Self-adaptive Multi-Access Edge Architectures: A Robotics Case
Mahyar T Moghaddam, Joakim Leed, Anders Frandsen
TLDR
A self-adaptive multi-access edge architecture improves AI task processing for human-robot environments by intelligently offloading computation.
Key contributions
- Presents a self-adaptation approach for efficient computing in mixed human-robot environments.
- Leverages Neural Networks to predict human mobility for proactive robot path planning.
- Built a distributed edge offloading system with heterogeneous units, orchestrated by Kubernetes.
- Employs a MAPE-K supervisor to adaptively scale and offload based on performance metrics.
Why it matters
This paper tackles the growing challenge of compute-intensive AI tasks in robotics by proposing a self-adaptive edge architecture. It significantly improves service quality and energy efficiency, crucial for real-world human-robot collaboration.
Original Abstract
The growth of compute-intensive AI tasks highlights the need to mitigate the processing costs and improve performance and energy efficiency. This necessitates the integration of intelligent agents as architectural adaptation supervisors tasked with adaptive scaling of the infrastructure and efficient offloading of computation within the continuum. This paper presents a self-adaptation approach for an efficient computing system of a mixed human-robot environment. The computation task is associated with a Neural Network algorithm that leverages sensory data to predict human mobility behaviors, to enhance mobile robots' proactive path planning, and ensure human safety. To streamline neural network processing, we built a distributed edge offloading system with heterogeneous processing units, orchestrated by Kubernetes. By monitoring response times and power consumption, the MAPE-K-based adaptation supervisor makes informed decisions on scaling and offloading. Results show notable improvements in service quality over traditional setups, demonstrating the effectiveness of the proposed approach for AI-driven systems.
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