Cloud-Native Fog Robotics: Model-Based Deployment and Evaluation of Real-Time Applications

As the field of robotics evolves, robots become increasingly multi-functional and complex. Currently, there is a need for solutions that enhance flexibility and computational power without compromising real-time performance. The emergence of fog computing and cloud-native approaches addresses these...

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Veröffentlicht in:IEEE robotics and automation letters Jg. 10; H. 1; S. 398 - 405
Hauptverfasser: Wen, Long, Zhang, Yu, Rickert, Markus, Lin, Jianjie, Pan, Fengjunjie, Knoll, Alois
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2025
Schlagworte:
Computer architecture
Edge computing
Hardware
Hardware-software integration in robotics
Microservice architectures
Modeling
Real-time systems
Resource management
Robot kinematics
Robots
Software
software architecture for robotic and automation
ISSN:2377-3766, 2377-3766
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Zusammenfassung:As the field of robotics evolves, robots become increasingly multi-functional and complex. Currently, there is a need for solutions that enhance flexibility and computational power without compromising real-time performance. The emergence of fog computing and cloud-native approaches addresses these challenges. In this paper, we integrate a microservice-based architecture with cloud-native fog robotics to investigate its performance in managing complex robotic systems and handling real-time tasks. Additionally, we apply model-based systems engineering (MBSE) to achieve automatic configuration of the architecture and to manage resource allocation efficiently. To demonstrate the feasibility and evaluate the performance of this architecture, we conduct comprehensive evaluations using both bare-metal and cloud setups, focusing particularly on real-time and machine-learning-based tasks. The experimental results indicate that a microservice-based cloud-native fog architecture offers a more stable computational environment compared to a bare-metal one, achieving over 20% reduction in the standard deviation for complex algorithms across both CPU and GPU. It delivers improved startup times, along with a 17% (wireless) and 23% (wired) faster average message transport time. Nonetheless, it exhibits a 37% slower execution time for simple CPU tasks and 3% for simple GPU tasks, though this impact is negligible in cloud-native environments where such tasks are typically deployed on bare-metal systems.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3504243