Checkpoint Placement for Systematic Fault-Injection Campaigns

TL;DR

This paper investigates optimal checkpoint placement in systematic fault-injection campaigns to significantly reduce forwarding cycles, using formal models and algorithms, leading to substantial efficiency improvements in resilience analysis.

Contribution

It formalizes the checkpoint placement problem as a maximum-weight reward path, proposes ILP and dynamic programming solutions, and introduces a genetic algorithm heuristic for optimal fault injection efficiency.

Findings

Reduced forwarding cycles by up to 99.934% with 16 checkpoints

Formalized checkpoint placement as a graph optimization problem

Developed ILP, dynamic programming, and heuristic methods

Abstract

Shrinking hardware structures and decreasing operating voltages lead to an increasing number of transient hardware faults,which thus become a core problem to consider for safety-critical systems. Here, systematic fault injection (FI), where one program-under-test is systematically stressed with faults, provides an in-depth resilience analysis in the presence of faults. However, FI campaigns require many independent injection experiments and, combined, long run times, especially if we aim for a high coverage of the fault space. One cost factor is the forwarding phase, which is the time required to bring the system-under test into the fault-free state at injection time. One common technique to speed up the forwarding are checkpoints of the fault-free system state at fixed points in time. In this paper, we show that the placement of checkpoints has a significant influence on the required forwarding cycles, especially if we place faults non-uniformly on the time axis. For this, we discuss the checkpoint-selection problem in general, formalize it as a maximum-weight reward path problem in graphs, propose an ILP formulation and a dynamic programming algorithm that find the optimal solution, and provide a heuristic checkpoint-selection method based on a genetic algorithm. Applied to the MiBench benchmark suite, our approach consistently reduces the forward-phase cycles by at least 88 percent and up to 99.934 percent when placing 16 checkpoints.