  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.
