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

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.

Event-Driven Deep Learning for Edge Intelligence (EDL-EI)

“…Jetson Nano’s performance is faster than that of Raspberry Pi. Our observations for Jetson Nano and Raspberry Pi are similar to observations reported in a recent study that describes a comparative evaluation of the latency of Jetson Nano and Raspberry Pi 4; Jetson Nano achieved 2.2 s lower latency than Raspberry Pi 4 [ 45 ].…”

Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator

“…In theory, any generic function approximator could be used instead of an ANN to approximate the physics-based simulator. An ANN model was selected for this study mainly for following reasons: (1) ANN models are highly parallel and thus provide fast inference, (2) ANN models provide gradient based optimization through back propagation, which is important when the model is used in planning and control [17][18][19], and (3) ANN platforms such as TensorFlow also provide good support for model deployment in edge environments [14,15].…”

“…On the other hand, machine learning-based methods are scalable, accurate, and require less human effort in the modelling [9][10][11][12][13]. Their deployment is also easier and supported by existing tools and infrastructure [14][15][16]. Machine learning models also typically provide fast enough inference for model-based optimization.…”