Checkpointing algorithms and fault prediction

This paper deals with the impact of fault prediction techniques on checkpointing strategies. We extend the classical first-order analysis of Young and Daly in the presence of a fault prediction system, characterized by its recall and its precision. In this framework, we provide an optimal algorithm to decide when to take predictions into account, and we derive the optimal value of the checkpointing period. These results allow to analytically assess the key parameters that impact the performance of fault predictors at very large scale.Comment: Supported in part by ANR Rescue. Published in Journal of Parallel and Distributed Computing. arXiv admin note: text overlap with arXiv:1207.693

Analysis of checkpointing schemes for multiprocessor systems

Parallel computing systems provide hardware redundancy that helps to achieve low cost fault-tolerance, by duplicating the task into more than a single processor, and comparing the states of the processors at checkpoints. This paper suggests a novel technique, based on a Markov Reward Model (MRM), for analyzing the performance of checkpointing schemes with task duplication. We show how this technique can be used to derive the average execution time of a task and other important parameters related to the performance of checkpointing schemes. Our analytical results match well the values we obtained using a simulation program. We compare the average task execution time and total work of four checkpointing schemes, and show that generally increasing the number of processors reduces the average execution time, but increases the total work done by the processors. However, in cases where there is a big difference between the time it takes to perform different operations, those results can change

Optimizing memory management for optimistic simulation with reinforcement learning

Simulation is a powerful technique to explore complex scenarios and analyze systems related to a wide range of disciplines. To allow for an efficient exploitation of the available computing power, speculative Time Warp-based Parallel Discrete Event Simulation is universally recognized as a viable solution. In this context, the rollback operation is a fundamental building block to support a correct execution even when causality inconsistencies are a posteriori materialized. If this operation is supported via checkpoint/restore strategies, memory management plays a fundamental role to ensure high performance of the simulation run. With few exceptions, adaptive protocols targeting memory management for Time Warp-based simulations have been mostly based on a pre-defined analytic models of the system, expressed as a closed-form functions that map system's state to control parameters. The underlying assumption is that the model itself is optimal. In this paper, we present an approach that exploits reinforcement learning techniques. Rather than assuming an optimal control strategy, we seek to find the optimal strategy through parameter exploration. A value function that captures the history of system feedback is used, and no a-priori knowledge of the system is required. An experimental assessment of the viability of our proposal is also provided for a mobile cellular system simulation