Dynamic Alarm Smoothing for Network Element Outage Response
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Solution Overview
Problem
Network monitoring systems generate large volumes of notifications for network element outages, many of which resolve on their own within a short period, leading to unnecessary resource allocation and inefficient troubleshooting.
Innovation Solution
Implement a dynamic alarm smoothing interval based on a machine learning model that considers factors like alarm patterns, device type, service impact, and external events to determine optimal delay before generating trouble tickets, using techniques such as feature engineering, time-series analysis, and natural language processing to customize smoothing intervals for each network element.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alarm smoothing intervals are extended to filter out transient outages, then false alarms are reduced, but response time to critical outages increases
Solution Approach 1:
The patent implements dynamic alarm smoothing intervals that automatically adjust their duration based on the specific characteristics of each alarm and network element. Instead of using fixed smoothing intervals, the system calculates variable intervals considering factors like alarm frequency, network element criticality, and historical patterns, allowing critical alarms to be processed faster while still filtering transient false alarms effectively
Solution Approach 2:
The system changes the parameter of alarm smoothing interval duration based on multiple inputs including alarm type, network element attributes, and historical data. By dynamically adjusting this parameter rather than maintaining a constant value, the system optimizes both false alarm reduction and response time for different scenarios
2Productivity
If manual adjustment of alarm smoothing intervals is used, then resource allocation can be optimized, but system complexity and operational overhead increase
Solution Approach 1:
The patent implements a self-adjusting alarm smoothing system that automatically determines appropriate intervals without requiring manual configuration or intervention. The system uses machine learning models to analyze historical alarm data, network element characteristics, and current conditions to autonomously optimize smoothing intervals, eliminating the need for manual adjustment while maintaining high resource allocation efficiency
Solution Approach 2:
The system incorporates feedback mechanisms where alarm outcomes and resolution data are continuously fed back into the machine learning model. This feedback loop allows the system to learn from past performance and automatically refine smoothing interval decisions, reducing operational overhead while improving resource allocation over time
3Device complexity
If fixed alarm smoothing intervals are applied to all network elements, then system complexity is reduced, but alarm accuracy and service level agreement compliance deteriorate
Solution Approach 1:
The patent applies different alarm smoothing intervals to different network elements based on their specific characteristics, criticality levels, and historical behavior. Instead of using a uniform interval for all elements, the system tailors the smoothing interval to each network element's local requirements, ensuring optimal performance and SLA compliance for each while maintaining manageable system complexity through automated differentiation
Data Source
AI summary
A processing system including at least one processor may detect an outage associated with at least one network element, apply at least one attribute of the at least one network element as at least a first input of a plurality of inputs to a machine learning model that is implemented by the processing system to obtain an output of the machine learning model indicative of an alarm smoothing interval, and present, in accordance with the alarm smoothing interval, an alarm indicative of the outage associated with the at least one network element.