Elastic Insight for IT Performance Data Bottleneck Detection
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Solution Overview
Problem
Existing IT monitoring solutions in datacenters and IT infrastructures face challenges in providing timely insights into performance issues, as historical data is not readily available at the front-end, leading to delayed problem recognition and user frustration despite extensive data storage at the back-end.
Innovation Solution
A method and system for providing 'elastic insight' by continuously pushing local performance data from the front-end to the back-end and global performance data from the back-end to the front-end, enabling a hierarchical view and aggregation of data to identify performance bottlenecks across IT infrastructure levels, using compression algorithms to manage vast amounts of data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If extensive performance data is stored at the back-end, then historical data availability is improved, but data accessibility speed and problem recognition time deteriorate
Solution Approach 1:
The patent segments the centralized back-end storage into distributed front-end caches at multiple levels (e.g., L1, L2, L3 caches). This segmentation allows historical data to be stored remotely while enabling fast local access to recent data, resolving the contradiction between maintaining extensive historical data availability and achieving fast problem recognition.
Solution Approach 2:
The system performs preliminary action by proactively pushing performance data from the back-end to front-end caches before problems occur. This ensures that historical data is already available at the front-end when needed, eliminating the time delay in problem recognition while maintaining comprehensive historical data access.
2Measurement precision
If performance data is aggregated at multiple hierarchical levels, then problem identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal data aggregation framework where the same hierarchical caching structure serves multiple functions: performance data storage, rapid access, historical data retrieval, and multi-level analysis. This multi-functionality enables accurate performance bottleneck detection across different granularities without requiring separate complex systems for each function.
Solution Approach 2:
The system uses a nested hierarchical cache structure where front-end caches are contained within a larger back-end storage system. This nesting allows the system to maintain multiple levels of data aggregation (device level, server level, datacenter level) while managing complexity through a unified, hierarchical organization rather than separate independent systems.
3Speed
If real-time monitoring is implemented across the entire IT infrastructure, then response speed to failures is improved, but data processing load and resource consumption increase
Solution Approach 1:
The patent applies partial action by implementing real-time monitoring and data pushing only for performance-critical data at the front-end caches, while maintaining a simpler structure at the back-end. This selective approach enables fast failure detection for the most important data without unnecessarily processing the entire IT infrastructure in real-time, thereby reducing overall resource consumption.
Data Source
AI summary
Elastic insight to Information Technology (“IT”) performance data is provided. Local performance data is continuously pushed from a front-end component to a back-end component. Global performance data is continuously pushed from the back-end component to the front-end component. The local performance data and the global performance data are aggregated at the front-end component by product, product family, and product solution. The aggregated data is monitored at the front-end component to identify a performance bottleneck.