An Empirical Study of Performance Interference: Timing Violation Patterns and Impacts

Abstract

Multi-core platforms are becoming increasingly prevalent in cyber-physical systems such as automobiles and robots. However, contention for shared resources makes it chal-lenging to guarantee timing predictability. Existing studies have primarily focused on characterizing the extent to which such interference can induce delays (usually from an adversarial perspective). Unfortunately, less is understood on the physical impacts of these timing delays in different cyber-physical plat-forms. In this paper, we fill this gap by providing an empirical examination of the end-to-end effects of performance interference on real-world applications. We analyze the root causes of harmful interference and summarize potential implementation pitfalls. To automate this process, we introduce TimeTrap, a tool that analyzes performance interference in autonomous systems through the lens of control outcome. To understand the extent to which timing interference may cause control deviations, TimeTrap has to strategically leverage different magnitudes of resource contention to trigger targeted deadline miss patterns. Through this exercise, we found that a naive approach that maximizes task latency via performance interference may fail to trigger worst-case outcomes (i.e. physical damages) due to built-in fail-safe mechanisms. As a result, delays have to be induced in a stealthy manner to avoid triggering fail-safes. To achieve this, TimeTrap first employs a system that actively injects fine-grained delays into the target software, adjusting the duration based on measured feedback. Second, TimeTrap leverages predictability in CPS execution patterns and resource usage to automatically tune its aggressor workloads, matching these patterns to achieve targeted interference and execution delays in a victim. We evaluate TimeTrap on two physical-world platforms and six platforms in a hardware-in-the-Ioop simulation environment, including robotic arms, UGVs, UAVs, self-driving cars, and humanoid robots. These studies demonstrate that an interference-based attack surface exists in different stages of the CPS pipeline, from perception to planning and control.