A study of dynamic meta-learning for failure prediction in large-scale systems
Journal of Parallel and Distributed Computing
Proactive process-level live migration and back migration in HPC environments
Journal of Parallel and Distributed Computing
Checkpointing algorithms and fault prediction
Journal of Parallel and Distributed Computing
A job submission manager for large-scale distributed systems based on job futurity predictor
International Journal of Grid and Utility Computing
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As the scale of parallel systems continues to grow, fault management of these systems is becoming a critical challenge. While existing research mainly focuses on developing or improving fault tolerance techniques, a number of key issues remain open. In this paper, we propose runtime strategies for spare node allocation and job rescheduling in response to failure prediction. These strategies, together with failure predictor and fault tolerance techniques, construct a runtime system called FARS (Fault-Aware Runtime System). In particular, we propose a 0-1 knapsack model and demonstrate its flexibility and effectiveness for reallocating running jobs to avoid failures. Experiments, by means of synthetic data and real traces from production systems, show that FARS has the potential to significantly improve system productivity (i.e., performance and reliability).