Probabilistic Analysis of Predictability in Discrete Event Systems

International audiencePredictability is a key property allowing one to expect in advance the occurrence of a fault in a system based on its observed events. Existing works give a binary answer to the question of knowing whether a system is predictable or not. In this paper, we consider discrete event systems where probabilities of the transitions are available. We show how to take advantage of this information to perform a Markov chain based analysis and extract a variety of probability values that give a finer appreciation of the degree of predictability. This analysis is particularly important in case of non predictable systems. We consider a "light" analysis that focuses only on predictability as well as a "deep" analysis that handles in a uniform framework both predictability and diagnosability

Compositional Verification for Timed Systems Based on Automatic Invariant Generation

We propose a method for compositional verification to address the state space explosion problem inherent to model-checking timed systems with a large number of components. The main challenge is to obtain pertinent global timing constraints from the timings in the components alone. To this end, we make use of auxiliary clocks to automatically generate new invariants which capture the constraints induced by the synchronisations between components. The method has been implemented in the RTD-Finder tool and successfully experimented on several benchmarks

Modeling Cache Coherence to Expose

International audienceTo facilitate programming, most multi-core processors feature automated mechanisms maintaining coherence between each core's cache. These mechanisms introduce interference, that is, delays caused by concurrent access to a shared resource. This type of interference is hard to predict, leading to the mechanisms being shunned by real-time system designers, at the cost of potential benefits in both running time and system complexity. We believe that formal methods can provide the means to ensure that the effects of this interference are properly exposed and mitigated. Consequently, this paper proposes a nascent framework relying on timed automata to model and analyze the interference caused by cache coherence

Modeling Cache Coherence to Expose Interference

To facilitate programming, most multi-core processors feature automated mechanisms maintaining coherence between each core\u27s cache. These mechanisms introduce interference, that is, delays caused by concurrent access to a shared resource. This type of interference is hard to predict, leading to the mechanisms being shunned by real-time system designers, at the cost of potential benefits in both running time and system complexity. We believe that formal methods can provide the means to ensure that the effects of this interference are properly exposed and mitigated. Consequently, this paper proposes a nascent framework relying on timed automata to model and analyze the interference caused by cache coherence

P3b reflects periodicity in linguistic sequences

Temporal predictability is thought to affect stimulus processing by facilitating the allocation of attentional resources. Recent studies have shown that periodicity of a tonal sequence results in a decreased peak latency and a larger amplitude of the P3b compared with temporally random, i.e., aperiodic sequences. We investigated whether this applies also to sequences of linguistic stimuli (syllables), although speech is usually aperiodic. We compared aperiodic syllable sequences with two temporally regular conditions. In one condition, the interval between syllable onset was fixed, whereas in a second condition the interval between the syllables’ perceptual center (p-center) was kept constant. Event-related potentials were assessed in 30 adults who were instructed to detect irregularities in the stimulus sequences. We found larger P3b amplitudes for both temporally predictable conditions as compared to the aperiodic condition and a shorter P3b latency in the p-center condition than in both other conditions. These findings demonstrate that even in acoustically more complex sequences such as syllable streams, temporal predictability facilitates the processing of deviant stimuli. Furthermore, we provide first electrophysiological evidence for the relevance of the p-center concept in linguistic stimulus processing

A compositional monitoring framework for hard real-time systems

Runtime Monitoring of hard real-time embedded systems is a promising technique for ensuring that a running system respects timing constraints, possibly combined with faults originated by the software and/or hardware. This is particularly important when we have real-time embedded systems made of several components that must combine different levels of criticality, and different levels of correctness requirements. This paper introduces a compositional monitoring framework coupled with guarantees that include time isolation and the response time of a monitor for a predicted violation. The kind of monitors that we propose are automatically generated by synthesizing logic formulas of a timed temporal logic, and their correctness is ensured by construction.This work was partially supported by National Funds through FCT (Portuguese Foundation for Science and Technology) and by ERDF (European Regional Development Fund) through COMPETE (Operational Programme ’Thematic Factors of Competitiveness’), within projects Ref. FCOMP-01-0124-FEDER-022701 (CISTER), FCOMP-01-0124- FEDER-015006 (VIPCORE) and FCOMP-01-0124-FEDER-020486 (AVIACC)

Completeness, robustness, and safety in real-time software requirements specification

This paper presents an approach to providing a rigorous basis for ascertaining whether or not a given set of software requirements is internally complete, i.e., closed with respect to questions and inferences that can be made on the basis of information included in the specification. Emphasis is placed on aspects of software requirements specifications that previously have not been adequately handled, including timing abstractions, safety, and robustness

Modeling, Analysis, and Hard Real-time Scheduling of Adaptive Streaming Applications

In real-time systems, the application's behavior has to be predictable at compile-time to guarantee timing constraints. However, modern streaming applications which exhibit adaptive behavior due to mode switching at run-time, may degrade system predictability due to unknown behavior of the application during mode transitions. Therefore, proper temporal analysis during mode transitions is imperative to preserve system predictability. To this end, in this paper, we initially introduce Mode Aware Data Flow (MADF) which is our new predictable Model of Computation (MoC) to efficiently capture the behavior of adaptive streaming applications. Then, as an important part of the operational semantics of MADF, we propose the Maximum-Overlap Offset (MOO) which is our novel protocol for mode transitions. The main advantage of this transition protocol is that, in contrast to self-timed transition protocols, it avoids timing interference between modes upon mode transitions. As a result, any mode transition can be analyzed independently from the mode transitions that occurred in the past. Based on this transition protocol, we propose a hard real-time analysis as well to guarantee timing constraints by avoiding processor overloading during mode transitions. Therefore, using this protocol, we can derive a lower bound and an upper bound on the earliest starting time of the tasks in the new mode during mode transitions in such a way that hard real-time constraints are respected.Comment: Accepted for presentation at EMSOFT 2018 and for publication in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) as part of the ESWEEK-TCAD special issu