Multi-Layer Network Routing Engine Using Evolving Path Populations
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Solution Overview
Problem
Existing routing technologies for multi-layer networks are inefficient due to static optimization, lack of consideration for inter-layer dependencies, and failure to adapt to changes in network topology or resource utilization, leading to suboptimal route determination and resource utilization.
Innovation Solution
The development of network simulation equipment that includes an adaptor module to convert multi-layer systems into networks of nodes and links, with multiple routing engines to determine and evolve optimal routes across layers, utilizing an evolving module to mate and mutate path populations based on objective functions and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If known apparatus determine routes separately and in a pre-determined order for each requirement, then the routing process is simple and manageable, but network resource utilization becomes inefficient
Solution Approach 1:
The patent combines multiple single-requirement routing processes into a unified multi-requirement routing engine that simultaneously determines routes for multiple requirements. The system merges the routing of different traffic types, QoS requirements, and service levels into a single optimization process that considers all requirements together, thereby improving network resource utilization while maintaining operational simplicity through integration.
Solution Approach 2:
The routing system transitions from static, pre-determined ordering to dynamic route determination. The engine adaptively adjusts routing decisions based on real-time network conditions, resource availability, and multiple competing requirements. This dynamic approach allows the system to optimize resource utilization by responding to changing network states rather than following fixed routing sequences.
2Device complexity
If routing apparatus optimize individual routes for telecommunications services in a specific order at each network layer independently, then the optimization process is manageable and technology-specific, but global network resource utilization is compromised and inter-layer dependencies are not understood
Solution Approach 1:
The routing engine is designed as a universal multi-functional system that handles multiple network layers, traffic types, and QoS requirements simultaneously. Rather than using separate technology-specific optimization processes for each layer, the engine provides a unified framework that can optimize routes across different network technologies and layers while considering their interdependencies, thereby improving global resource utilization.
Solution Approach 2:
The system transitions from two-dimensional independent layer optimization to multi-dimensional global optimization. By considering multiple network layers, traffic requirements, and resource constraints simultaneously in a unified optimization space, the engine captures inter-layer dependencies and achieves better overall network resource utilization than separate layer-by-layer optimization could provide.
3Stability of the object's composition
If fixed outcome methods are used to determine routes, then the routing process is deterministic and predictable, but network efficiency is reduced and competitive differentiation is limited
Solution Approach 1:
The routing engine implements dynamic route determination that adapts to changing network conditions while maintaining deterministic behavior through defined optimization criteria. The system evaluates multiple routing options and selects optimal paths based on real-time resource availability, traffic patterns, and QoS requirements, providing both efficiency improvement and predictable outcomes through structured decision-making processes.
Solution Approach 2:
The system changes routing parameters dynamically based on network conditions, traffic requirements, and resource availability. Rather than using fixed routing tables, the engine adjusts route parameters such as bandwidth allocation, latency constraints, and priority levels to optimize network efficiency while maintaining predictable service quality through controlled parameter modification.
4Measurement precision
If routing apparatus focus on a single or only several requirements at a time, then the optimization for each requirement is thorough and precise, but overall network resource utilization becomes inefficient
Solution Approach 1:
The routing engine merges multiple single-requirement optimization processes into a unified multi-criteria optimization framework. It simultaneously considers bandwidth requirements, latency constraints, QoS parameters, and service level agreements in a single routing decision process, achieving both precise optimization for each requirement type and efficient overall resource utilization through integrated optimization.
Solution Approach 2:
The system employs multi-parameter optimization where routing decisions are made based on simultaneous consideration of multiple requirements parameters. The engine adjusts route selection based on combinations of bandwidth, latency, priority, and other QoS parameters, achieving precise optimization that satisfies multiple requirements concurrently rather than optimizing single parameters in isolation.
Data Source
AI summary
Network simulation equipment for determining routes across a multi-layer system, the network simulation equipment comprising: an adaptor module configured to convert a multi-layer system into a multi-layer network of nodes and links; a first routing engine configured to determine a plurality of populations of paths, each population of paths corresponding to a route across a layer of the multi-layer network; a second routing engine configured to determine a plurality of multi-layer populations of paths, each multi-layer population of paths corresponding to a route across the multi-layer network and comprising populations of paths for at least two different layers of the multi-layer network selected from the plurality of populations of paths determined by the first routing engine; and an evolving module configured to mate at least two multi-layer populations of paths from the plurality of multi-layer populations of paths to create a third multi-layer population of paths.