Network Configuration Changes Using Multi-Timescale Cost Analysis
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Solution Overview
Problem
Existing network optimization methods often result in inefficiencies due to the addition of spare capacity based on worst-case scenarios, leading to suboptimal resource allocation and delayed responses to traffic anomalies, which can disrupt network performance and increase operational costs.
Innovation Solution
A cost-of-change analysis framework that considers the impact and duration of network changes across multiple time scales, allowing for proactive and efficient resource allocation and traffic steering to address stress conditions without over-sizing capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If spare capacity is added based on worst-case scenarios, then network resiliency is improved, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent changes the parameter of capacity planning from static worst-case sizing to dynamic allocation based on probabilistic stress scenarios. By modeling stress occurrence probabilities and durations, the system determines optimal spare capacity levels that balance resiliency requirements with resource efficiency, avoiding both over-provisioning and under-provisioning.
Solution Approach 2:
The patent introduces dynamic traffic steering capabilities that can rapidly redirect traffic around stressed network components in real-time. This dynamic response mechanism allows the network to handle stress conditions adaptively without requiring excessive static spare capacity, thereby improving resource allocation efficiency while maintaining resiliency.
2Speed
If rapid network routing changes are enacted to respond to traffic anomalies, then response speed is improved, but operational complexity increases
Solution Approach 1:
The patent pre-computes and stores optimal routing paths for various stress scenarios before they occur. When a stress condition is detected, the system can immediately enact pre-planned routing changes without performing complex real-time calculations, thereby achieving rapid response while keeping operational complexity manageable through automation.
Solution Approach 2:
The patent implements continuous monitoring of network stress conditions with automated feedback loops that trigger routing changes when thresholds are exceeded. This closed-loop control system automatically detects stress, evaluates the need for rerouting, and enacts changes without human intervention, reducing operational complexity while maintaining fast response times.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining parametric data, analyzing the parametric data to identify a non-ideality with respect to a configuration of a network, performing, based on the analyzing of the parametric data, a cost-of-change analysis to identify whether one or more changes to the configuration of the network are warranted, and based on the performing of the cost-of-change analysis indicating that at least one change of the one or more changes is warranted, modifying the configuration of the network, the modifying resulting in a modified configuration of the network that is different from the configuration of the network. Other embodiments are disclosed.