Hybrid deep learning scheduling method for accelerated processing of multi-ami data stream in edge computing

Abstract

Disclosed is a hybrid deep learning scheduling method for accelerated processing of a multi-advanced metering infrastructure (AMI) data stream in edge computing, wherein a skewed data distribution change, which occurs in AMI data, is detected, an edge server computes an online gradient, which is comparatively quickly computed, on the basis of the detected change, a cloud server computes a normalized gradient, which requires a large amount of computation, according to selection by a hybrid scheduler, and the hybrid scheduler performs a total of three operations: (1) a data stream distribution profiling operation, (2) a memory buffer update operation, and (3) a hybrid scheduling operation.

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