Network Link Capacity Augmentation via Shortest Path Analysis
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Solution Overview
Problem
Existing communication networks face challenges in maintaining capacity and fault tolerance when links fail, as current methods for augmenting link capacity are often reactive and do not efficiently identify the most critical paths for augmentation.
Innovation Solution
A method and system that use processors to identify subsets of links on the shortest and alternative paths between node pairs, attribute demand capacities to these links, and iteratively accumulate and compare these capacities to determine which links need augmentation, allowing for proactive enhancement of network capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network link capacity is augmented in anticipation of each failure topology using algorithms like CSPF, then fault tolerance is improved, but device complexity and operational difficulty increase due to the need to identify and modify multiple critical paths
Solution Approach 1:
The patent applies preliminary action by proactively identifying critical network links and augmenting their capacity before failures occur. The system performs link failure analysis and identifies critical links that would impact service level objectives, then augments capacity in advance. This eliminates the need for reactive modifications after failures and simplifies operational complexity by automating the identification process.
Solution Approach 2:
The system applies self-service by automatically identifying critical links and determining capacity augmentation requirements without manual intervention. The link failure analysis module autonomously evaluates network topology and service level objectives to identify which links require capacity increases, eliminating the need for operators to manually analyze each failure topology.
2Quantity of substance
If personnel are dispatched to modify the physical networking infrastructure for capacity augmentation, then link capacity is improved, but loss of time increases due to physical deployment requirements
Solution Approach 1:
The system performs capacity augmentation in advance by provisioning additional bandwidth or capacity on existing links before failures occur. This preliminary capacity preparation eliminates the need for urgent physical deployments after failures, reducing deployment time while ensuring capacity is available when needed.
Solution Approach 2:
The patent applies parameter changes by modifying network capacity parameters through configuration changes rather than physical infrastructure modifications. The system can augment link capacity by adjusting bandwidth allocations, reconfiguring network devices, or activating standby resources, which are much faster than physical cable deployments.
3Reliability
If network capacity is augmented to meet expected traffic demand and fault tolerance, then reliability is improved, but loss of energy increases due to additional infrastructure and higher bandwidth allocation
Solution Approach 1:
The system applies local quality by augmenting capacity only on specific critical links identified through link failure analysis, rather than uniformly increasing capacity across the entire network. The link failure analysis module identifies which specific links are critical to service level objectives and targets capacity augmentation only to those locations, minimizing overall energy consumption while maintaining reliability.
Solution Approach 2:
The patent applies partial action by providing capacity augmentation only when and where needed based on actual failure analysis results. Rather than over-provisioning the entire network, the system selectively augments capacity on critical links, avoiding unnecessary energy consumption on non-critical portions of the network while still achieving fault tolerance goals.
Data Source
AI summary
A system and method is provided for identifying network links for augmentation based on potential link failures. In one aspect, the links are selected by identifying multiple shortest paths between a node pair and generating augmentation recommendations for a single link by accumulating recommendations based on multiple node pairs.