Method for reporting multi-dimensional perception of edge computing-based computing network resource state

By deploying lightweight edge computing units on computing network nodes for data processing and prediction, and generating streamlined resource status data, the problems of high computing pressure and data redundancy on the central server are solved, and efficient resource status awareness and scheduling are achieved.

CN122640459AActive Publication Date: 2026-08-25联通(陕西)产业互联网有限公司 +1
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Patent Information

Application Number
CN202611096063.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-07-23
Publication Date
2026-08-25
Estimated Expiration
2046-07-23

AI Technical Summary

Technical Problem

In large-scale, highly dynamic computing networks, the central server suffers from overloaded computing pressure and high redundancy of raw data, resulting in insufficient real-time reporting and inability to meet real-time scheduling requirements.

Method used

Lightweight edge computing units are deployed at the computing network nodes to collect data locally, remove anomalies, standardize and quantize data in a hierarchical manner, generate simplified resource status data, and make predictions and early warnings through a lightweight time series prediction model. Combined with task adaptation tags, a local pre-scheduling scheme is generated and reported to the central server using incremental transmission.

Benefits of technology

It offloads the central computing load, reduces bandwidth consumption, improves scheduling response speed, enables real-time edge early warning and proactive scheduling, and solves the problems of central computing overload and data redundancy.

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Abstract

The application discloses an edge-computing-based multi-dimensional perception and reporting method for computing network resource state, and relates to the technical field of computing network resource management. The method comprises the following steps: deploying a lightweight edge computing unit on each computing network node to collect computing power, storage, network, load and environment dimension running data nearby; performing abnormality elimination, standardization and hierarchical quantization to output a simplified resource state; calling a standard task library to match resource demand calculation adaptation degree and generate a task adaptation label; inputting the simplified state into a time series prediction model to identify abnormalities and divide early warnings, and generate a local pre-scheduling scheme; and encapsulating the simplified state, the adaptation label, the early warning and the scheme, and reporting them to a central server in an incremental manner through a lightweight protocol. The application solves the problems of large computing pressure, data redundancy and poor real-time performance of the central server in the prior art.
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