Data Center Root Cause Analysis System
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Solution Overview
Problem
Conventional data center monitoring systems generate overwhelming amounts of information, making it difficult to isolate the root cause of alarms and determine appropriate corrective actions in large-scale data center installations.
Innovation Solution
A system and method utilizing a data center management device that receives event indications from physical infrastructure devices, determines generic cause models, adapts them using data center profile information, and displays probabilities to identify potential root causes, along with displaying corrective actions and initiating them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional monitoring systems monitor all equipment in large data centers, then comprehensive coverage is achieved, but the amount of alarm information becomes overwhelming and root cause isolation becomes difficult
Solution Approach 1:
The patent introduces an intermediary system that sits between the monitoring sensors and the alarm generation system. This intermediary analyzes sensor data and generates consolidated alarms with identified root causes, rather than passing all raw sensor data directly to the alarm system. This reduces the complexity of information reaching operators while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The system extracts and identifies the root cause from among multiple potential causes by analyzing the relationships between alarms and equipment. Instead of presenting all possible causes, the system extracts the most likely root cause and presents it prominently, reducing the information complexity operators must process.
2Loss of information
If all alarm information is presented to personnel, then complete information availability is achieved, but the difficulty of determining appropriate corrective action increases
Solution Approach 1:
The system applies local quality by providing different levels of information detail to different users or contexts. The interface presents summarized root cause information prominently for quick decision-making, while still allowing access to complete detailed information when needed. This allows operators to quickly determine corrective actions without being overwhelmed by comprehensive data.
Solution Approach 2:
The alarm information is segmented into hierarchical levels: root cause identification, affected equipment lists, and detailed alarm information. This segmentation allows operators to first address the root cause and then systematically work through affected equipment, making corrective action determination more manageable.
3Measurement precision
If generic cause models are adapted to specific data centers using profile information, then accuracy of root cause identification is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-adapting generic cause models with data center profile information during system setup or initialization. This creates customized cause models specific to each data center's equipment and configurations beforehand, so that during operation, the system can quickly and accurately identify root causes without performing complex adaptation in real-time, thus balancing accuracy with operational simplicity.
Data Source
AI summary
Systems and methods for determining the root cause of an event in a data center are presented. The system includes a data center management device coupled to a network and configured to receive an indication of the event from a physical infrastructure device via the network, determine a first generic cause model for the event by accessing an event cause model data store, determine a first event profile by adapting the first generic cause model to the data center using data center profile information stored in a data center profile data store and display a first probability that a potential cause defined by the first event profile is the root cause.