Micro-service topology-aware load prediction method and system based on graph neural network

Abstract

The embodiment of the application provides a micro-service topology-aware load prediction method and system based on a graph neural network, which comprises the following steps: obtaining micro-service calling data corresponding to a plurality of micro-service instances in a target container; inputting the micro-service calling data corresponding to a service instance to be predicted into a load prediction model to determine a heterogeneous relation graph based on data construction layers; determining topology features based on a graph structure module, and determining time sequence features based on a time sequence sequence module; performing fusion processing on the topology features and the time sequence features based on a fusion module to output first feature representations; determining candidate micro-service instances according to the first feature representations and the plurality of micro-service instances; performing load state classification processing on the candidate micro-service instances based on a classification layer to obtain load state categories; and determining a target micro-service instance from the candidate micro-service instances based on the load state categories. The technical scheme improves the transparency and credibility of the prediction result, and improves the accuracy and robustness of the micro-service load state prediction.

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