Infrastructure Behavior Analysis via Multi-Dimensional Data Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer infrastructure monitoring systems face challenges in efficiently detecting and diagnosing malfunctions, particularly in complex environments, leading to potential system crashes and financial losses due to the difficulty in identifying abnormal behaviors and performance deviations in real-time.
Innovation Solution
A system and method utilizing an analytics engine that analyzes continuous and asynchronous data to create a multi-dimensional model of the computer infrastructure, visualizing behavior through graphic elements, allowing IT administrators to intuitively detect abnormal performances and malfunctions, and reducing the need for time-consuming data mining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods examining protocols or log files are used, then forensic examination can be conducted, but detection and diagnosis of malfunctions become time-consuming and difficult in complex environments
Solution Approach 1:
The system performs preliminary analysis by continuously collecting and processing data from multiple sources (protocols, log files, performance metrics) to establish baseline behavior patterns before malfunctions occur. This preliminary action enables the system to detect deviations from normal behavior in real-time, eliminating the need for time-consuming post-incident forensic examinations and allowing administrators to respond proactively to emerging issues.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes raw data from various monitoring sources and transforms it into meaningful behavioral patterns and anomalies. This intermediary layer correlates data across multiple devices and processes, making complex infrastructure behavior understandable and enabling rapid diagnosis without requiring administrators to manually examine numerous log files and protocols.
2Reliability
If comprehensive monitoring of complex computer infrastructure is implemented, then malfunction detection capability improves, but system complexity and difficulty of operation increase
Solution Approach 1:
The monitoring system is segmented into distinct functional modules: data collection agents deployed on individual devices, a central correlation engine that processes data from multiple sources, and visualization components that present information in manageable formats. This segmentation allows comprehensive monitoring of complex infrastructure while maintaining operational simplicity, as each module handles specific tasks and the system can be configured incrementally.
Solution Approach 2:
An intermediary correlation engine serves as a mediator between the complex underlying infrastructure and the user interface. This engine automatically correlates data from numerous devices, processes, and log sources, transforming complex multi-dimensional data into simplified behavioral patterns and anomaly detections that are easy for administrators to interpret and act upon.
3Reliability
If real-time behavior analysis is performed on multiple data sources, then abnormal behavior detection improves, but data processing requirements and computational resources increase
Solution Approach 1:
Data processing is performed in preliminary stages where raw data from multiple sources is collected, filtered, and pre-processed into standardized formats before being analyzed for behavioral patterns. This preliminary action reduces the computational burden of real-time analysis by preparing data in advance and identifying obvious anomalies early, allowing the system to focus computational resources on detecting subtle deviations from normal behavior.
Solution Approach 2:
The system implements partial analysis by focusing computational resources on the most critical data sources and the most significant deviations from baseline behavior. Rather than analyzing every data point from every source with equal intensity, the system applies varying levels of analysis depth based on the importance and anomaly level of each data stream, optimizing the balance between detection capability and resource consumption.
Data Source
AI summary
The field of the disclosure relates generally to a method and system for analyzing behavior of a computer infrastructure and the displaying the behavior of the computer infrastructure in a graphical manner. The system comprises an analytical engine connected to agents running on devices in the computer infrastructure and analyzing continuous data and asynchronous data.