): Saia, Jared
Committee Member(s): Hayes, Thomas
Moore, Cris
Berger-Wolf, Tanya
Department: University of New Mexico. Dept. of Computer Science
Subject: algorithms
data structure
half-full tree
LC Subject(s): Adaptive routing (Computer network management)
Self-adaptive software.
Adaptive computing systems.
Distributed algorithms.
Degree Level: Doctoral
Abstract: Many modern networks are reconfigurable, in the sense that the topology of the network can be changed by the nodes in the network. For example, peer-to-peer, wireless and ad-hoc networks are reconfigurable. More generally, many social networks, such as a company's organizational chart; infrastructure networks, such as an airline's transportation network; and biological networks, such as the human brain, are also reconfigurable. Modern reconfigurable networks have a complexity unprecedented in the history of engineering, resembling more a dynamic and evolving living animal rather than a structure of steel designed from a blueprint. Unfortunately, our mathematical and algorithmic tools have not yet developed enough to handle this complexity and fully exploit the flexibility of these networks. We believe that it is no longer possible to build networks that are scalable and never have node failures. Instead, these networks should be able to admit small, and, maybe, periodic failures and still recover like skin heals from a cut. This process, where the network can recover itself by maintaining key invariants in response to attack by a powerful adversary is what we call self-healing. Here, we present several fast and provably good distributed algorithms for self-healing in reconfigurable dynamic networks. Each of these algorithms have different properties, a different set of gaurantees and limitations. We also discuss future directions and theoretical questions we would like to answer.
Graduation Date: May 2010
URI: http://hdl.handle.net/1928/10914

Files in this item

Files Size Format View
PhDDissertation.pdf 2.416Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record

UNM Libraries

Search LoboVault


My Account