An application programming interface orchestration model training method, an orchestration method and an apparatus

By constructing a directed heterogeneous graph and training an orchestration model using a graph convolutional neural network, the problem of API orchestration relying on manual design is solved, realizing automated and optimized API call sequence generation, shortening the design cycle and improving system performance.

CN120821468BActive Publication Date: 2025-11-18CLOUDCHAIN GRP CO LTD
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Patent Information

Application Number
CN202511318147.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-09-16
Publication Date
2025-11-18
Estimated Expiration
2045-09-16

AI Technical Summary

Technical Problem

Existing technologies rely on manual design during API orchestration, which cannot effectively utilize the valuable information in runtime API call logs. This results in long design cycles, high maintenance costs, and a disconnect between design and runtime data, leading to missed optimization opportunities.

Method used

By constructing a directed heterogeneous graph and using a graph convolutional neural network for iterative link prediction, combined with topological correctness, latency penalty, and error rate penalty loss, an application programming interface orchestration model is trained to automatically output efficient and reliable API call sequences.

Benefits of technology

It significantly shortens the workflow design cycle, reduces labor costs, enables the automatic generation and optimization of orchestration schemes, and enhances system resilience and scalability.

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Abstract

The application provides an application programming interface (API) orchestration model training method and device, which converts original call logs into a directed heterogeneous graph with features containing topology, performance and error information. Then, based on a graph neural network, incremental link reasoning is performed in a context subgraph to automatically generate the API call link with the highest probability and its performance estimation, thereby realizing intelligent recommendation of the orchestration scheme. During the training process, a composite loss function is used to simultaneously optimize the topology accuracy, delay and error rate, thereby driving the model to integrate the function correctness and operation efficiency. This method automatically generates the workflow design instead of manual orchestration, thereby greatly improving the development efficiency. Meanwhile, relying on the runtime data backflow, the system can be iterated to continuously enhance the system adaptability and robustness.
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