Network Capacity Management via Hierarchical Segmentation
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Solution Overview
Problem
Current network capacity management systems face challenges in accurately analyzing and optimizing network topologies due to their generic nature, which can lead to insufficient information and high costs in defining tens of thousands of nodes, links, and interrelationships, making it difficult to meet projected needs and ensure network optimality.
Innovation Solution
A network capacity management system that includes a data repository for topology and service consumption data, a network definition component to generate a detailed network definition, and an optimization component using an evolution engine to process the definition based on predetermined criteria, enabling high-granularity analysis and optimization of network paths and capacity consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a network topology is defined at high granularity with tens of thousands of nodes, links, and interrelationships, then measurement precision and analysis accuracy are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent segments the network topology into hierarchical levels (e.g., core network, access network, edge devices) and processes each segment separately. This allows high-granularity analysis of individual segments without requiring simultaneous processing of the entire tens-of-thousands-node network, thereby maintaining measurement precision while reducing overall system complexity.
Solution Approach 2:
The patent introduces temporal dimension by capturing network topology at specific time points and comparing changes over time. This allows the system to analyze high-granularity topology data in manageable time slices, improving analysis accuracy without overwhelming the system with all-time data simultaneously.
2Device complexity
If a network topology is defined generically with abstract components, then device complexity is reduced, but measurement precision and available information for optimization decrease
Solution Approach 1:
The patent applies different levels of detail to different parts of the network topology. Critical segments (e.g., core routing paths, high-traffic areas) are defined with high granularity including detailed node and link attributes, while less critical segments use more abstract representations. This local quality approach ensures measurement precision where needed while maintaining overall system manageability.
3Measurement precision
If real network data is used for analysis, then measurement precision is improved, but the network must be taken out of service or testing capacity is reduced
Solution Approach 1:
The patent creates virtual copies or models of the network topology that replicate the structure and characteristics of the real network. These models can be populated with real network data during maintenance windows or using historical data, allowing high-precision analysis to be performed on the copies without removing the actual network from service. The optimization results are then validated and applied back to the real network.
4Manufacturing precision
If detailed topology data is collected and processed, then optimization accuracy is improved, but loss of time for data collection and processing increases
Solution Approach 1:
The patent performs preliminary processing of topology data by pre-identifying critical paths, high-traffic segments, and potential bottleneck areas during periods when full analysis is not required. This preliminary action creates a focused subset of data that requires less intensive processing during actual optimization events, thereby maintaining high optimization accuracy while reducing real-time data processing time.
Data Source
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AI summary
A network capacity management system and method are disclosed. The system (10) includes a first data repository (20) encoding topology definition data, the topology definition data comprising data on topology elements of a network and including data on capacity of a respective topology element, a second data repository (30) encoding service consumption data, the service consumption data comprising data on service elements associated with a network and including data on capacity requirements associated with a respective service element, a network definition component (40) operable generate a network definition from the topology definition data and the service consumption data, the network definition encoding capacity criteria determined from said data on capacity of the respective topology elements and from data on capacity requirements of the respective service elements and an optimisation component (50) operable to process said network definition in dependence on one or more predetermined optimisation criteria to optimise said network definition based on said optimisation criteria and on said capacity criteria.