Network KPI Analysis for Quantifying Automated Event Impact
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network management systems fail to quantify the operational cost savings and resource consumption generated by automated network events, leading to inefficiencies and underutilization of these events.
Innovation Solution
A management system that quantifies network changes by analyzing key performance indicators (KPIs), ranks them, and associates them with automated network events to calculate network changes, enabling informed resource allocation and discontinuation or enhancement of events based on their performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated network events are implemented to manage network configurations, then network management efficiency is improved, but the ability to quantify operational cost savings and resource consumption deteriorates
Solution Approach 1:
The system implements feedback mechanisms by collecting KPI data from network events, analyzing the impact of automated events on network performance, and using this information to quantify operational cost savings. The feedback loop continues by using quantified savings information to optimize future automated network events, thereby resolving the information loss problem while maintaining productivity improvements.
Solution Approach 2:
The patent introduces an intermediary analysis system that acts as a mediator between automated network events and operational cost quantification. This intermediary component processes KPI data, calculates the impact of automated events, and generates quantified savings metrics, enabling the measurement of operational cost savings without disrupting the automated network management workflow.
2Reliability
If automated network events are used to adjust parameters, then network performance optimization is improved, but resource consumption and operational cost transparency deteriorate
Solution Approach 1:
The system uses feedback by continuously monitoring network performance KPIs before and after automated parameter adjustments, calculating the actual performance improvement, and using this information to quantify resource consumption. This feedback mechanism provides transparency on resource usage while maintaining network performance optimization.
Solution Approach 2:
The patent replaces manual resource accounting with an automated analytical system that processes KPI data and calculates resource consumption metrics. This substitution of manual mechanical accounting with automated data analysis provides transparent quantification of resource consumption associated with automated network events.
3Measurement precision
If manual network management is used, then control precision is maintained, but productivity and operational efficiency deteriorate
Solution Approach 1:
The system segments network management into two parts: automated event execution for routine parameter adjustments (improving productivity) and targeted manual intervention for complex scenarios requiring human judgment (maintaining control precision). The segmentation allows each approach to operate in its optimal domain, combining the benefits of automation and human expertise.
Solution Approach 2:
The patent implements dynamic management by allowing the system to automatically adjust the degree of automation based on network conditions and event complexity. The control mechanism dynamically switches between automated and manual modes, maintaining operational efficiency during routine operations while preserving control precision when needed through adaptive decision-making.
Data Source
AI summary
A device may receive key performance indicators (KPIs) associated with a network, and may rank a set of the KPIs associated with anomalous data to generate a list of ranked KPIs. The device may identify a worst performing KPI and a best performing KPI based on the list of ranked KPIs, and may join and filter the KPIs based on the worst performing KPI and the best performing KPI and to generate intermediate KPIs. The device may modify the intermediate KPIs based on customer experience prioritization data and to generate final KPIs, and may associate the final KPIs with automated network events to calculate a network change generated by the automated network events. The device may perform one or more actions based on the network change generated by the automated network events.