research-article

PROARTIS: Probabilistically Analyzable Real-Time Systems

Authors:

Francisco J. Cazorla,

Eduardo Quiñones,

Tullio Vardanega,

Benoit Triquet,

Guillem Bernat,

Michael Houston,

Luca Santinelli,

Leonidas Kosmidis,

Dorin MaximAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 12, Issue 2s

Article No.: 94, Pages 1 - 26

https://doi.org/10.1145/2465787.2465796

Published: 01 May 2013 Publication History

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.

References

[1]

Abeni, L. and Buttazzo, G. 1998. Integrating multimedia applications in hard real-time systems. In Proceedings of the 19th IEEE Real-Time Systems Symposium (RTSS98). 4--13.

Digital Library

[2]

Albrecht, K., Raimund, K., and Puschner, P. P. 2010. Avoiding timing anomalies using code transformations. In Proceedings of ISORC.

Digital Library

[3]

Berger, E. D. and Zorn, B. G. 2006. DieHard: Probabilistic memory safety for unsafe languages. In Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation. ACM Press, New York, 158--168.

Digital Library

[4]

Bernat, G. and Newby, M. 2006. Probabilistic WCET analysis, an approach using copulas. J. Embed. Comput.

Digital Library

[5]

Bernat, G., Colin, A., and Petters, S. 2002. WCET analysis of probabilistic hard real-time systems. In Proceedings of RTSS.

Digital Library

[6]

Binns, P. 2004. Statistical estimation of aperiodic response times when scheduled on top of static timelines. In Proceedings of the 1st International Workshop on Probabilistic Analysis Techniques for Real-Time and Embedded Systems (PARTES’04).

[7]

Charette, R. 2009. This car runs on code. IEEE Spectrum Online. http://spectrum.ieee.org/green-tech/advanced-cars/this-car-runs-on-code.

[8]

Clarke, P. 2011. Automotive chip content growing fast, says gartner. http://www.eetimes.com/electronics-news/4207377/Automotive-chip-content-growing-fast.

[9]

Díaz, J., Garcia, D., Kim, K., Lee, C., Bello, L., López, J. M., and Mirabella, O. 2002. Stochastic analysis of periodic real-time systems. In Proceedings of the 23rd IEEE Real-Time Systems Symposium (RTSS02). 289--300.

Digital Library

[10]

Edgar, S. and Burns, A. 2001. Statistical analysis of WCET for scheduling. In Proceedings of the 22nd IEEE Real-Time Systems Symposium (RTSS’01). 215--225.

Digital Library

[11]

Gardner, M. and Lui, J. 1999. Analyzing stochastic fixed-priority real-time systems. In Proceedings of the 5th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS’99). 44--58.

Digital Library

[12]

Gnedenko, B. 1943. Sur la distribution limite du terme maximum d’une seris aleatoire. Ann. Math. 44, 423--453.

[13]

Griffin, D. and Burns, A. 2010. Realism in statistical analysis of worst case execution times. In Proceedings of the 10th International Workshop on Worst-Case Execution Time Analysis (WCET’10). 44--53.

[14]

Hansen, J., Hissam, S., and Moreno, G. A. 2009. Statistical-based wcet estimation and validation. In Proceedings of the 9th International Workshop on Worst-Case Execution Time (WCET) Analysis.

[15]

Lahiri, K., Raghunathan, A., and Lakshminarayana, G. 2001. LOTTERYBUS: A new high-performance communication architecture for system-on-chip designs. In Proceedings of the 38th annual Design Automation Conference (DAC’01). 15--20.

Digital Library

[16]

Lehoczky, J. 1996. Real-time queueing theory. In Proceedings of the 10th IEEE Real-Time Systems Symposium (RTSS’96). 186--195.

Digital Library

[17]

Lewis, J. T. and Russel, R. 1997. An introduction to large deviations for teletraffic engineers. http://www.statslab.cam.ac.uk/&percnt;7Errw1/ld/LD-tutorial.ps.

[18]

López, J., Díaz, J. L., Entrialgo, J., and Garcia, D. 2008. Stochastic analysis of real-time systems under preemptive priority-driven scheduling. Real-time Syst. 40, 2, 180--207.

