Performance Comparison of Lightweight Kubernetes in Edge Devices

Fathoni, Halim; Yang, Chao‐Tung; Chang, Chih‐Hung; Huang, Chin‐Yin

doi:10.1007/978-3-030-30143-9_25

Cited by 23 publications

(7 citation statements)

References 9 publications

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“…Docker was discovered to cause only negligible overhead (<5%). CPU and memory consumption of k3s has been initially attempted with RPi devices [41]. Memory and storage requirements of a Kubernetes compatible container solution (FLEDGE) has been compared to k3s with edge devices (RPi) [42].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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“…k3s has been used as lightweight container manager compatible with Kubernetes (k8s). It was preferred to other solutions such as micro k8s and k8s for its low memory footprint [31], its security level using centralized Role Based Access Control (RBAC), and finally for its CUDA support. One Raspberry Pi plays the role of master node, while three others are worker nodes.…”

Section: Experimentationmentioning

confidence: 99%

A New Edge Computing Architecture for IoT and Multimedia Data Management

2022

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The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we use a pure cloud computing strategy. On the other hand, new applications requiring the processing of large amounts of data in real time have gradually emerged, such as virtual reality and augmented reality. These new applications have gradually won over users and developed a demand for near real-time interaction of their applications, which has completely called into question the way we process and store data. To address these two problems of congestion and computing time, edge architecture has emerged with the goal of processing data as close as possible to users, and to ensure privacy protection and responsiveness in real-time. With the continuous increase in computing power, amounts of memory and data storage at the level of smartphone and connected objects, it is now possible to process data as close as possible to sensors or directly on users devices. The coupling of these two types of processing as close as possible to the data and to the user opens up new perspectives in terms of services. In this paper, we present a new distributed edge architecture aiming to process and store Internet of Things and multimedia data close to the data producer, offering fast response time (closer to real time) in order to meet the demands of modern applications. To do this, the processing at the level of the producers of data collaborate with the processing ready for the users, establishing a new paradigm of short supply circuit for data transmission inspired of short supply chains in agriculture. The removing of unnecessary intermediaries between the producer and the consumer of the data improves efficiency. We named this new paradigm the Short Supply Circuit Internet of Things (SSCIoT).

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“…The main pillar of our framework is the cloud/edge paradigm, corresponding to i) a cloud layer where assurance results, coming from the below layer, are aggregated, evaluated, and stored creating a holistic view of the target system; ii) an edge layer where assurance evidence, collected at the perception layer from IoT devices, is preprocessed according to an initial analysis. The framework in Figure 1 has Kubernetes as its core technology, enabling out of the box horizontal scalability, high availability, and support for edge computing and 5G [13]. The framework consists of a set of microservices operating both on the cloud and on the edge, as follows.…”

Section: Framework Architecturementioning

confidence: 99%

Towards an Assurance Framework for Edge and IoT Systems

Anisetti

Ardagna

Bena

et al. 2021

2021 IEEE International Conference on Edge Computing (EDGE)

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Current distributed systems increasingly rely on hybrid architectures built on top of IoT, edge, and cloud, backed by dynamically configurable networking technologies like 5G. In this complex environment, traditional security governance solutions cannot provide the holistic view needed to manage these systems in an effective and efficient way. In this paper, we propose a security assurance framework for edge and IoT systems based on an advanced architecture capable of dealing with 5G-native applications.

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Performance Comparison of Lightweight Kubernetes in Edge Devices

Cited by 23 publications

References 9 publications

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

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

A New Edge Computing Architecture for IoT and Multimedia Data Management

Towards an Assurance Framework for Edge and IoT Systems

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