Application Monitoring System for Predictive Event Remediation
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Solution Overview
Problem
Existing systems lack the ability to proactively predict and mitigate unplanned events in system-wide applications, such as outages or security breaches, by efficiently monitoring and adapting to changing application states without overstraining computing resources.
Innovation Solution
A comprehensive application monitoring system that analyzes event data to generate dynamic testing strategies, applies these strategies in real-time, and alerts managing entities to potential future events with instructions for preventative actions, utilizing past event data to prioritize testing and reduce computational strain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring of event data with multiple variables is implemented to predict future events, then the ability to proactively detect and mitigate unplanned events is improved, but the computational resources and system complexity increase
Solution Approach 1:
The monitoring system is divided into multiple independent testing sequences, each focused on specific event types and variables. This segmentation allows the complex monitoring task to be broken down into manageable, specialized components that can be executed independently, reducing overall system complexity while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The system pre-calculates and stores testing sequences based on historical event data and likelihood scores before actual event prediction is needed. By preparing test configurations, variable selections, and threshold criteria in advance, the system reduces real-time computational requirements and complexity during active monitoring operations.
2Measurement precision
If multiple testing sequences with multiple tests each are applied to event data, then the precision of event prediction is improved, but the time required for analysis and processing increases
Solution Approach 1:
The system dynamically selects and applies only the most relevant testing sequences based on current event data characteristics and historical patterns. Rather than executing all possible tests uniformly, the system adapts which testing sequences are applied based on real-time conditions, reducing unnecessary processing time while maintaining high prediction precision through targeted test selection.
Solution Approach 2:
The system uses historical event datasets to create representative test scenarios and likelihood models that can be replicated and applied to current event data. By copying patterns from past events and applying them through standardized testing sequences, the system achieves high prediction precision without needing to analyze every possible variable combination from scratch.
3Measurement precision
If comprehensive event data analysis with multiple variables is performed continuously, then the accuracy of predicting unplanned events is improved, but the computing resources consumed increase
Solution Approach 1:
The system changes parameters such as which variables are monitored, what thresholds are applied, and which testing sequences are executed based on current system state, historical patterns, and event likelihood scores. By dynamically adjusting monitoring parameters rather than maintaining fixed comprehensive monitoring, the system achieves high prediction accuracy while reducing computing resource consumption through selective analysis.
Solution Approach 2:
The system automatically selects relevant variables, determines appropriate testing sequences, and adjusts monitoring intensity based on its own analysis of historical data and current event patterns. This self-service capability eliminates the need for external configuration and optimization, allowing the system to maintain high accuracy while efficiently managing its own computing resources without constant human intervention.
4Reliability
If real-time monitoring and dynamic testing strategy application is implemented, then the ability to minimize impact of future events is improved, but the system complexity and processing requirements increase
Solution Approach 1:
The system prepares multiple testing sequences and predetermined responses in advance based on historical event data. When events are detected, the system can immediately apply pre-configured testing strategies and mitigation protocols, reducing the need for complex real-time decision-making while maintaining the ability to effectively respond to and mitigate unplanned events.
Data Source
AI summary
Embodiments of the invention relate to an application monitoring and unplanned event remediation system, which analyzes event data to generate dynamic testing strategies and then apply said testing strategies in real-time to predict future events. The invention utilizes past event data to prioritize testing of particular application conditions, allowing for continuous monitoring without creating unnecessary strain on available computing resources. Because a variety of application operating states can result in unplanned events, the present invention provides the functional benefit of adapting testing strategies in real-time as an application's operating state changes, increasing the likelihood of correctly predicting a future event. The present invention also provides a system of alerting a managing entity system to a future event, as well as providing instructions for preventative or remedial actions which may serve to lessen the potential impact of an unplanned event on a managing entity.