论文标题
层次云/边缘/设备计算的面向AI的医疗工作负载分配
AI-oriented Medical Workload Allocation for Hierarchical Cloud/Edge/Device Computing
论文作者
论文摘要
在层次结构的云/边缘/设备计算环境中，工作负载分配会极大地影响整体系统性能。本文介绍了大都市地区急诊室（ER）或重症监护病房（ICU）产生的面向AI的医疗工作量。目的是优化为云簇，边缘服务器和端设备的AI-Workload分配，以便在挽救生命的紧急应用中可以实现最小响应时间。 特别是，我们为分布式云/边缘/设备计算系统中的AI工作负载开发了一种新的工作负载分配方法。制定了有效的调度和分配策略，以减少总体响应时间以满足多人需求。我们从全面的边缘计算基准边缘AIBENCH上应用了几个ICU AI工作负载。涉及的医疗保健AI应用程序是呼吸急性警报，患者表型分类和生命死亡威胁。我们的实验结果表明，在现实生活中的医疗保健和紧急应用中，效率和有效性很高。
In a hierarchically-structured cloud/edge/device computing environment, workload allocation can greatly affect the overall system performance. This paper deals with AI-oriented medical workload generated in emergency rooms (ER) or intensive care units (ICU) in metropolitan areas. The goal is to optimize AI-workload allocation to cloud clusters, edge servers, and end devices so that minimum response time can be achieved in life-saving emergency applications. In particular, we developed a new workload allocation method for the AI workload in distributed cloud/edge/device computing systems. An efficient scheduling and allocation strategy is developed in order to reduce the overall response time to satisfy multi-patient demands. We apply several ICU AI workloads from a comprehensive edge computing benchmark Edge AIBench. The healthcare AI applications involved are short-of-breath alerts, patient phenotype classification, and life-death threats. Our experimental results demonstrate the high efficiency and effectiveness in real-life health-care and emergency applications.
