A path computing scheme for low-latency task is proposed to solve the problem that there exist high communication load and high task processing latency when cloud computing is applied to medical big data processing. The scheme firstly constructs the medical big data task into a directed acyclic graph that is composed of multiple subtasks with explicit input and output relationships. Then
a cloud and fog network architecture is designed
in which network edge devices such as switches and routers in hospitals form a fog computing layer. The computing capacity of fog nodes is used to gradually complete the big data task in the process of end-to-end directional data transmission. A task mapping strategy based on the discrete binary particle swarm optimi-ation(BPSO)algorithm is proposed to deploy the medical big data task to a hospital network. The big data task in the form of a directed acyclic graph is mapped into a topology graph of the hospital fog network to find an appropriate comput
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