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K.: Game theoretic stochastic routing for fault tolerance and security in communication networks
 IEEE/ACM Trans. on Parallel and Distributed Systems
, 2007
"... Most of today’s Internet routing protocols forward packets of a connection over a single path. This means that, even if redundant resources are available, a single failure (accidental or due to malicious activities) along a route will interrupt connections that use that route. Given the reactive app ..."
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Cited by 9 (2 self)
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Most of today’s Internet routing protocols forward packets of a connection over a single path. This means that, even if redundant resources are available, a single failure (accidental or due to malicious activities) along a route will interrupt connections that use that route. Given the reactive approach to failure recovery that most current routing protocols employ, these communication disruptions may last for long enough time to be noticeable by higher protocol layers. Also, given that the path over which a connection’s packets travels is fairly predictable and easy to determine, connections are vulnerable to packet interception and eavesdropping attacks. In this paper, we introduce the GameTheoretic Stochastic Routing (GTSR) framework, a proactive alternative to today’s reactive approaches to route repair. GTSR minimizes the impact of link and router failure by: (1) computing multiple paths between source and destination and (2) selecting among these paths randomly to forward packets. Moreover, besides improving faulttolerance, the fact that GTSR makes packets take random paths from source to destination also improves security. For example, it makes connection eavesdropping attacks maximally difficult as the attacker would have to listen on all possible routes. The approaches developed are suitable for network layer routing as well as for application layer
Graph optimization using fractal decomposition with application to cooperative routing problems.” Submitted for publication
, 2007
"... We introduce a method of hierarchically decomposing graph optimization problems to obtain approximate solutions with low computation. The method uses a partition on the graph to convert the original problem to a high level problem and several lower level problems. On each level, the resulting proble ..."
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Cited by 1 (0 self)
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We introduce a method of hierarchically decomposing graph optimization problems to obtain approximate solutions with low computation. The method uses a partition on the graph to convert the original problem to a high level problem and several lower level problems. On each level, the resulting problems are in exactly the same form as the original one, so they can be further decomposed. In this way, the problems become fractal in nature. We use bestcase and worstcase instances of the decomposed problems to establish upper and lower bounds on the optimal criteria, and these bounds are achieved with significantly less computation than what is required to solve the original problem. We show that as the number of hierarchical levels increases, the computational complexity approaches O(n) at the expense of looser approximation bounds. For regular lattice graphs, we provide constant factor bounds on the approximation error. We demonstrate the fractal decomposition method on three example problems related to cooperative routing: shortest path matrix, maximum flow matrix, and cooperative search. Largescale simulations show that this fractal decomposition method is computationally fast and can yield good results for practical problems. 1
Education
, 2007
"... GraphBased Approximation Algorithms for Cooperative Routing Problems Copyright c ○ 2007 by ..."
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GraphBased Approximation Algorithms for Cooperative Routing Problems Copyright c ○ 2007 by
GRAPH OPTIMIZATION USING FRACTAL DECOMPOSITION
"... We introduce a method of hierarchically decomposing graph optimization problems to obtain approximate solutions with low computation. The method uses a partition on the graph to convert the original problem to a high level problem and several lower level problems. On each level, the resulting proble ..."
Abstract
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We introduce a method of hierarchically decomposing graph optimization problems to obtain approximate solutions with low computation. The method uses a partition on the graph to convert the original problem to a high level problem and several lower level problems. On each level, the resulting problems are in exactly the same form as the original one, so they can be further decomposed. In this way, the problems become fractal in nature. We use bestcase and worstcase instances of the decomposed problems to establish upper and lower bounds on the optimal criteria, and these bounds are achieved with significantly less computation than what is required to solve the original problem. We show that as the number of hierarchical levels increases, the computational complexity approaches O(n) at the expense of looser bounds on the optimal solution. We demonstrate this method on three example problems: allpairs shortest path, allpairs maximum flow, and cooperative search. Largescale simulations show that this fractal decomposition method is computationally fast and can yield good results for practical problems.
ative Commons Attribution NonCommercial No Derivatives licence. Researchers
"... I herewith certify that all material in this dissertation which is not my own work has been properly acknowledged. ..."
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I herewith certify that all material in this dissertation which is not my own work has been properly acknowledged.