Information Processing System Proactive Fault Detection
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Solution Overview
Problem
Existing information processing systems face inefficiencies in fault identification and resolution, as they rely solely on investigation data collected after a fault occurs, which can be insufficient and time-consuming, leading to user inconvenience and increased costs for administrators.
Innovation Solution
An information processing system that acquires and stores resource utilization information prior to and after a fault, using artificial intelligence learned through machine learning to select solutions based on this data, enabling proactive fault management and reducing the need for extensive post-fault data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only investigation data after fault occurrence is collected, then data collection simplicity is maintained, but fault identification accuracy deteriorates
Solution Approach 1:
The system performs preliminary data collection by continuously acquiring resource utilization information before faults occur. The determination unit identifies normal operation patterns and stores baseline data in advance, enabling accurate fault diagnosis when anomalies occur without needing to collect extensive post-fault data.
Solution Approach 2:
The determination unit acts as an intermediary that continuously monitors resource utilization and compares actual data against pre-established normal patterns. This mediator identifies deviations indicating faults, enabling accurate detection without requiring complex post-fault investigation data collection systems.
2Reliability
If extensive post-fault data collection is performed, then fault analysis completeness is improved, but user downtime increases
Solution Approach 1:
By continuously collecting and analyzing resource utilization data during normal operation, the system builds a comprehensive understanding of system behavior before faults occur. This preliminary analysis enables rapid fault identification when anomalies happen, eliminating the need for extended post-fault data collection and reducing user downtime.
Solution Approach 2:
The system implements continuous feedback by monitoring resource utilization in real-time and comparing it against established normal patterns. When deviations are detected, the system immediately identifies potential faults, enabling proactive response without requiring extensive post-fault investigation, thus reducing downtime while maintaining analysis completeness.
3Reliability
If real-time resource utilization monitoring is implemented, then proactive fault detection is enabled, but system complexity increases
Solution Approach 1:
The determination unit automatically monitors resource utilization, compares data against pre-stored normal patterns, and identifies faults without requiring external intervention or complex monitoring infrastructure. The system serves itself by maintaining baseline data and performing continuous self-diagnosis, enabling proactive fault detection with minimal added complexity.
Solution Approach 2:
The system implements automated feedback loops where resource utilization data is continuously collected, compared against normal operation patterns, and used to detect anomalies. This self-regulating mechanism enables proactive fault detection through straightforward monitoring and comparison logic rather than complex predictive analytics.
Data Source
AI summary
An information processing system includes a first and a second information processing apparatus including a memory and one or more processors. In the first information processing apparatus, the processor is configured to acquire information on utilization of a resource utilized in the operation and cause the memory to store information, and with a condition satisfied with the information on the utilization of the resource, output to second information processing apparatus the information on the utilization of the resource from memory. In the second information processing apparatus, the processor is configured to cause the memory to store information on the utilization of the resource output from first information processing apparatus and select a solution responsive to the information on the utilization of the resource stored on the memory via artificial intelligence that has learned through machine learning to select the solution responsive to the information on the utilization of the resource.