Digital Library

[19]

Lundqvist, T. and Stenstrom, P. 1999. Timing anomalies in dynamically scheduled microprocessors. In Proceedings of RTSS.

Digital Library

[20]

MerasaD2.2. 2008. Merasa project. http://www.merasa.org/downloads/Deliverable_2_2.pdf.

[21]

Navet, N., Cucu, L., and Schott, R. 2007. Probabilistic estimation of response times through large deviations. In Proceedings of the WIP Session of the 28th IEEE Real-Time Systems Symposium (RTSS’07).

[22]

PROARTIS. 2010. Probabilistically analyzable real-time systems. http://www.proartis-project.eu/.

[23]

Quinones, E., Berger, E., Bernat, G., and Cazorla, F. 2009. Using randomized caches in probabilistic real-time systems. In Proceedings of the 22nd Euromicro Conference on Real-Time Systems (ECRTS). IEEE, 129--138.

Digital Library

[24]

RapiTime. 2008. RapiTime tool. www.rapitasystems.com.

[25]

Refaat, K. S. and Hladik, P.-E. 2010. Efficient stochastic analysis of real-time systems via random sampling. In Proceedings of ECRTS. 175--183.

Digital Library

[26]

Reineke, J., Wachter, B., Thesing, S., Wilheim, R., Polian, I., Eisinger, J., and Becker, B. 2006. A definition and classification of timing anomalies. In Proceedings of WCET.

[27]

Tia, T., Deng, Z., Shankar, M., Storch, M., Sun, J., Wu, L., and Liu, J. 1995. Probabilistic performance guarantee for real-time tasks with varying computation times. In Proceedings of the 2nd IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS’95). 164--174.

Digital Library

[28]

Wilhelm, R., Engblom, J., Ermedahl, A., Holsti, N., Thesing, S., Whalley, D., Bernat, G., Ferdinand, C., Heckman, R., Mitra, T., Mueller, F., Puant, I., Duschner, P., Staschulat, J., and Stenström, P. 2008. The worst-case execution-time problem---overview of methods and survey of tools. ACM Trans. Embed. Comput. Syst. 7, 1--53.

Digital Library

[29]

Yue, L., Bate, I., Nolte, T., and Cucu-Grosjean, L. 2011. A new way about using statistical analysis of worst-case execution times. In Proceedings of ECRTS.

[30]

Zhou, S. 2010. An efficient simulation algorithm for cache of random replacement policy. In Proceedings of the IFIP International Conference on Network and Parallel Computing (NPC’10).

Digital Library

[31]

Zhu, H., Hansen, J., Lehoczky, J., and Rajkumar, R. 2002. Optimal partitioning for quantized EDF scheduling. Proceedings of the 23rd IEEE Real-time Systems Symposium (RTSS’02). 202--213.

Digital Library

Cited By

Yang HMitra ACui YSundaram SMa XSullivan SVoyles R(2024)A Human-Augmenting Resource/Performance Co-Design Tool for Real-Time Distributed Control SystemsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.339338954:8(5061-5073)Online publication date: Aug-2024
https://doi.org/10.1109/TSMC.2024.3393389
Costa DCuomo LOliveira DSavino IMorelli BMartins JBiasci APinto S(2023)IRQ Coloring and the Subtle Art of Mitigating Interrupt-Generated Interference2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)10.1109/RTCSA58653.2023.00015(47-56)Online publication date: 30-Aug-2023
https://doi.org/10.1109/RTCSA58653.2023.00015
Zhou AXue WXiao YHe BIbrahim SCheng R(2022)Taming System Dynamics on Resource Optimization for Data Processing Workflows: A Probabilistic ApproachIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.309140033:1(231-248)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TPDS.2021.3091400
Show More Cited By

Index Terms

PROARTIS: Probabilistically Analyzable Real-Time Systems

Recommendations

Schedulability analysis of EDF-scheduled embedded real-time systems with resource sharing

