Towards optimal priority assignments for real-time tasks with probabilistic arrivals and execution times

In this paper we present the problem of optimal priority assignments in fixed priority preemptive single processor systems where tasks have probabilistic arrivals and execution times.We show that Rate Monotic is not optimal for our problem

Towards an analysis framework for tasks with probabilistic execution times and probabilistic inter-arrival times

In this paper we investigate the problem of calculating the response time distribution for real-time tasks with probabilistic worst-case execution times, probabilistic inter-arrival times and probabilistic deadlines. We propose a definition for the probabilistic deadlines and a first discussion on the response time calculation

Analysis and Simulation Tools for Probabilistic Real-Time Systems

International audienceIn this paper we present two tools meant to simulate and analyze probabilistic real-time task sets. That is, tasks sets which have their timing parameters represented by discrete probabilistic distributions. We describe the main features of each tool and provide configuration details necessary to use them. The two tools are compared, pointing out the advantages and disadvantages of each one, so that interested users can make an informed choice regarding which tool best fits their needs. Both tools are open source and freely available. One of the main objectives of this paper is to make these tools available to the real-time systems research community, which is also invited to participate in their improvements, by giving feedback and even extending the implementations

Probabilistic Analysis for Mixed Criticality Scheduling with SMC and AMC

This paper introduces probabilistic analysis for fixed priority preemptive scheduling of mixed criticality systems on a uniprocessor using the Adaptive Mixed Criticality (AMC) and Static Mixed Criticality (SMC) schemes. We compare this analysis to the equivalent deterministic methods, highlighting the performance gains that can be obtained by utilising more detailed information about worst-case execution time estimates described in terms of probability distributions

Delay Analysis of AVB traffic in Time-Sensitive Networks (TSN)

International audienceFuture autonomous vehicles and ADAS (Advanced Driver Assistance Systems) need real-time audio and video transmission together with control data traac (CDT). Audio/video stream delay analysis has been largely investigated in AVB (Audio Video Bridging) context, but not yet with the presence of the CDT in the new TSN context. In this paper we present a local delay analysis of AVB frames under hierarchical scheduling of credit-based shaping and time-aware shaping on TSN switches. We present the eeects of time aware shaping on AVB traac, how it changes the relative order of transmission of frames leading to bursts and worst case scenarios for lower priority streams. We also show that these bursts are upper-bounded by the Credit-Bases Shaper, hence the worst-case transmissions delay of a given stream is also upper-bounded. We present the analysis to compute the worst case delay for a frame, as well as the feasibility condition necessary for the analysis to be applied. Our methods (analysis and simulation) are applied to an automotive use case, which is deened within the Eurostars RETINA project, and where both control data traac and AVB traac must be guaranteed. CCS CONCEPTS • Computer systems organization →Embedded systems; • Networks →Network reliability

Probabilistic Analysis for Mixed Criticality Systems using Fixed Priority Preemptive Scheduling

International audienceThis paper introduces probabilistic analysis for fixed priority preemptive scheduling of mixed criticality systems on a uniprocessor using the Adaptive Mixed Criticality (AMC) and Static Mixed Criticality (SMC) schemes. We compare this analysis to existing deterministic methods, highlighting the performance gains that can be obtained by utilising more detailed information about worst-case execution time estimates described in terms of probability distributions. Besides improvements in schedulability, we also demonstrate signiicant gains in terms of the budgets that can be allocated to LO-criticality tasks. A preliminary version [26] of the research described in this paper was published in the Workshop on Mixed Criticality Systems (WMC) in 2016. In this paper, we correct the analysis given in [26], ensuring that the schedulability of HI-criticality tasks does not depend on the behavior of LO-criticality tasks. Further, we provide an alternative analysis (in Section 4.4) and show how support for LO-criticality tasks can be improved via increased execution time budgets (in Section 4.6)

PROARTIS: Probabilistically analyzable real-time systems

Static timing analysis is the state-of-the-art practice of ascertaining the timing behavior of currentgeneration 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. © 2013 ACM.Peer Reviewe

PROARTIS: Probabilistically Analysable Real-Time Systems

Static Timing Analysis is the state-of-the-art practice to ascertain the timing behaviour 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) detail information on the hardware to develop an accurate model of its execution latency as well as (2) knowledge of the timing behaviour 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 paper we present Probabilistic Timing Analysis, a novel approach to the analysis of the timing behaviour 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-safe and tight WCET estimations

