Architecting Dependable Systems with Proactive Fault Management

Salfner, Felix; Malek, Miroslaw

doi:10.1007/978-3-642-17245-8_8

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…System failures that reduce system availability have lower priority, but higher priority than failures that merely result in inconveniences. This can be achieved with multiple mechanisms like proactive fault management [40] and failure transformation [44].…”

Section: Related Workmentioning

confidence: 99%

Towards Dynamic Dependable Systems Through Evidence-Based Continuous Certification

Faqeh

Fetzer

Hermanns

et al. 2020

Leveraging Applications of Formal Methods, Verification and Validation: Engineering Principles

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Future cyber-physical systems are expected to be dynamic, evolving while already being deployed. Frequent updates of software components are likely to become the norm even for safety-critical systems. In this setting, a full re-certification before each software update might delay important updates that fix previous bugs, or security or safety issues. Here we propose a vision addressing this challenge, namely through the evidence-based continuous supervision and certification of software variants in the field. The idea is to run both old and new variants of component software inside the same system, together with a supervising instance that monitors their behavior. Updated variants are phased into operation after sufficient evidence for correct behavior has been collected. The variants are required to explicate their decisions in a logical language, enabling the supervisor to reason about these decisions and to identify inconsistencies. To resolve contradictory information, the supervisor can run a component analysis to identify potentially faulty components on the basis of previously observed behavior, and can trigger micro-experiments which plan and execute system behavior specifically aimed at reducing uncertainty. We spell out our overall vision, and provide a first formalization of the different components and their interplay. In order to provide efficient supervisor reasoning as well as automatic verification of supervisor properties we introduce SupERLog, a logic specifically designed to this end.

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Section: Related Workmentioning

confidence: 99%

Towards Dynamic Dependable Systems Through Evidence-Based Continuous Certification

Faqeh

Fetzer

Hermanns

et al. 2020

Leveraging Applications of Formal Methods, Verification and Validation: Engineering Principles

View full text Add to dashboard Cite

show abstract

“…Monitoring a computer network involves collecting data to provide real-time statistics and analyse network performance, as well as checking the state of the system continuously and in several possible ways [1]. These resources are often complementary, such as logs, audit trails, system performance data, etc.…”

Section: Introductionmentioning

confidence: 99%

Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision

Chakor

Azmani

2022

Applied Computer Systems

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Intelligent monitoring of a computer network provides a clear understanding of its behaviour at various times and in various situations. It also provides relief to support teams that spend most of their time troubleshooting problems caused by hardware or software failures. This type of monitoring ensures the accuracy and efficiency of the network to meet the expectations of its users. However, to ensure intelligent monitoring, it is necessary to start by automating this process, which often leads to long and costly interventions. The success of such automation implies the establishment of predictive maintenance as a prerequisite for good preventive maintenance governance. However, even when it is practiced effectively, preventive maintenance requires a great deal of time and the mobilization of several full-time resources, especially for large IT structures. This paper gives an overview of the monitoring of a computer network and explains its process and the problems encountered. It also proposes a method based on machine learning to allow for prediction and support decision making to proactively anticipate interventions.

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Applying Machine Learning in Self-adaptive Systems

Gheibi

Weyns

Quin

2020

ACM Trans. Auton. Adapt. Syst.

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Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

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Architecting Dependable Systems with Proactive Fault Management

Cited by 3 publications

References 44 publications

Towards Dynamic Dependable Systems Through Evidence-Based Continuous Certification

Towards Dynamic Dependable Systems Through Evidence-Based Continuous Certification

Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision

Applying Machine Learning in Self-adaptive Systems

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