Jimenez Gil, Samuel, Bate, Iain orcid.org/0000-0003-2415-8219, Lima, George et al. (3 more authors) (2017) Open Challenges for Probabilistic Measurement-Based Worst-Case Execution Time. IEEE Embedded Systems Letters. 7942057. pp. 69-72. ISSN 1943-0663
Abstract
The worst-case execution time (WCET) is a critical parameter describing the largest value for the execution time of programs. Even though such a parameter is very hard to attain, it is essential as part of guaranteeing a real-time system meets its timing requirements. The complexity of modern hardware has increased the challenges of statically analyzing the WCET and reduced the reliability of purely measuring the WCET. This has led to the emergence of probabilistic WCETs (pWCETs) analysis as a viable technique. The low probability of appearance of large execution times of a program has motivated the utilization of rare events theory like extreme value theory (EVT). As pWCET estimation based on EVT has matured as a discipline, a number of open challenges have become apparent when applying the existing approaches. This letter enumerates key challenges while establishing a state of the art of EVT-based pWCET estimation methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works |
Keywords: | Embedded software,real-time systems,statistical distributions |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 03 Oct 2017 09:30 |
Last Modified: | 16 Oct 2024 14:05 |
Published Version: | https://doi.org/10.1109/LES.2017.2712858 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/LES.2017.2712858 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:121926 |