Predictive Behavioral Analytics for IT Operations
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Solution Overview
Problem
IT operations teams face overwhelming numbers of alerts, many of which are false positives, and struggle to manage thresholds and understand complex system issues due to siloed monitoring solutions, leading to inefficiencies and rising costs, with existing tools failing to provide a holistic view of application and environment operations.
Innovation Solution
The implementation of predictive behavioral analytics using machine learning and statistical analysis to collect, analyze, and visualize key performance indicators from multiple data sources, fitting behavioral models to identify abnormal behavior and reduce noise, thereby automating alerting and root-cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring tools are used to collect and analyze performance metrics from multiple applications, then each application can be monitored with specific thresholds, but the complexity of managing multiple unique monitoring tools and thresholds increases significantly
Solution Approach 1:
The patent combines multiple application-specific monitoring tools into a single unified monitoring platform that can handle diverse applications (Exchange, authentication systems, etc.) through a common architecture. This consolidation reduces the number of separate tools needed while maintaining application-specific monitoring capabilities through configurable thresholds and adapters.
Solution Approach 2:
The monitoring system is designed with universal functionality to monitor multiple types of applications and data sources through a single platform. It provides multi-sourced and multi-tiered monitoring capabilities that can adapt to different applications without requiring separate specialized tools for each.
2Reliability
If multiple unique monitoring tools are deployed for different applications, then each application receives dedicated monitoring coverage, but the cost and resource requirements increase
Solution Approach 1:
The patent consolidates multiple monitoring tools into a single unified platform that shares common infrastructure, data collection mechanisms, and analysis engines. This merging reduces redundant resource consumption while maintaining comprehensive monitoring coverage across all applications through efficient resource utilization.
3Reliability
If traditional alerting systems are used to monitor IT operations, then issues can be detected when thresholds are exceeded, but the number of false positive alerts increases and overwhelms IT operations teams
Solution Approach 1:
The patent implements feedback mechanisms where the monitoring system learns from historical data and alert patterns to refine its detection algorithms. This feedback loop enables the system to distinguish between genuine issues and false positives, improving alert accuracy over time and reducing the burden on IT operations teams.
Solution Approach 2:
The system dynamically adjusts monitoring parameters and thresholds based on learned behavioral patterns rather than using static thresholds. This adaptive approach changes the parameters of monitoring based on historical data and contextual information, reducing false positives while maintaining reliable issue detection.
Data Source
AI summary
Example embodiments of the present invention relate to a method, an apparatus, and a computer program product for predictive behavioral analytics for information technology (IT) operations. The method includes collecting key performance indicators from a plurality of data sources in a network. The method also includes performing predictive behavioral analytics on the collected data and reporting on results of the predictive behavioral analytics.