ED4I: Error Detection by Diverse Data and Duplicated Instructions
IEEE Transactions on Computers - Special issue on fault-tolerant embedded systems
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Detecting Processor Hardware Faults by Means of Automatically Generated Virtual Duplex Systems
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Statistical Selection of Compiler Options
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
SWIFT: Software Implemented Fault Tolerance
Proceedings of the international symposium on Code generation and optimization
A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
Applied Soft Computing
Watchdog Processors and Structural Integrity Checking
IEEE Transactions on Computers
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
COTS-based applications in space avionics
Proceedings of the Conference on Design, Automation and Test in Europe
Compiler-Directed Soft Error Mitigation for Embedded Systems
IEEE Transactions on Dependable and Secure Computing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
| Hi-index | 12.05 |
The design of fault tolerant systems is gaining importance in large domains of embedded applications where design constrains are as important as reliability. New software techniques, based on selective application of redundancy, have shown remarkable fault coverage with reduced costs and overheads. However, the large number of different solutions provided by these techniques, and the costly process to assess their reliability, make the design space exploration a very difficult and time-consuming task. This paper proposes the integration of a multi-objective optimization tool with a software hardening environment to perform an automatic design space exploration in the search for the best trade-offs between reliability, cost, and performance. The first tool is commanded by a genetic algorithm which can simultaneously fulfill many design goals thanks to the use of the NSGA-II multi-objective algorithm. The second is a compiler-based infrastructure that automatically produces selective protected (hardened) versions of the software and generates accurate overhead reports and fault coverage estimations. The advantages of our proposal are illustrated by means of a complex and detailed case study involving a typical embedded application, the AES (Advanced Encryption Standard).