Self-Tuned Adaptive Routing for Network Utilization Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional routing methods in IP/MPLS networks fail to efficiently balance network utilization and link utilization, leading to suboptimal path selection that can result in link congestion and reduced revenue generation due to inefficient resource allocation.
Innovation Solution
A self-tuned adaptive routing system that uses a Path Computation Engine (PCE) to dynamically balance link utilization and network utilization objectives by determining adaptable path selection values, optimizing network capacity and reducing congestion through a method that considers both objectives in real-time network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional routing methods use minimum hop count or minimum maximum link utilization to select paths, then path selection is simplified, but network utilization and link utilization cannot be efficiently balanced
Solution Approach 1:
The patent implements dynamic path selection values that adapt to changing network conditions. The PCE continuously monitors network state and adjusts path selection values in real-time, transforming static routing into a dynamic system that can balance multiple objectives (network utilization, link utilization, hop count) based on current conditions, thereby resolving the contradiction between simple path selection and efficient network utilization.
Solution Approach 2:
The system changes routing parameters dynamically by adjusting path selection values based on network utilization metrics. Instead of using fixed routing parameters, the PCE modifies these parameters adaptively to optimize the balance between network utilization and link utilization, enabling efficient resource allocation while maintaining operational simplicity through automated parameter adjustment.
2Reliability
If routing paths are selected based on minimum maximum link utilization, then link congestion is reduced, but network capacity utilization is not optimized
Solution Approach 1:
The PCE dynamically adjusts path selection values to balance link utilization and network capacity utilization. By changing these parameters adaptively based on real-time network conditions, the system can prevent link congestion while simultaneously optimizing overall network capacity utilization, resolving the contradiction between reliability and productivity.
Solution Approach 2:
The system implements feedback mechanisms where the PCE continuously monitors network utilization metrics and uses this information to adjust path selection decisions. This closed-loop control enables the system to respond to changing conditions, preventing congestion while maximizing capacity utilization through adaptive feedback-driven routing decisions.
3Productivity
If routing paths are selected based on minimum hop count, then routing efficiency is improved, but link congestion increases
Solution Approach 1:
The system dynamically modifies path selection values to balance routing efficiency and link congestion prevention. By adjusting these parameters based on real-time network conditions, the PCE can select paths that maintain efficient routing while avoiding congested links, thereby resolving the contradiction between productivity and reliability.
Solution Approach 2:
The patent transforms static minimum-hop routing into a dynamic system that adapts to network conditions. The PCE continuously adjusts path selection based on current link utilization states, enabling the system to maintain routing efficiency while dynamically avoiding congested links, thus resolving the contradiction between speed and reliability.
4Productivity
If the system adaptively tunes path selection values based on network utilization, then network efficiency and revenue generation improve, but system complexity increases
Solution Approach 1:
The PCE serves as an intermediary that centralizes the complex adaptive routing computations. By placing the path computation engine as a separate intermediary component, the system can implement sophisticated adaptive routing logic without increasing the complexity of individual network nodes, thereby resolving the contradiction between improved productivity and increased device complexity.
Solution Approach 2:
The feedback-driven adaptive tuning mechanism automates the complexity management by using real-time network metrics to automatically adjust path selection values. This self-tuning capability reduces the need for manual configuration and complex centralized control, enabling high network efficiency while managing system complexity through automated feedback loops.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Various embodiments provide a method and apparatus for providing adaptive self-tuned routing within a network. In particular, one or more path selection values are adaptable to the changing network utilization and are configured to balance the influence of a first objective and a second objective on the path selection. Advantageously, balancing the influence of the first and second objectives on path selection provides improved efficiency and improved revenue generating capacity when compared to conventional routing methods.