自己適応型マルチアクセスエッジアーキテクチャ：ロボティクス事例
Self-adaptive Multi-Access Edge Architectures: A Robotics Case
人間とロボットが混在する環境で、AIタスクの処理効率とエネルギー効率を高めるため、MAPE-Kに基づく自己適応型エッジオフロードシステムを構築し、応答時間と消費電力を監視してスケーリングとオフロードを最適化した。
著者: Mahyar T Moghaddam, Joakim Leed, Anders Frandsen
分類: cs.RO, cs.DC, cs.SE
原文アブストラクト
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.