Capacity expansion optimization method, system and equipment based on network performance marginal gain
By constructing the MCFP model and Lagrange duality theory, the global correlation between network topology and service requirements is quantified, key bottleneck links are identified, and expansion schemes are optimized. This solves the problems of network performance bottlenecks and low resource utilization, and enables accurate network expansion decisions and resource optimization.
Patent Information
- Application Number
- CN202512005144.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-01-27
- Estimated Expiration
- 2045-12-29
AI Technical Summary
Existing technologies have shortcomings in network load assessment, bottleneck identification, and capacity expansion optimization, and cannot accurately match network resources with service needs, resulting in network performance bottlenecks and low resource utilization.
By constructing the MCFP model and combining Lagrange duality theory and column generation algorithm, the global correlation between network topology, link capacity and service requirements is quantified, key bottleneck links are identified and expansion schemes are optimized, and accurate network load assessment and bottleneck quantification and identification are achieved.
It provides accurate network performance metrics, enables precise quantitative ranking of the importance of bottleneck links, and improves the scientific nature of network expansion decisions and the efficiency of resource utilization.
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Abstract
Citation Information
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