Combining Genetic Algorithms & Simulation to Search for Failure Scenarios in System Models

Published

October 28, 2013

Author(s)

Kevin L. Mills, Christopher E. Dabrowski, James J. Filliben, Sanford P. Ressler

Abstract

Large infrastructures, such as clouds, can exhibit substantial outages, sometimes due to failure scenarios that were not considered during system design. We define a method that uses a genetic algorithm (GA) to search system simulations for parameter combinations that result in system failures, so that designers can take mitigation steps before deployment. We apply the method to study an existing infrastructure-as-a-service cloud simulator. We characterize the dynamics, quality, effectiveness and cost of GA search, when applied to seek a known failure scenario. Further, we iterate the GA search to reveal unknown failure scenarios. We find that, when schedule permits and failure costs are high, combining GA search with simulation proves useful for exploring and improving system designs.

Proceedings Title

Proceedings of the Fifth International Conference on Advances in System Simulation

Conference Dates

October 27-November 1, 2013

Conference Location

Venice

Conference Title

SIMUL 2013, The Fifth International Conference on Advances in System Simulation

Pub Type

Conferences

Keywords

failure prediction, genetic algorithms, simulation methodology, system design

Citation

Mills, K. , Dabrowski, C. , Filliben, J. and Ressler, S. (2013), Combining Genetic Algorithms & Simulation to Search for Failure Scenarios in System Models, Proceedings of the Fifth International Conference on Advances in System Simulation, Venice, -1 (Accessed April 18, 2024)