AR and Cognitive Analytics for Data Center Incident Diagnosis
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Solution Overview
Problem
Data centers face challenges in diagnosing incidents efficiently due to the complexity of their IT infrastructures and the high correlation between the number of changes and incidents, which existing technologies fail to address effectively.
Innovation Solution
The implementation of a method using augmented reality (AR) and cognitive analytics to diagnose data center incidents by receiving incident reports, applying AR for evidence collection, and utilizing a cognitive analytical process to determine the root cause through statistical inference, with graphical models like Bayesian networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional diagnostic methods are used in data centers with hundreds of thousands of devices, then the diagnostic process becomes increasingly complex and time-consuming, but the system complexity and number of IT assets continue to increase
Solution Approach 1:
The diagnostic system segments the complex IT infrastructure into manageable components by using AR to guide technicians through step-by-step diagnostic procedures for specific device types (servers, network devices, UPS). The system divides the diagnostic process into discrete tasks that can be executed sequentially, reducing the overwhelming complexity of diagnosing hundreds of thousands of devices.
Solution Approach 2:
The patent introduces a cognitive analytics engine as an intermediary between the technician and the complex IT infrastructure. This intermediary processes diagnostic data, applies statistical inference, and provides guided recommendations through the AR interface, effectively mediating between human operators and the complex system without requiring the technician to directly manage all system complexity.
2Measurement precision
If manual diagnostic procedures are used to identify root causes in complex IT infrastructures, then diagnostic accuracy may be insufficient, but the complexity of troubleshooting increases with the number of changes and incidents
Solution Approach 1:
The system implements continuous feedback loops where diagnostic data from IT devices is collected, analyzed by the cognitive analytics engine, and results are fed back to the technician through the AR interface. The system refines diagnostic accuracy by iteratively processing additional evidence and updating root cause probabilities, allowing technicians to follow guided paths based on previous diagnostic outcomes.
Solution Approach 2:
The patent replaces manual diagnostic reasoning with automated cognitive analytics that use statistical inference and Bayesian networks. Instead of relying on technician expertise alone, the system substitutes mechanical analysis with computational algorithms that process diagnostic data, calculate probabilities, and provide objective root cause identification, thereby improving diagnostic accuracy beyond human capabilities.
3Reliability
If comprehensive incident evidence collection is performed across all IT devices, then diagnostic thoroughness improves, but the time and resources required for evidence collection increase significantly
Solution Approach 1:
The system applies partial action by collecting only the specific evidence needed for the current diagnostic context rather than comprehensively examining all IT devices. The cognitive analytics engine determines which devices and data points are relevant based on the incident type and initial symptoms, guiding technicians to collect evidence from only the necessary subset of the infrastructure, thus maintaining diagnostic thoroughness while improving efficiency.
Solution Approach 2:
The system performs preliminary actions by using the cognitive analytics engine to pre-analyze incident reports and predict potential root causes before evidence collection begins. This preliminary analysis guides the AR interface to prioritize evidence collection from specific devices and locations, allowing technicians to focus on high-probability areas first and collect comprehensive evidence more efficiently by following a pre-planned diagnostic path.
Data Source
AI summary
One embodiment provides a method for diagnosing data center incidents including receiving a data center incident report including information technology (IT) device incident information. Augmented reality (AR) is applied for an AR interface for receiving incident evidence information based on the IT device incident information. The incident evidence information is sent to a cognitive analytical process. Using the cognitive analytical process, statistical inference is determined and an incident diagnosis recommendation including analytical results is generated. The analytical results are received by the AR interface for determining a root cause of the incident report.