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In a hierarchically-structured cloud/edge/device computing environment,workload allocation can greatly affect the overall system performance. Thispaper deals with AI-oriented medical workload generated in emergency rooms (ER)or intensive care units (ICU) in metropolitan areas. The goal is to optimizeAI-workload allocation to cloud clusters, edge servers, and end devices so thatminimum response time can be achieved in life-saving emergency applications.  In particular, we developed a new workload allocation method for the AIworkload in distributed cloud/edge/device computing systems. An efficientscheduling and allocation strategy is developed in order to reduce the overallresponse time to satisfy multi-patient demands. We apply several ICU AIworkloads from a comprehensive edge computing benchmark Edge AIBench. Thehealthcare AI applications involved are short-of-breath alerts, patientphenotype classification, and life-death threats. Our experimental resultsdemonstrate the high efficiency and effectiveness in real-life health-care andemergency applications.

AI-oriented Medical Workload Allocation for Hierarchical Cloud/Edge/Device Computing