Network Function Scaling Trigger Conditions
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Solution Overview
Problem
Current methods for determining trigger conditions for scaling network functions in communications networks are inadequate, as they fail to accurately identify load states and underlying causes, leading to inefficient resource allocation and scaling actions.
Innovation Solution
A method that receives metrics data to identify primary and secondary indicators of load states, uses machine learning techniques to derive secondary trigger conditions based on historical patterns, and predicts future metric values to determine optimal scaling actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods are used to determine trigger conditions for scaling network functions, then the system is simple to operate, but the accuracy of identifying load states and their causes is insufficient
Solution Approach 1:
The patent segments the scaling determination process into multiple distinct components: primary indicator identification (direct load state indicators), secondary indicator identification (underlying cause indicators), and predictive analysis. This segmentation allows the system to systematically analyze different aspects of load states separately, improving identification accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimension of analysis by deriving secondary indicators that reveal underlying causes of load states, rather than only monitoring primary load metrics. This dimensional expansion from surface-level load monitoring to root-cause analysis enables more precise identification of load states and their causes, transforming the system from reactive to proactive scaling.
2Productivity
If scaling actions are triggered based on current methods, then the response time is fast, but the resource allocation efficiency deteriorates due to inaccurate load state identification
Solution Approach 1:
The patent implements feedback mechanisms where secondary indicators provide information about the underlying causes of load states, which feeds back into the scaling decision process. This feedback loop allows the system to adjust scaling trigger conditions based on root-cause analysis, improving both the accuracy of scaling decisions and the efficiency of resource allocation by avoiding unnecessary scaling actions.
Solution Approach 2:
The patent performs preliminary analysis by identifying secondary indicators and underlying causes before triggering scaling actions. By deriving predictive models and analyzing root causes in advance, the system can make more accurate scaling decisions, improving resource allocation efficiency while maintaining high reliability of scaling trigger conditions.
3Loss of information
If only primary load indicators are monitored, then the system complexity is low, but the ability to identify underlying causes of load states is insufficient
Solution Approach 1:
The patent creates a multi-functional metrics analysis system where the same monitoring infrastructure serves dual purposes: tracking primary load indicators for immediate scaling decisions and deriving secondary indicators for root-cause analysis. This universal approach maximizes information utilization from existing metrics while minimizing additional system complexity.
Data Source
AI summary
A method of determining trigger conditions for scaling a scalable unit of network function comprising identifying a primary set of metrics associated with usage of an instance of the unit of network function as a primary indicator of occurrence of a load state thereof, and determining usage points when the primary indicator indicates that the load state occurs. Deriving a secondary set of the metrics, different to the primary set, as a secondary indicator of occurrence of the load state of the instance at each of a group of one or more of the usage points when the primary indicator indicates that the load state occurs, and measured data corresponding to values of the metrics in the secondary set of metrics at each of the group of usage points. Storing a trigger condition for scaling the unit of network function based on the secondary set and the measured data.