Title page for ETD etd-01042006-134224


Type of Document Dissertation
Author Adams, Kevin Page
URN etd-01042006-134224
Title An Approach to Real Time Adaptive Decision Making in Dynamic Distributed Systems
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Gracanin, Denis Committee Chair
Arthur, James D. Committee Member
Bohner, Shawn A. Committee Member
Bouguettaya, Athman Committee Member
Triantis, Konstantinos P. Committee Member
Keywords
  • Resiliency
  • Policy-based Backup
  • Neural Networks
  • Dynamic Optimization
  • Control Theory
  • Prediction
Date of Defense 2005-12-12
Availability unrestricted
Abstract
Efficient operation of a dynamic system requires (near) optimal real-time control decisions. Those decisions depend on a set of control parameters that change over time. Very often, the optimal decision can be made only with the knowledge of future values of control parameters. As a consequence, the decision process is heuristic in nature. The optimal decision can be determined only after the fact, once the uncertainty is removed.

For some types of dynamic systems, the heuristic approach can be very effective. The basic premise is that the future values of control parameters can be predicted with sufficient accuracy. We can either predict those value based on a good model of the system or based on historical data. In many cases, the good model is not available. In that case, prediction using historical data is the only option. It is necessary to detect similarities with the current situation and extrapolate future values. In other words, we need to (quickly) identify patterns in historical data that match the current data pattern. The low sensitivity of the optimal solution is critical. Small variations in data patterns should affect minimally the optimal solution. Resource allocation problems and other “discrete decision systems” are good examples of such systems.

The main contribution of this work is a novel heuristic methodology that uses neural networks for classifying, learning and detecting changing patterns, as well as making (near) real-time decisions. We improve on existing approaches by providing a real-time adaptive approach that takes into account changes in system behavior with minimal operational delay without the need for an accurate model. The methodology is validated by extensive simulation and practical measurements. Two metrics are proposed to quantify the quality of control decisions as well as a comparison to the optimal solution.

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