Failure classification with respect to detection

Bondavalli, Andrea; Simoncini, L.

doi:10.1109/ftdcs.1990.138293

Cited by 39 publications

(30 citation statements)

References 2 publications

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“…Laprie et al in [63] define the fundamental concept of a failure as: a failure occurs when an actual running system deviates from its specified behavior. Several proposals for failure classification are presented from different perspectives, such as [13,63]. But none of them introduce the failures as a hierarchical classification with chained behavior.…”

Section: Classification Of Failure Typesmentioning

confidence: 99%

On the Quality of Service of Crash-Recovery Failure Detectors

Hillston

Anderson

2007

37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07)

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This thesis presents the results of an investigation into the failure detection problem.We consider the specific case of the Quality of Service (QoS) of crash failure detection.In contrast to previous work, we address the crash failure detection problem when the monitored target is resilient and recovers after failure. To the best of our knowledge, this is the first work to provide an analysis of crash-recovery failure detection from the QoS perspective.We develop a probabilistic model of the behavior of a crash-recovery target, i.e. one which has the ability to recover from the crash state. We show that the fail-free run and the crash-stop run are special cases of the crash-recovery run with mean time to failure (MTTF) approaching to infinity and mean time to recovery (MTTR) approaching to infinity, respectively. We extend the previously published QoS metrics to allow the measurement of the recovery speed, and the definition of the completeness property of a failure detector. Then, the impact of the dependability of the crash-recovery target on the QoS bounds for such a crash-recovery failure detector is analyzed using general dependability metrics, such as MTTF and MTTR, based on an approximate probabilistic model of the two-process failure detection system. Then according to our approximate model, we show how to estimate the failure detector's parameters to achieve a required QoS, based on Chen et al.'s NFD-S algorithm analytically, and how to execute the configuration procedure of this crash-recovery failure detector.In order to make the failure detector adaptive to the target's crash-recovery behavior and enable the autonomy of the monitoring procedure, we propose two types of recovery detection protocols. One is a reliable recovery detection protocol, which can guarantee to detect each occurring failure and recovery by adopting persistent storage.The other is a lightweight recovery detection protocol, which does not guarantee to detect every failure and recovery but which reduces the system overhead. Both of these recovery detection protocols improve the completeness without reducing the other QoS aspects of a failure detector. In addition, we also demonstrate how to estimate the inputs, such as the dependability metrics, using the failure detector itself.In order to evaluate our analytical work, we simulate the following failure detection al- This conforms well to our models and analysis. We show that in the case of reasonable long MTTF, the NFD-S algorithm with the lightweight recovery detection protocol exhibits better QoS than the NFD-S algorithm for the completeness of a crash-recovery failure detector, and similarly for other QoS metrics.

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Section: Classification Of Failure Typesmentioning

confidence: 99%

On the Quality of Service of Crash-Recovery Failure Detectors

Hillston

Anderson

2007

37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07)

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“…Meanwhile, prototypical tools have been presented that generate CFT structures from SysML [29] and from Simulink [30] system models. In [29], the failure keywords from HAZOP were used with the failure classification scheme from [31] to propose the unified hierarchical failure type system shown in Figure 2, which will serve as the contractbased failure classification in Section 4.2. [29] further proposes a sub-refinement of failure classes using attributes (e.g., to what extent is it too high?).…”

Section: Component-oriented Safety Analysismentioning

confidence: 99%

Contract-Based Design of Embedded Systems Integrating Nominal Behavior and Safety

Kaiser¹,

Weber

Oertel

et al. 2015

CSIMQ

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Abstract. The distributed design process for safety-critical embedded systems has become an increasingly difficult challenge: Electronic Control Units (ECUs) in vehicles, for instance, participate in many vehicle functions, while each vehicle function, in turn, is spread across several ECUs. Many suppliers participate in systems design and many partial functions are reused from past projects, not always knowing the assumptions at the time of their development. In particular, efficient allocation of safety mechanisms and a sound safety case are difficult tasks for original equipment manufacturers (OEMs). Contractbased development has gained popularity as an approach for supporting distributed development by explicitly annotating assumptions and guarantees to components, but an integrated process covering specification of nominal behavior and safety has not been described so far. We present such an integrated development approach that encompasses the systematic breakdown of nominal system behavior using contracts, the consistent derivation of safety analysis by interpreting several types of contract violations as a specification for failure modes, and the subsequent integration of safety mechanisms that cover these failure modes through safety contracts. The approach equally fits hardware and software and is therefore applicable on the system level. We demonstrate it by an electric drive example. The extensibility of our approach towards Cyber Physical Systems, which compose themselves at runtime, is briefly outlined at the end of the article.

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“…Our approach builds upon the failure concepts originally introduced in [1,3,20]. For the sake of readability, we denote the i th replica of a given node by R i .…”

Section: System Modelmentioning

confidence: 99%

VTV - A Voting Strategy for Real-Time Systems

Aysan

Punnekkat

Dobrin

2008

2008 14th IEEE Pacific Rim International Symposium on Dependable Computing

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Real-time applications typically have to satisfy high dependability requirements and require fault tolerance in both value and time domains. A widely used approach to ensure fault tolerance in dependable systems is the N-modular redundancy (NMR) which typically uses a majority voting mechanism. However, NMR primarily focuses on producing the correct value, without taking into account the time dimension. In this paper, we propose a new approach, Voting on Time and Value (VTV), applicable to real-time systems, which extends the modular redundancy approach by explicitly considering both value and timing failures, such that correct value is produced at a correct time, under specified assumptions. We illustrate our voting approach by instantiating it in the context of the well-known triple modular redundancy (TMR) approach. Further, we present a generalized version targeting NMR that enables a high degree of customization from the user perspective.

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Failure classification with respect to detection

Cited by 39 publications

References 2 publications

On the Quality of Service of Crash-Recovery Failure Detectors

On the Quality of Service of Crash-Recovery Failure Detectors

Contract-Based Design of Embedded Systems Integrating Nominal Behavior and Safety

VTV - A Voting Strategy for Real-Time Systems

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