Extending reference architecture of big data systems towards machine learning in edge computing environments

Companies across domains are rapidly engaged in shifting computational power and intelligence from centralized cloud to fully decentralized edges to maximize value delivery, strengthen security and reduce latency. However, most companies have only recently started pursuing this opportunity and are therefore at the early stage of the cloud-toedge transition. To provide an overview of AI deployment in the context of edge/cloud/hybrid architectures, we conduct a systematic literature review and a grey literature review. To advance understanding of how to integrate, deploy, operationalize and evolve AI models, we derive a framework from existing literature to accelerate the end-to-end deployment process. The framework is organized into five phases: Design, Integration, Deployment, Operation and Evolution. We make an attempt to analyze the extracted results by comparing and contrasting them to derive insights. The contribution of the paper is threefold. First, we conduct a systematic literature review in which we review the contemporary scientific literature and provide a detailed overview of the state-of-the-art of AI deployment. Second, we review the grey literature and present the state-of-practice and experience of practitioners while deploying AI models. Third, we present a framework derived from existing literature for the end-to-end deployment process and attempt to compare and contrast SLR and GLR results.

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“…Figure 1 outlines the overall SLR process adopted in the study. [12][13][14][15][16][17][18][19][20][21][22][23][24] represent primary studies.…”

Section: Systematic Literature Review (Slr)mentioning

confidence: 99%

Architecting AI Deployment: A Systematic Review of State-of-the-Art and State-of-Practice Literature

John

Olsson

Bosch

2021

Lecture Notes in Business Information Processing

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“…A system must integrate these diverse concepts into a cognitive state for big data analytics and statistical machine learning to predict cyber risks [19]. But the design of big data systems for edge computing environments is challenging [20]. One of the most pressing points for CPS is perhaps security [21], both electronic and physical, that relates physical and cyber systems [22].…”

Section: Literature Review On Artificial Intelligence Cps and Predicmentioning

confidence: 99%

Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

et al. 2020

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We explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in connected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio-technical systems. This resulted in the modelling of the connections and interdependencies between a system's edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new conceptual framework. With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge.

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“…Various Reference Architectures (RA) have been designed for edge computing environments for facilitating the design of concrete architectures [16,17]. Also, many choices have been explored for increasing the performance of Machine learning (ML)-based inference in cloud-edge continuum [12].…”

Section: Memorymentioning

confidence: 99%

Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

et al. 2020

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The current approaches for energy consumption optimisation in buildings are mainly reactive or focus on scheduling of daily/weekly operation modes in heating. Machine Learning (ML)-based advanced control methods have been demonstrated to improve energy efficiency when compared to these traditional methods. However, placing of ML-based models close to the buildings is not straightforward. Firstly, edge-devices typically have lower capabilities in terms of processing power, memory, and storage, which may limit execution of ML-based inference at the edge. Secondly, associated building information should be kept private. Thirdly, network access may be limited for serving a large number of edge devices. The contribution of this paper is an architecture, which enables training of ML-based models for energy consumption prediction in private cloud domain, and transfer of the models to edge nodes for prediction in Kubernetes environment. Additionally, predictors at the edge nodes can be automatically updated without interrupting operation. Performance results with sensor-based devices (Raspberry Pi 4 and Jetson Nano) indicated that a satisfactory prediction latency (~7–9 s) can be achieved within the research context. However, model switching led to an increase in prediction latency (~9–13 s). Partial evaluation of a Reference Architecture for edge computing systems, which was used as a starting point for architecture design, may be considered as an additional contribution of the paper.

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Extending reference architecture of big data systems towards machine learning in edge computing environments

Cited by 27 publications

References 42 publications

Architecting AI Deployment: A Systematic Review of State-of-the-Art and State-of-Practice Literature

Architecting AI Deployment: A Systematic Review of State-of-the-Art and State-of-Practice Literature

Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

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