Automating efficient variable-grained resiliency for low-power IoT systems

Abstract

New trends in edge computing encourage pushing more of the compute and analytics to the outer edge and processing most of the data locally. We explore how to transparently provide resiliency for heavy duty edge applications running on low-power devices that must deal with frequent and unpredictable power disruptions. Complicating this process further are (a) memory usage restrictions in tiny low-power devices, that affect not only performance but efficacy of the resiliency techniques, and (b) differing resiliency requirements across deployment environments. Nevertheless, an application developer wants the ability to write an application once, and have it be reusable across all low-power platforms and across all different deployment settings. In response to these challenges, we have devised a transparent roll-back recovery mechanism that performs incremental checkpoints with minimal execution time overhead and at variable granularities. Our solution includes the co-design of firmware, runtime and compiler transformations for providing seamless fault-tolerance, along with an auto-tuning layer that automatically generates multiple resilient variants of an application. Each variant spreads application’s execution over atomic transactional regions of a certain granularity. Variants with smaller regions provide better resiliency, but incur higher overhead; thus, there is no single best option, but rather a Pareto optimal set of configurations. We apply these strategies across a variety of edge device applications and measure the execution time overhead of the framework on a TI MSP430FR6989. When we restrict unin- terrupted atomic intervals to 100ms, our framework keeps geomean overhead below 2.48x.