Probabilistic analysis of real-time systems

Les systèmes embarqués temps réel critiques intègrent des architectures complexes qui évoluent constamment afin d'intégrer des nouvelles fonctionnalités requises par les utilisateurs finaux des systèmes (automobile, avionique, ferroviaire, etc.). Ces nouvelles architectures ont un impact direct sur la variabilité du comportement temporel des systèmes temps réel. Cette variabilité entraîne un sur-approvisionnement important si la conception du système est uniquement basée sur le raisonnement pire cas. Approches probabilistes proposent des solutions basées sur la probabilité d'occurrence des valeurs les plus défavorables afin d'éviter le sur-approvisionnement, tout en satisfaisant les contraintes temps réel. Les principaux objectifs de ce travail sont de proposer des nouvelles techniques d'analyse des systèmes temps réel probabilistes et des moyens de diminuer la complexité de ces analyses, ainsi que de proposer des algorithmes optimaux d'ordonnancement à priorité fixe pour les systèmes avec des temps d'exécution décrits par des variables aléatoires. Les résultats que nous présentons dans ce travail ont été prouvés surs et à utiliser pour les systèmes temps réel durs, qui sont l'objet principal de notre travail. Notre analyse des systèmes avec plusieurs paramètres probabilistes a été démontrée considérablement moins pessimiste que d'autres types d'analyses. Cet analyse combinée avec des algorithmes d'ordonnancement optimaux appropriées pour les systèmes temps réel probabilistes peut aider les concepteurs de systèmes à mieux apprécier la faisabilité d'un système, en particulier de ceux qui sont jugé irréalisable par des analyses/algorithmes d'ordonnancement déterministesCritical real-time embedded systems integrate complex architectures that evolve constantly in order to provide new functionality required by the end users of the systems (automotive, avionics, railway, etc). These new architectures have a direct impact on the variability of the timing behavior of the real-time system. This variability leads to important over-provisioning if the design of the system is based only on worst case reasoning. Probabilistic approaches propose solutions are based on the probability of occurrence of the worst case values in order to avoid over provisioning while satisfying real-time constraints. The main objectives of this work are new analysis techniques for probabilistic real-time systems and ways of decreasing the complexity of these analyses, as well as to propose optimal fixed priority scheduling algorithms for systems that have variability at the level of execution times. The results that we provide in this work have been proved applicable to hard real-time systems, which are the main focus of our work. Our proposed analysis for systems with multiple probabilistic parameters has been shown to greatly decrease the pessimism introduced by other types of analyses. This type of analysis combined with the proper optimal scheduling algorithms for probabilistic real-time system help the system designers to better appreciate the feasibility of a system, especially of those that are deemed unfeasible by deterministic analyses/scheduling algorithm

Analyse probabiliste des systèmes temps réel

Critical real-time embedded systems integrate complex architectures that evolve constantly in order to provide new functionality required by the end users of the systems (automotive, avionics, railway, etc). These new architectures have a direct impact on the variability of the timing behavior of the real-time system. This variability leads to important over-provisioning if the design of the system is based only on worst case reasoning. Probabilistic approaches propose solutions are based on the probability of occurrence of the worst case values in order to avoid over provisioning while satisfying real-time constraints. The main objectives of this work are new analysis techniques for probabilistic real-time systems and ways of decreasing the complexity of these analyses, as well as to propose optimal xed priority scheduling algorithms for systems that have variability at the level of execution times. The results that we provide in this work have been proved applicable to hard real-time systems, which are the main focus of our work. Our proposed analysis for systems with multiple probabilistic parameters has been shown to greatly decrease the pessimism introduced by other types of analyses. This type of analysis combined with the proper optimal scheduling algorithms for probabilistic real-time system help the system designers to better appreciate the feasibility of a system, especially of those that are deemed unfeasible by deterministic analyses/scheduling algorithms.Les systèmes embarqués temps réel critiques intègrent des architectures complexes qui évoluent constamment a n d'intégrer des nouvelles fonctionnalités requises par les utilisateurs naux des systèmes (automobile, avionique, ferroviaire, etc.). Ces nouvelles architectures ont un impact direct sur la variabilité du comportement temporel des systèmes temps réel. Cette variabilité entraîne un sur-approvisionnement important si la conception du système est uniquement basée sur le raisonnement pire cas. Approches probabilistes proposent des solutions basées sur la probabilité d'occurrence des valeurs les plus défavorables a n d'éviter le sur-approvisionnement, tout en satisfaisant les contraintes temps réel. Les principaux objectifs de ce travail sont de proposer des nouvelles techniques d'analyse des systèmes temps réel probabilistes et des moyens de diminuer la complexité de ces analyses, ainsi que de proposer des algorithmes optimaux d'ordonnancement á priorité xe pour les systèmes avec des temps d'exécution décrits par des variables aléatoires. Les résultats que nous présentons dans ce travail ont été prouvés surs et á utiliser pour les systèmes temps réel durs, qui sont l'objet principal de notre travail. Notre analyse des systèmes avec plusieurs paramètres probabilistes a été démontrée considérablement moins pessimiste que d'autres types d'analyses. Cette analyse combinée avec des algorithmes d'ordonnancement optimaux appropriées pour les systèmes temps réel probabilistes peut aider les concepteurs de systèmes á mieux apprécier la faisabilité d'un systéme, en particulier de ceux qui sont jugé irréalisable par des analyses/algorithmes d'ordonnancement déterministes