Chaos: a System for Criticality-Aware, Multi-Core Coordination

The incentive to minimize size, weight and power (SWaP) in embedded systems has driven the consolidation both of disparate processors into single multi-core systems, and of software of various functionalities onto shared hardware. These consolidated systems must address a number of challenges that include providing strong isolation of the highly-critical tasks that impact human or equipment safety from the more feature-rich, less trustworthy applications, and the effective use of spare system capacity to increase functionality. The coordination between high and low criticality tasks is particularly challenging, and is common, for example, in autonomous vehicles where controllers, planners, sensor fusion, telemetry processing, cloud communication, and logging all must be orchestrated together. In such a case, they must share the code of the software run-time system that manages resources, and provides communication abstractions. This paper presents the Chaos system that uses devirtualization to extract high-criticality tasks from shared software environments, thus alleviating interference, and runs them in a minimal runtime. To maintain access to more feature-rich software, Chaos provides low-level coordination through proxies that tightly bound the overheads for coordination. We demonstrate Chaos's ability to scalably use multiple cores while maintaining high isolation with controlled inter-criticality coordination. For a sensor/actuation loop in satellite software experiencing inter-core interference, Chaos lowers processing latency by a factor of 2.7, while reducing worst-case by a factor 3.5 over a real-time Linux variant.

I. Introduction

Embedded systems are increasingly required to provide both complicated feature-sets, and high-confidence in the correctness of mission-critical computations. From self-driving cars and Unmanned Aerial Vehicles (UAVs) to CubeSats, soft-ware systems must provide more communication facilities and more complicated sensor fusion, while still maintaining the expected physical dynamics of the systems. This challenge is complicated by the trend in these and other domains that functionalities traditionally performed by disparate computational elements are consolidated onto less expensive and more capable multi-core, commodity processors. Unfortunately, current systems have difficulty in both supporting feature rich, general computation provided by large amounts of code, and the high-confidence physical control that often requires software simplicity while also providing high resource utilization.