An energy efficiency service deployment and delivery method and system for edge computing network
By employing a self-attention mechanism and a dual-timescale hierarchical learning framework, the problems of difficulty in capturing multidimensional dependencies and lack of adaptability in decision-making mechanisms in edge computing networks are solved, enabling efficient service deployment and delivery, improving response speed, and reducing system costs.
CN120751406BActive Publication Date: 2026-06-26SOUTHWEST JIAOTONG UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTHWEST JIAOTONG UNIV
- Filing Date
- 2025-07-15
- Publication Date
- 2026-06-26
Smart Images
Figure CN120751406B_ABST
Abstract
The application provides an energy efficiency service deployment and delivery method and system for an edge computing network, and relates to the technical field of edge computing.The method comprises the following steps: extracting multi-dimensional service features, encoding and fusing the extracted multi-dimensional service features to generate a global context feature representation; combining system load dynamic prediction results to adaptively adjust an upper-layer decision time scale based on the global context feature representation; executing service deployment decisions and base station sleep / activation switching decisions under the adjusted upper-layer time scale; executing service delivery decisions and resource allocation decisions based on real-time service requests under a fine-grained lower-layer time scale; and coordinating the decision-making process through a double-time-scale hierarchical learning framework.The method can effectively reduce long-term network overhead, effectively respond to dynamic service requests, improve the processing capacity for time coupling relationships between deployment and delivery cycles, and reduce system costs.
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