Architecture-based Software Reliability Incorporating Fault Tolerant Machine Learning

Nafreen, Maskura; Bhattacharya, Saikath; Fiondella, Lance

doi:10.1109/rams48030.2020.9153718

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…However, this requires significant investments in terms of cost and resources. N-version programming [57,66,67] can improve the reliability of the agent by using different versions of FMs as basis for its reasoning.…”

Section: Ai Modelsmentioning

confidence: 99%

Responsible-AI-by-Design: A Pattern Collection for Designing Responsible Artificial Intelligence Systems

Zhu

et al. 2023

IEEE Softw.

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The release of ChatGPT, Bard, and other large language model (LLM)-based chatbots has drawn huge attention on foundations models worldwide. There is a growing trend that foundation models will serve as the fundamental building blocks for most of the future AI systems. However, incorporating foundation models in AI systems raises significant concerns about responsible AI due to their black box nature and rapidly advancing super-intelligence. Additionally, the foundation model's growing capabilities can eventually absorb the other components of AI systems, introducing the moving boundary and interface evolution challenges in architecture design. To address these challenges, this paper proposes a pattern-oriented responsible-AI-by-design reference architecture for designing foundation model-based AI systems. Specially, the paper first presents an architecture evolution of AI systems in the era of foundation models, from "foundation-model-as-a-connector" to "foundationmodel-as-a-monolithic architecture". The paper then identifies the key design decision points and proposes a pattern-oriented reference architecture to provide reusable responsible-AI-by-design architectural solutions to address the new architecture evolution and responsible AI challenges. The patterns can be embedded as product features of foundation model-based AI systems and can enable organisations to capitalise on the potential of foundation models while minimising associated risks.

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Section: Ai Modelsmentioning

confidence: 99%

Responsible-AI-by-Design: A Pattern Collection for Designing Responsible Artificial Intelligence Systems

Zhu

et al. 2023

IEEE Softw.

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“…Nafreen et al [29] study architecture-based reliability modeling by considering learning-enable components using faulttolerant machine-learning approaches. Similarly, Kumar et al [24] investigate machine-learning techniques to predict software reliability.…”

Section: B Machine-learning Approachesmentioning

confidence: 99%

Smart and Adaptive Routing Architecture: An Internet-of-Things Traffic Manager Based on Artificial Neural Networks

Amiri,

Zdun

2023

2023 IEEE International Conference on Software Services Engineering (SSE)

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Many studies have been performed on integrating the Internet of Things (IoT) with cloud services. As these systems become widely used, quality metrics are of concern. For example, users might specify access control to restrict their sensitive data being processed in the cloud. Routers, e.g., API gateways, message brokers, or sidecars, can provide this access control by blocking or routing device data to a specific cloud service. However, a static routing application might not suit the dynamic behavior of IoT applications well. For example, in a centralized schema, where all device data is routed to a component for control checking, performance can be an issue. On the other hand, distributed routing can harm the reliability of a system, as device data might be lost due to an unresponsive service. We present the Smart and Adaptive Routing (SAR) architecture that creates an optimal reconfiguration solution using a deep neural network based on the quality metrics of an IoT application. To design our architecture, we give a background of the published studies and a review of the gray literature, e.g., practitioner blogs, to categorize the knowledge in the domain of IoT-cloud traffic management. We systematically evaluate our approach in an extensive evaluation of 4500 cases and compare SAR with an empirical data set of 1200 hours. The results show that our approach significantly improves quality-of-service measures by adapting the IoT-cloud system at runtime.

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Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

Lu,

Zhu,

et al. 2024

ACM Comput. Surv.

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Responsible AI is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of AI. Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. Also, significant efforts have been placed at algorithm-level rather than system-level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize responsible AI from a system perspective, in this paper, we present a Responsible AI Pattern Catalogue based on the results of a Multivocal Literature Review (MLR). Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The Responsible AI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and responsible-AI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement responsible AI.

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Architecture-based Software Reliability Incorporating Fault Tolerant Machine Learning

Cited by 3 publications

References 16 publications

Responsible-AI-by-Design: A Pattern Collection for Designing Responsible Artificial Intelligence Systems

Responsible-AI-by-Design: A Pattern Collection for Designing Responsible Artificial Intelligence Systems

Smart and Adaptive Routing Architecture: An Internet-of-Things Traffic Manager Based on Artificial Neural Networks

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

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