Source-Based Routing Tables for Dynamic Network Demand Adaptation
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Solution Overview
Problem
Existing routing methods in networked systems are computationally expensive and fail to adapt to changing network demands, especially when the number of nodes is large, as they rely on approximating techniques that converge slowly and assume static network conditions.
Innovation Solution
A method for route selection in a network based on payload delivery capacities and routing distance, which involves generating a simplified demand graph, calculating return routes, and selecting advised return routes based on payload delivery capacity and distance, with periodic updates to network demand information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If approximating techniques (genetic algorithm, ant colony algorithm, simulated annealing) are used to find optimal paths, then routing solutions can be obtained for large networks, but computational cost increases and convergence speed decreases
Solution Approach 1:
The patent segments the routing problem by dividing the network into source-specific routing tables rather than computing global paths. Each source node maintains its own routing table with pre-calculated next-hop information, avoiding the need to repeatedly execute complex approximation algorithms for every routing decision. This segmentation transforms the computational burden from global pathfinding to local table lookup.
Solution Approach 2:
The patent applies preliminary action by pre-calculating and storing routing information in source-based routing tables before actual data transmission occurs. The routing tables are computed in advance using approximation techniques, but only once per source node, rather than repeatedly executing these computationally expensive algorithms for each routing decision. This preliminary computation stores the results for rapid retrieval during operational phase.
2Measurement precision
If existing approximating techniques are used for routing, then paths can be found in large networks, but these methods converge very slowly on the solution
Solution Approach 1:
The patent introduces dynamics by implementing periodic updates to routing tables based on changing network conditions. Instead of static routing tables computed once, the system dynamically refreshes routing information at scheduled intervals, allowing the network to adapt to changing demands and topology. This dynamic update mechanism balances convergence accuracy with computational efficiency by updating only when necessary.
Solution Approach 2:
The patent implements feedback mechanisms where routing table updates are triggered by detected changes in network conditions or demand patterns. The system monitors network state and feeds this information back into the routing computation process, adjusting routing tables based on actual performance and changing conditions. This feedback loop ensures routing solutions remain optimal without requiring continuous recomputation.
3Adaptability or versatility
If existing routing methods are used, then paths can be approximated, but these methods often assume that the demands of the network are static and fail to fulfill ever-changing demands
Solution Approach 1:
The patent makes the routing system dynamic by implementing periodic updates to routing tables that reflect changing network demands and conditions. Each source node's routing table is refreshed at scheduled intervals or when changes are detected, allowing the system to adapt to evolving network requirements. This dynamic approach replaces static routing assumptions with adaptive routing that responds to actual network state.
Solution Approach 2:
The patent applies periodic action by scheduling regular updates to routing tables at predetermined intervals. This periodic refresh mechanism ensures routing information remains current with changing network demands without requiring continuous computation. The systematic periodic updates provide a balance between adaptability and computational resource management, updating routing tables frequently enough to capture demand changes but not so frequently as to waste computational resources.
Data Source
AI summary
A system, computer-readable storage medium storing at least one program, and computer-implemented method for route selection based on payload delivery capacity and routing distance are described. Network demand information is obtained. The network demand information may include a network graph and information related to an outbound demand of each node of the network graph. A simplified demand graph based on the outbound demand of each node and a distance between each node pair is generated. A plurality of return routes for the simplified network graph is generated and a payload delivery capacity of each of the routes is calculated. An advised return route from the plurality of return routes is generated based in part on the payload delivery capacities of the plurality of return routes.