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This paper introduces a self-adaptive multi-access edge architecture for robotics, using a MAPE-K loop to dynamically scale infrastructure and offload computation of a neural network predicting human mobility. The system optimizes for response time and power consumption in a mixed human-robot environment. Experiments demonstrate improved service quality compared to static setups, validating the approach for AI-driven robotic systems.
Adaptive edge offloading slashes response times and power consumption in human-robot collaboration, proving that intelligent infrastructure management is key to efficient AI.
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.
