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Cited by 16IoT and Edge/Fog ComputingEnergy Efficiency in ComputingBuilding Energy and Comfort Optimization

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

Open access
Pääkkönen, Pekka, Pakkala, Daniel, Kiljander, Jussi, Sarala, Roope·2020·Future Internet
DOI 10.3390/fi13010005
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Abstract

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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Crossref · 10.3390/fi13010005

Cites

35 of 35 references verified0 citation errors · 0 not checkable

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  • Another reported method of integration is to serialize the model and load it at runtime, possibly with an ML framework optimized for production.

    “Another reported method of integration is to serialize the model (e.g., into ONNXfoot_2 , HDF5, Joblib 4 ) and load it at runtime, possibly with an ML framework that is optimized for production [9,21,39,40].”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [40]

  • In a pull-pattern deployment, the target environment periodically polls for model updates and downloads when available.

    “In a pull-pattern deployment, the target environment (host application running on, e.g., server or edge device) periodically polls for model updates and downloads when available [28,39,40].”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [40]

  • Models are commonly made available for serving predictions through a REST or RPC API.

    “Models are commonly made available for serving predictions through a REST [12,16,26,29,40,45] or RPC [12,28,45] API.”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [40]

  • If the hosting application is containerized, model redeployment may be orchestrated by deploying an entirely new application instance with the new model data alongside the old instance; when the new instance is initialized, it can start serving requests and the old instance may be evicted.

    “If the hosting application is containerized, model redeployment may be orchestrated by deploying an entirely new application instance with the new model data alongside the old instance.When the new application instance is finished initializing, it can start serving requests, and the old instance may be evicted [40].”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [40]

  • Models packaged in containers are typically accessed through either a REST API or an RPC interface.

    “Models packaged in containers are typically accessed through either a REST API [16,40], such as Flask 18 [20], or RPC interface [12,45], such as Apache Thrift 19 [28].”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [40]

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Research works printed but not linked: 3

  1. Zhang, Z.; Chong, A.; Pan, Y.; Zhang, C.; Lu, S.; Lan, K.P. A Deep Reinforcement Learning Approach to Using Whole Building Energy Model for HVAC Optimal Control. In Proceedings of the Building Performance Modeling Conference and SIMBuild, Chicago, IL, USA, 26-28 September 2018.
  2. Salmi, T.; Kiljander, J.; Pakkala, D. Stacked Boosters Network Architecture for Short-Term Load Forecasting in Buildings. Energies 2020, 13, 2370. [CrossRef] · DOI
  3. Zhou, L.; Wen, H.; Teodorescu, R.; Du, D.H.C. Distributing deep neural networks with containerized partitions at the edge. In Proceedings of the 2nd Usenix Workshop on Hot Topics in Edge Computing, Renton, WA, USA, 9 July 2019.

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Authors
Pääkkönen, P., Pakkala, D., Kiljander, J., & Sarala, R.
Affiliations
VTT Technical Research Centre of Finland (FI)
Corresponding author
Pääkkönen, Pekka
ORCID
Venue
Future Internet
Publisher
MDPI AG
Published
2020-12-29
Volume
13
Issue
1
Pages
5
ISSN
1999-5903 (ISSN-L)
DOI
10.3390/fi13010005
OpenAlex
W3113668354
Cited by
16
References
35
Citations by year
2026: 1 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 6
FWCI
1.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Future Internet (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
  • Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
Keywords
edge inference, model transfer, Kubernetes, building energy consumption prediction, edge computing architecture, inference latency, Raspberry Pi, Jetson Nano

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