Breaking the Limits of Redundancy Systems Analysis

12/11/2019
by   Clemens Dubslaff, et al.
0

Redundancy mechanisms such as triple modular redundancy protect safety-critical components by replication and thus improve systems fault tolerance. However, the gained fault tolerance comes along with costs to be invested, e.g., increasing execution time, energy consumption, or packaging size, for which constraints have to be obeyed during system design. This turns the question of finding suitable combinations of components to be protected into a challenging task as the number of possible protection combinations grows exponentially in the number of components. We propose family-based approaches to tackle the combinatorial blowup in redundancy systems modeling and analysis phases. Based on systems designed in SIMULINK we show how to obtain models that include all possible protection combinations and present a tool chain that, given a probabilistic error model, generates discrete Markov chain families. Using symbolic techniques that enable concise family representation and analysis, we show how SIMULINK models of realistic size can be protected and analyzed with a single family-based analysis run while a one-by-one analysis of each protection combination would clearly exceed any realistic time constraints.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

01/27/2019

Majority and Minority Voted Redundancy for Safety-Critical Applications

A new majority and minority voted redundancy (MMR) scheme is proposed th...
04/28/2020

Iterative Variable Reordering: Taming Huge System Families

For the verification of systems using model-checking techniques, symboli...
03/22/2018

Securing Conditional Branches in the Presence of Fault Attacks

In typical software, many comparisons and subsequent branch operations a...
04/01/2021

Investigating the Reliability in Three RAID Storage Models and Effect of Ordering Replicas on Disks

One of the most important parts of cloud computing is storage devices, a...
05/17/2018

Deep-learning Based Modeling of Fault Detachment Stability for Power Grid

The project intends to model the stability of power system with a deep l...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.