A Container-based Design Methodology for Robotic Applications on Kubernetes Edge-Cloud architectures

Lumpp, Francesco; Panato, Marco; Fummi, Franco; Bombieri, Nicola

doi:10.1109/fdl53530.2021.9568376

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…The abstraction provided by ROS eases the development and integration of software across different platforms. Lumpp et al [76] propose the utilization of containers in the design flow of robotic applications. The proposed methodology uses Docker, Kubernetes, and ROS to achieve reusable, modular, and portable software components for use in multiple domains.…”

Section: Roboticsmentioning

confidence: 99%

“…Year Application Area Description EI Level [75] 2023 Federated learning Dynamic FL deployment and learning scheme 4 [76] 2021 Robotics Design methodology for ROS-based applications 4 [77] 2021 Healthcare Readmission prediction system for healthcare facilities 3 [78] 2022 Healthcare Electronic health records decomposing the patient's body into containers - [79] 2021 Virtual assistant Voice control for human-machine interaction 3 [81] 2022 Composite AI Ontology model for development of multi-agent AI systems - [82] 2018 Healthcare Human activity recognition 3 [83] 2021 Security Re-identification of people across multiple cameras 2 [84] 2022 Wildfire modelling A federation architecture to enable a composable infrastructure - [85] 2019 Computer vision Architecture for image processing 3…”

Section: Referencementioning

confidence: 99%

See 1 more Smart Citation

Containerization in Edge Intelligence: A Review

Urblik,

Kajati,

Papcun

et al. 2024

Electronics

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The onset of cloud computing brought with it an adoption of containerization—a lightweight form of virtualization, which provides an easy way of developing and deploying solutions across multiple environments and platforms. This paper describes the current use of containers and complementary technologies in software development and the benefits it brings. Certain applications run into obstacles when deployed on the cloud due to the latency it introduces or the amount of data that needs to be processed. These issues are addressed by edge intelligence. This paper describes edge intelligence, the deployment of artificial intelligence close to the data source, the opportunities it brings, along with some examples of practical applications. We also discuss some of the challenges in the development and deployment of edge intelligence solutions and the possible benefits of applying containerization in edge intelligence.

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Section: Roboticsmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Containerization in Edge Intelligence: A Review

Urblik,

Kajati,

Papcun

et al. 2024

Electronics

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“…Their results have shown that the service invocation time is considerably reduced when compared to that of other monolithic solutions. In [37], [38] the authors investigated a design methodology to containerize and orchestrate ROS-based applications, so that performance and memory footprint overheads are minimized, and integration and verification take place before deployment. Their findings have shown that the additional orchestration overhead may not be negligible if careful splitting of ROS nodes into Docker containers is not taken into account.…”

Section: Related Workmentioning

confidence: 99%

AI-Driven Ground Robots: Mobile Edge Computing and mmWave Communications at Work

Baruffa,

Detti,

Rugini

et al. 2024

IEEE Open J. Commun. Soc.

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The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a cohesive continuum system, tailored for mobile applications. Many research projects and focused studies are proposing solutions in this area, the impact of which is undoubtedly increased by moving from theoretical and simulation studies to experimental validations. To this aim, this paper proposes a testbed architecture that combines contemporary communication and cloud technologies to provide microservice-based mobile applications with the ability to offload part of their tasks to cloud/edge data centers connected by multi-RAT cellular networks. The testbed leverages Kubernetes, Istio service mesh, OpenFlow, public 5G networks, and IEEE 802.11ad mmWave (60 GHz) Wi-Fi access points. The architecture is validated through a use case in which a ground robot autonomously follows a moving object by using an artificial intelligence-driven computer vision application. Computationally intensive navigation tasks are offloaded by the robot to microservice instances, which are executed on demand within cloud and edge data centers that the robot can exploit during its journey. The proposed testbed is flexible and can be reused to assess communication and cloud innovations focusing on multi-RAT cloud continuum scenarios.

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“…Containerization combined with orchestration has been investigated in fog and edge computing [28], [29], [30], [31]. Different strategies of micro-service deployment can be adopted to improve the performance, energy efficiency, and carbon footprint ( [28], [32]), as well as the quality of service ( [29], [31]).…”

Section: Related Workmentioning

confidence: 99%

Enabling Kubernetes Orchestration of Mixed-Criticality Software for Autonomous Mobile Robots

Lumpp,

Fummi,

Patel

et al. 2024

IEEE Trans. Robot.

Self Cite

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Containerization and orchestration have become two key requirements in software development best practices. Containerization allows for better resource utilization, platform-independent development, and secure deployment of software. Orchestration automates the deployment, networking, scaling, and availability of containerized workloads and services. While containerization is increasingly being adopted in the robotic community, the use of task orchestration platforms (e.g., Kubernetes) is still an open challenge. The biggest limitation is due to the fact that state-of-the-art orchestrators do not support real-time containers, while advanced robotic software often consists of a mix of heterogeneous tasks (i.e., ROS nodes) with different levels of temporal constraints (i.e., mixed-criticality systems). This work addresses this challenge by presenting RT-Kube, a platform that extends the de-facto reference standard for container orchestration, Kubernetes, to schedule tasks with mixed-criticality requirements. It implements monitoring of tasks and detects missed deadlines for those with real-time constraints. It selects low-priority tasks to be migrated at runtime to different units of the computing cluster to free resources and recover from temporal violations. We present quantitative experimental results on the software implementing the mission of a Robotnik RB-Kairos mobile robot to demonstrate the effectiveness of the proposed approach. The source code is publicly available on GitHub.

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A Container-based Design Methodology for Robotic Applications on Kubernetes Edge-Cloud architectures

Cited by 16 publications

References 15 publications

Containerization in Edge Intelligence: A Review

Containerization in Edge Intelligence: A Review

AI-Driven Ground Robots: Mobile Edge Computing and mmWave Communications at Work

Enabling Kubernetes Orchestration of Mixed-Criticality Software for Autonomous Mobile Robots

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