Confidence Intervals for KPIs in Communication Networks
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Solution Overview
Problem
Existing network monitoring systems lack the ability to accurately calculate and present confidence intervals for Key Performance Indicators (KPIs), relying on fixed global sampling ratios that render KPI extrapolations futile and fail to account for varying network conditions, leading to inadequate decision-making in carrier service provider operations.
Innovation Solution
The system calculates confidence intervals for KPIs by identifying vectors representing network events with adaptive sampling ratios, estimating the number of events without sampling, and determining standard deviations, allowing for dynamic adjustment of sampling ratios based on network loading and user input to improve data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed global sampling ratio is used for monitoring network events, then the system complexity is reduced and ease of operation is improved, but the measurement precision of KPIs deteriorates and reliability decreases
Solution Approach 1:
The patent implements dynamic sampling ratios that automatically adjust based on network conditions, event types, and confidence level requirements. Instead of using a fixed global sampling ratio, the system calculates and applies different sampling ratios for different network events and dimensions, thereby maintaining measurement precision while managing system complexity through automated adaptation.
Solution Approach 2:
The system changes the sampling ratio parameter dynamically based on network conditions, event characteristics, and desired confidence levels. By adjusting this key parameter, the system optimizes the balance between measurement precision and resource utilization, allowing high-precision monitoring when needed and reduced monitoring when acceptable confidence levels are already achieved.
2Measurement precision
If adaptive sampling with variable ratios is implemented for different network events, then the measurement precision and reliability of KPIs are improved, but the device complexity increases
Solution Approach 1:
The system performs self-service by automatically calculating confidence levels and adjusting sampling ratios without requiring manual intervention. The monitoring system autonomously determines when higher precision is needed and adjusts its own operation accordingly, reducing the need for complex external control mechanisms while maintaining high measurement precision.
Solution Approach 2:
The system implements feedback loops where confidence levels are continuously calculated based on observed network events and sampling ratios. This feedback information is then used to adjust future sampling decisions, creating a self-regulating system that maintains measurement precision while adapting to changing network conditions without requiring complex external management.
3Reliability
If confidence intervals are calculated and presented for KPIs, then the reliability and measurement precision are improved, but the loss of time for processing and presenting data increases
Solution Approach 1:
The system performs preliminary calculations of confidence levels and sampling ratios based on initial observations and network conditions. By pre-calculating these parameters before final KPI determination, the system reduces the time needed for final processing while maintaining high reliability, as the foundational statistical parameters are already established.
Data Source
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AI summary
Systems and methods for calculating and presenting confidence interval(s) for key performance indicator(s) (KPIs)are described. For example, in some embodiments, a method may include identifying vectors representing network events observed by a network monitoring system, each vector including: a dimension, an indication of a sampling ratio with which a respective event was observed, and a value associated with the dimension. The method may also include calculating a KPI corresponding to the observed events for the dimension based, at least in part, upon the values. The method may further include calculating a confidence associated with the KPI, based, at least in part, upon the sampling ratios. In some cases, events may be observed with different sampling ratios. Additionally or alternatively, sampling ratios may include adaptive sampling ratios controlled by the network monitoring system in response to network or resource loading (e.g., subject varying over time), whitelist differentiated sampling ratios, etc.