rt-muse: measuring real-time characteristics of execution platforms

Abstract

Operating systems code is often developed according to principles like simplicity, low overhead, and low memory footprint. Schedulers are no exceptions. A scheduler is usually developed with flexibility in mind, and this restricts the ability to provide real-time guarantees. Moreover, even when schedulers can provide realtime guarantees, it is unlikely that these guarantees are properly quantified using theoretical analysis that carries on to the implementation. To be able to analyze the guarantees offered by operating systems' schedulers, we developed a publicly available tool that analyzes timing properties extracted from the execution of a set of threads and computes the lower and upper bounds to the supply function offered by the execution platform, together with information about migrations and statistics on execution times. rt-muse evaluates the impact of many application and platform characteristics including the scheduling algorithm, the amount of available resources, the usage of shared resources, and the memory access overhead. Using rt-muse, we show the impact of Linux scheduling classes, shared data and application parallelism, on the delivered computing capacity. The tool provides useful insights on the runtime behavior of the applications and scheduler. In the reported experiments, rt-muse detected some issues arising with the real-time

Self-Aware Scheduling for Mixed-Criticality Component-Based Systems

“…tracing, it is worth mentioning that many kernels provide built-in mechanisms for lightweight instrumentation and for extracting traces, such as the Ftrace 2 for Linux or the QNX System Analysis Toolkit [39]. Ftrace is, e.g., used by rtmuse [40] for extracting supply bound functions from different scheduling implementations in Linux.…”

Real-Time Performance and Response Latency Measurements of Linux Kernels on Single-Board Computers

“…Standard benchmark tools such as cyclictest [10] could have also been used. However, this benchmark is difficult to extend and does not combine different types of operations [11]. As a result, taking in consideration the specific hardware platforms under investigation and the aimed real-time applications with certain constraints and requirements (e.g., high priorities, locks of memory pages, high-resolution timers, and specific metrics measurements), it was decided as the most optimal approach to build our own new measurements software.…”