PROARTIS: Probabilistically Analyzable Real-Time System - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles ACM Transactions on Embedded Computing Systems (TECS) Year : 2013

PROARTIS: Probabilistically Analyzable Real-Time System

Abstract

Static timing analysis is the state-of-the-art practice of ascertaining the timing behavior of current-generation real-time embedded systems. The adoption of more complex hardware to respond to the increasing demand for computing power in next-generation systems exacerbates some of the limitations of static timing analysis. In particular, the effort of acquiring (1) detailed information on the hardware to develop an accurate model of its execution latency as well as (2) knowledge of the timing behavior of the program in the presence of varying hardware conditions, such as those dependent on the history of previously executed instructions. We call these problems the timing analysis walls. In this vision-statement article, we present probabilistic timing analysis, a novel approach to the analysis of the timing behavior of next-generation real-time embedded systems. We show how probabilistic timing analysis attacks the timing analysis walls; we then illustrate the mathematical foundations on which this method is based and the challenges we face in the effort of efficiently implementing it. We also present experimental evidence that shows how probabilistic timing analysis reduces the extent of knowledge about the execution platform required to produce probabilistically accurate WCET estimations.
No file

Dates and versions

hal-00920535 , version 1 (18-12-2013)

Identifiers

Cite

Francisco J. Cazorla, Eduardo Quinones, Tullio Vardanega, Liliana Cucu-Grosjean, Benoit Triquet, et al.. PROARTIS: Probabilistically Analyzable Real-Time System. ACM Transactions on Embedded Computing Systems (TECS), 2013, 12 (2s), ⟨10.1145/2465787.2465796⟩. ⟨hal-00920535⟩
482 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More