Abnormality Detection Apparatus for Task Speed Distribution Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In distribution systems, temporary decreases in processing speed of tasks due to server failures, software bugs, data fragmentation, or network congestion can delay job completion, and existing techniques fail to accurately detect such abnormalities in processing speed.
Innovation Solution
An abnormality detection apparatus that acquires progress history information, determines a target range, calculates a task speed distribution, and compares the processing speed of a target task to this distribution to determine if it is abnormal, thereby improving accuracy in detecting speed changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing techniques use static performance index values or average processing speeds for task allocation, then allocation simplicity is maintained, but accuracy in detecting abnormal processing speeds deteriorates
Solution Approach 1:
The system performs preliminary actions by calculating the task speed distribution in advance based on historical progress data before actual task allocation occurs. This pre-computed distribution serves as a reference for detecting abnormalities, allowing the system to maintain simplicity in real-time allocation while achieving high detection accuracy through预先 prepared statistical models.
Solution Approach 2:
The system implements feedback by continuously monitoring actual task progress and comparing it against the pre-calculated task speed distribution. When deviations are detected, the system can trigger alerts or adjust allocations. This feedback mechanism enables accurate abnormality detection while maintaining simple allocation rules, as the complexity is shifted to the monitoring and comparison stage rather than the allocation decision stage.
2Loss of time
If the system monitors processing speed of each task to detect abnormalities, then job completion time is reduced, but system complexity increases
Solution Approach 1:
The system creates a simplified copy or model of the complex monitoring task by using pre-calculated task speed distributions derived from historical data. Instead of implementing complex real-time analysis of multiple parameters, the system compares actual progress against this simplified statistical model, reducing system complexity while maintaining effective abnormality detection capabilities that prevent job delays.
Data Source
AI summary
An abnormality detection apparatus (2000) handles tasks allocated to a plurality of processing servers (3200) as processing targets in a distribution system (3000) having the processing servers (3200). A history acquisition unit (2020) acquires progress history information which is information regarding progress of the plurality of tasks at a plurality of time point of recording. A target range determination unit (2040) determines a target range. A distribution calculation unit (2060) calculates a task speed distribution which is a probability distribution of processing speeds of the tasks using the progress history information regarding the plurality of tasks. An abnormality determination unit (2080) compares a processing speed of a task to be determined with the task speed distribution to thereby determine whether or not the processing speed of the task to be determined is abnormal.