Self-adaptive Multi-Access Edge Architectures: A Robotics Case

TL;DR

This paper introduces a self-adaptive edge computing system for human-robot environments, utilizing neural networks and Kubernetes to optimize performance and energy efficiency.

Contribution

It presents a novel self-adaptation framework with neural network-based prediction and distributed offloading, improving service quality in mixed human-robot settings.

Findings

Enhanced response times and reduced power consumption.

Effective neural network offloading on heterogeneous edge units.

Improved proactive path planning and safety for mobile robots.

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.