Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Weaklyhard. jl: Scalable analysis of weakly-hard constraints

Vreman, Nils LU ; Pates, Richard LU and Maggio, Martina LU (2022)
Abstract
Weakly-hard models have been used to analyse real-time systems subject to patterns of deadline hits and misses. However, the tools that are available in the literature have a set of shortcomings. The analysis they offer is limited to a single weaklyhard constraint and to patterns that specify the number of misses, rather than the number of hits. Furthermore, the scalability of the tools is limited, effectively making it hard to address systems where deadline misses are really sporadic events. In this paper we present WeaklyHard.jl, a scalable tool to analyse a set of weakly hard constraints belonging to all the four types of weakly hard models. To achieve scalability, we exploit novel dominance relations between weakly-hard constraints,... (More)
Weakly-hard models have been used to analyse real-time systems subject to patterns of deadline hits and misses. However, the tools that are available in the literature have a set of shortcomings. The analysis they offer is limited to a single weaklyhard constraint and to patterns that specify the number of misses, rather than the number of hits. Furthermore, the scalability of the tools is limited, effectively making it hard to address systems where deadline misses are really sporadic events. In this paper we present WeaklyHard.jl, a scalable tool to analyse a set of weakly hard constraints belonging to all the four types of weakly hard models. To achieve scalability, we exploit novel dominance relations between weakly-hard constraints, based on deadline hits. We provide experimental evidence of the tool’s scalability, compared to the state-of-the-art for a single constraint, a thorough investigation of hit-based weakly-hard constraints, and a sensitivity analysis to constraint set parameters. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Weakly-Hard, Toolbox
host publication
2022 IEEE 28th Real-Time and Embedded Technology and Applications Symposium (RTAS)
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85132019580
ISBN
978-1-6654-9998-9
978-1-6654-9999-6
DOI
10.1109/RTAS54340.2022.00026
project
Towards Adaptively Morphing Embedded Systems
language
English
LU publication?
yes
id
2e369097-8178-4563-803b-7f85e8ae6076
date added to LUP
2022-07-21 10:50:06
date last changed
2024-04-18 10:18:40
@inproceedings{2e369097-8178-4563-803b-7f85e8ae6076,
  abstract     = {{Weakly-hard models have been used to analyse real-time systems subject to patterns of deadline hits and misses. However, the tools that are available in the literature have a set of shortcomings. The analysis they offer is limited to a single weaklyhard constraint and to patterns that specify the number of misses, rather than the number of hits. Furthermore, the scalability of the tools is limited, effectively making it hard to address systems where deadline misses are really sporadic events. In this paper we present WeaklyHard.jl, a scalable tool to analyse a set of weakly hard constraints belonging to all the four types of weakly hard models. To achieve scalability, we exploit novel dominance relations between weakly-hard constraints, based on deadline hits. We provide experimental evidence of the tool’s scalability, compared to the state-of-the-art for a single constraint, a thorough investigation of hit-based weakly-hard constraints, and a sensitivity analysis to constraint set parameters.}},
  author       = {{Vreman, Nils and Pates, Richard and Maggio, Martina}},
  booktitle    = {{2022 IEEE 28th Real-Time and Embedded Technology and Applications Symposium (RTAS)}},
  isbn         = {{978-1-6654-9998-9}},
  keywords     = {{Weakly-Hard; Toolbox}},
  language     = {{eng}},
  month        = {{05}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Weaklyhard. jl: Scalable analysis of weakly-hard constraints}},
  url          = {{http://dx.doi.org/10.1109/RTAS54340.2022.00026}},
  doi          = {{10.1109/RTAS54340.2022.00026}},
  year         = {{2022}},
}