Orchestrating Fault Prediction with Live Migration and Checkpointing

Abstract

Checkpoint/Restart (C/R) is widely used to provide fault tolerance on High-Performance Computing (HPC) systems. However, Parallel File System (PFS) overhead and failure uncertainty cause significant application overhead. This paper develops an adaptive multi-level C/R model that incorporates a failure prediction and analysis model, which orchestrates failure prediction, checkpointing, checkpoint frequency, and proactive live migration along with the additional benefit of Burst Buffers (BB). It effectively reduces the overheads due to failures, checkpointing, and recovery. Simulation results for the Summit supercomputer yield a reduction of ∼20%-86% in application overhead due to BBs, orchestrated failure prediction, and migration. We also observe a ∼29% decrease in checkpoint writes to BBs, which can increase the longevity of the BB storage devices.