用自适应架构提升人机共存环境下的机器人计算效率。
Self-adaptive Multi-Access Edge Architectures: A Robotics Case
- 以MAPE-K框架监控响应时间与功耗，动态调整边缘计算资源。
- 在混合人机环境中实现神经网络任务的高效卸载，提升服务品质。
- 适合关注边缘计算与智能机器人协同的开发者与研究者。
计算密集型人工智能任务的增长凸显了降低处理成本、提升性能与能效的迫切需求。这要求将智能代理作为架构自适应管理者，负责基础设施的动态扩展与计算任务的高效卸载。本文提出一种面向混合人-机器人环境的自适应计算系统。计算任务基于神经网络算法，利用传感数据预测人类移动行为，以增强移动机器人的主动路径规划并保障人类安全。为优化神经网络处理，构建了由Kubernetes编排的异构处理单元分布式边缘卸载系统。通过监测响应时间与功耗，基于MAPE-K的自适应管理器做出资源扩展与任务卸载决策。结果表明，该方法在服务品质上显著优于传统方案，验证了其在AI驱动系统中的有效性。
原文摘要 · Abstract (English)
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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