Earliest Deadline First (EDF) is the most widely studied optimal dynamic scheduling algorithm for uniprocessor real-time systems. In the existing literature, however, there is no complete exact analysis for EDF scheduling when both resource sharing and ...
Semi-automatic derivation of timing models for WCET analysis
LCTES '10: Proceedings of the ACM SIGPLAN/SIGBED 2010 conference on Languages, compilers, and tools for embedded systems

Embedded systems are widely used for supporting our every day life. In the area of safety-critical systems human life often depends on the system's correct behavior. Many of such systems are hard real-time systems, so that the notion of correctness not ...
Including user-defined timing exception support in FRTL
RTCSA '00: Proceedings of the Seventh International Conference on Real-Time Systems and Applications

In previous papers, we have presented both a new framework for building flexible hard real-time systems (FRTS) and a corresponding run-time support system, called Flexible Real-Time Linux (FRTL). This paper presents how timing exception support can be ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 12, Issue 2s

Special Section on Probabilistic Embedded Computing

May 2013

269 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/2465787

Issue’s Table of Contents

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 01 May 2013

Accepted: 01 November 2011

Revised: 01 October 2011

Received: 01 June 2011

Published in TECS Volume 12, Issue 2s

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

125
Total Citations
View Citations
623
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)2

Reflects downloads up to 04 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yang HMitra ACui YSundaram SMa XSullivan SVoyles R(2024)A Human-Augmenting Resource/Performance Co-Design Tool for Real-Time Distributed Control SystemsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2024.339338954:8(5061-5073)Online publication date: Aug-2024
https://doi.org/10.1109/TSMC.2024.3393389
Costa DCuomo LOliveira DSavino IMorelli BMartins JBiasci APinto S(2023)IRQ Coloring and the Subtle Art of Mitigating Interrupt-Generated Interference2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)10.1109/RTCSA58653.2023.00015(47-56)Online publication date: 30-Aug-2023
https://doi.org/10.1109/RTCSA58653.2023.00015
Zhou AXue WXiao YHe BIbrahim SCheng R(2022)Taming System Dynamics on Resource Optimization for Data Processing Workflows: A Probabilistic ApproachIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.309140033:1(231-248)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TPDS.2021.3091400
Guo ZVaidhun SSatinelli LArefin SWang JYang K(2022)Mixed-Criticality Scheduling Upon Permitted Failure Probability and Dynamic PriorityIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.305323241:1(62-75)Online publication date: Jan-2022
https://doi.org/10.1109/TCAD.2021.3053232
Hapka RChristmann AErnst R(2022)Controlling High-Performance Platform Uncertainties with Timing Diversity2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)10.1109/RTCSA55878.2022.00029(212-219)Online publication date: Aug-2022
https://doi.org/10.1109/RTCSA55878.2022.00029
Rubio-Anguiano LBriz JRamirez-Trevino A(2022)Accounting for Preemption and Migration Costs in the Calculation of Hard Real-Time Cyclic Executives for MPSoCsIEEE Robotics and Automation Letters10.1109/LRA.2022.31864897:3(7990-7997)Online publication date: Jul-2022
https://doi.org/10.1109/LRA.2022.3186489
Park HEaswaran ABorde E(2022)Online cycle detection for models with mode-dependent input and output dependenciesJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2021.102017115:COnline publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1016/j.sysarc.2021.102017
Maxim DCucu-Grosjean LDavis R(2022)Probabilistic AnalysisHandbook of Real-Time Computing10.1007/978-981-287-251-7_9(323-346)Online publication date: 9-Aug-2022
https://doi.org/10.1007/978-981-287-251-7_9
Amalou APuaut IMuller G(2021)WE-HML: hybrid WCET estimation using machine learning for architectures with caches2021 IEEE 27th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)10.1109/RTCSA52859.2021.00011(31-40)Online publication date: Aug-2021
https://doi.org/10.1109/RTCSA52859.2021.00011
Fusi MMazzocchetti FFarres AKosmidis LCanal RCazorla FAbella J(2020)On the Use of Probabilistic Worst-Case Execution Time Estimation for Parallel Applications in High Performance SystemsMathematics10.3390/math80303148:3(314)Online publication date: 1-Mar-2020
https://doi.org/10.3390/math8030314
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents