A Genetic Algorithm-Based Approach to Identify Near-Optimal Non-Equidistant Checkpointing Strategies

Software intensive systems rely on checkpointing to prevent loss of computation, by performing periodic backups. Non-equidistant checkpointing strategies have been proposed for specialized hardware and software applications as well as specific failure distributions. However, a general method to identify a non-equidistant checkpointing strategy for an arbitrary combination of application and failure distribution would be beneficial. This paper proposes an approach to identify a near optimal non-equidistant checkpointing strategy with a genetic algorithm, which only requires knowledge of the failure distribution. Experiments suggest that the approach consistently outperformed the traditional strategy of equidistant checkpoints under (i) a range of total processing times and (ii) different values of distributions exhibiting increasing, constant, and decreasing failure rates.