Wayne Goodridgehttp://hdl.handle.net/2139/84932016-05-28T23:58:26Z2016-05-28T23:58:26ZCOMPARING A NOVEL QOS ROUTING ALGORITHM TO STANDARDSivakumar, ShyamalaPhillips, BillRobertson, WilliamGoodridge S., Waynehttp://hdl.handle.net/2139/85062011-03-03T22:01:19Z2010-11-19T16:42:41ZCOMPARING A NOVEL QOS ROUTING ALGORITHM TO STANDARD
Sivakumar, Shyamala; Phillips, Bill; Robertson, William; Goodridge S., Wayne
The problem of finding QoS paths involving several
combinations of network metrics is NP-complete. This
motivates the use of heuristic approaches for finding
feasible QoS paths. Many constraint based routing
algorithms find QoSpaths by first pruning resources that
do not satish the requirements of the trafic flow and
then running a shortest path algorithm on the residual
graph. This approach results in a QoS path that biases
thefirst metric used in the search process. In addition, it
can be shown that this approach may not alwaysfind the
optimal path. Our research introduces a QoS routing
algorithm that is based on a decision support system that
is used to compute QoS paths. We demonstrate the
feasibility of this approach by comparing it to standard
pruning techniques.
2010-11-19T16:42:41ZA Study of the Effectiveness of the Routing Decision Support AlgorithmSahai, AshokNikov, AlexanderGoodridge, Waynehttp://hdl.handle.net/2139/84972011-03-03T22:01:29Z2010-11-03T15:11:32ZA Study of the Effectiveness of the Routing Decision Support Algorithm
Sahai, Ashok; Nikov, Alexander; Goodridge, Wayne
Multi criteria decision making (MCDM) methods like
analytic hierarchy process, ELECTRE and multi-attribute utility
theory are critically studied. They have irregularities in terms of the
reliability of ranking of the best alternatives. The Routing Decision
Support (RDS) algorithm is trying to improve some of their
deficiencies. This paper gives a mathematical verification that the
RDS algorithm conforms to the test criteria for an effective MCDM
method when a linear preference function is considered
2010-11-03T15:11:32ZHeuristic Constraint-Path Routing Decision SystemSivakumar, ShyamalaPhillips, William J.Robertson, WilliamGoodridge, Waynehttp://hdl.handle.net/2139/84962010-11-03T15:11:23Z2005-05-18T00:00:00ZHeuristic Constraint-Path Routing Decision System
Sivakumar, Shyamala; Phillips, William J.; Robertson, William; Goodridge, Wayne
Heuristic QoS algorithms under strict constraints perform poorly in terms of finding a path that is suitable for a user?s QoS needs - the multiple constraint path problem (MCP). Exact QoS algorithms, on the other hand, guarantee that a path satisfying user needs would be found and o..er a more realistic approach for solving the MCP problem in view of the fact that the NP-complete character of graphs are not common in real networks. This fact has driven approaches like the SAMCRA and A*prune algorithms. However, these algorithms still have very high running times relative to heuristic approaches. When QoS routing algorithms are used in online Tra..c Engineering (TE) environments it may be necessary to route thousands of traffic flows each minute. Exact algorithms simply cannot work in such environments. We propose a heuristic algorithm that is suitable for working in an online TE environment. Simulations show that this algorithm produce high success rates in terms of finding suitable constraint paths for user flows while at the same time having execution times comparable to another heuristic based algorithms.
2005-05-18T00:00:00ZIntegrating Two Artificial Intelligence Theories in a Medical Diagnosis ApplicationPeter, HadrainGoodridge, Waynehttp://hdl.handle.net/2139/84952010-11-03T14:27:45Z2010-11-03T14:27:45ZIntegrating Two Artificial Intelligence Theories in a Medical Diagnosis Application
Peter, Hadrain; Goodridge, Wayne
Reasoning Systems (Inference Mechanisms) and Neural Networks are two major areas of Artificial Intelligence(AI). The use of case-based reasoning in Artificial Intelligence systems is well known. Similarly, the AI literature is replete with papers on neural networks. However, there is relatively little research in which the theories of case-based reasoning and neural networks are combined.In this paper we integrate the two theories and show how the resulting model is used in a medical diagnosis application.An implementation of our model provides a valuable prototype for medical experts and medical students alike.
2010-11-03T14:27:45Z