Alert Optimization Platform for Server Infrastructure
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Solution Overview
Problem
Large computing environments face challenges in distinguishing between transient and genuine issues from server alerts, leading to inefficient management and resource allocation.
Innovation Solution
A computing platform that receives and filters server alerts, identifies trends and drifts, generates new alert rules, and updates configuration settings to prioritize genuine alerts, reducing false positives and optimizing network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all server alerts are monitored and sent to administrators, then comprehensive system monitoring is achieved, but alert management efficiency deteriorates due to false positives and transient issues
Solution Approach 1:
The patent introduces an intermediary alert optimization computing platform between the server infrastructure and administrators. This platform receives alerts from servers, applies machine learning models to filter transient issues and false positives, and only sends verified genuine alerts to administrators. The intermediary resolves the contradiction by maintaining comprehensive monitoring while improving alert management efficiency through intelligent filtering.
Solution Approach 2:
The alert optimization computing platform performs self-service by automatically analyzing alerts using machine learning models, generating new alert rules, and continuously optimizing its own configuration. The system autonomously distinguishes between genuine and transient alerts without requiring manual configuration or intervention, thereby maintaining high monitoring coverage while improving efficiency.
2Adaptability or versatility
If multiple servers are incorporated into the computing environment, then system capacity and functionality are improved, but the number of alerts and complexity of management increase
Solution Approach 1:
The alert optimization computing platform provides universal functionality that handles alerts from multiple diverse servers and applications through a single unified system. The machine learning models are trained on data from various server types and application stacks, enabling the platform to universally process and analyze alerts across the entire computing environment regardless of server diversity, thereby managing complexity centrally.
Solution Approach 2:
The platform acts as a central intermediary that consolidates alerts from numerous servers into a single processing point. By routing all server alerts through this intermediate layer for unified analysis and filtering, the system maintains high computing environment capacity while reducing management complexity through centralized intelligence.
3Productivity
If alert filtering and analysis are performed to distinguish genuine issues, then alert management efficiency is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical alert data and pre-configuring alert rules before actual alert processing begins. This preliminary preparation enables the system to quickly filter and analyze alerts in real-time without requiring extensive processing during critical alert events, thereby improving efficiency while minimizing time loss.
Solution Approach 2:
The alert optimization platform applies partial action by focusing computational resources on analyzing only the most critical alert features and patterns rather than processing every detail of each alert. The machine learning models are designed to identify key indicators of genuine issues with minimal processing overhead, achieving high efficiency while maintaining acceptable processing time.
Data Source
AI summary
Aspects of the disclosure relate to monitoring and managing computer networks by parsing and optimizing runtime infrastructure alerts. A computing platform may receive, from a server controller device associated with server infrastructure, alert information identifying a set of alerts associated with the server infrastructure. The computing platform may apply a pre-analyzer filter to the alert information to obtain a filtered set of alerts. Subsequently, the computing platform may identify alert trends and alert drifts associated with a set of applications hosted by the server infrastructure. The computing platform may generate a set of new alert rules based on the alert trends and the alert drifts, and may store updated configuration settings incorporating the set of new alert rules. Then, the computing platform may send, to an administrative computing device, a set of verified alerts based on the updated configuration settings incorporating the set of new alert rules.