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.

翻译：随着计算密集型人工智能任务的增长，降低处理成本、提升性能与能效的需求日益凸显。这要求将智能体作为架构自适应监管器，在计算连续体中自适应扩展基础设施并高效卸载计算。本文提出一种面向人机混合环境高效计算系统的自适应方法，其计算任务与利用传感数据预测人类移动行为的神经网络算法相关联，旨在增强移动机器人主动路径规划能力并保障人类安全。为简化神经网络处理，我们构建了由Kubernetes编排的分布式边缘卸载系统，集成异构处理单元。基于MAPE-K的自适应监管器通过监控响应时间与功耗，作出扩展与卸载的决策。实验结果表明，相较于传统配置，本方法在服务质量方面取得显著提升，验证了所提方法在AI驱动系统中的有效性。