Storage Node Abnormal Behavior Detection via Signal Pattern Analysis
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Solution Overview
Problem
IT operators face challenges in predicting storage system performance and capacity needs, as well as timely detection and response to abnormal access behaviors in shared storage systems.
Innovation Solution
A method and system for a computer storage node in a distributed shared storage system, featuring a user interface, sensor module, and processor to generate and analyze detection signals, allowing operators to define normal and abnormal patterns, and compare these with incoming signals to predict behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated detection systems are implemented to monitor storage system performance and detect abnormal behaviors, then detection speed and reliability are improved, but system complexity and operational difficulty increase
Solution Approach 1:
The detection system segments the storage system into multiple monitored components (storage nodes, access patterns, performance parameters) and analyzes them independently. The abnormal behavior detection method divides the complex monitoring task into specific detection dimensions including access frequency, data pattern changes, and performance parameter deviations, making the complex system manageable and easier to operate through structured analysis frameworks.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes raw detection data into meaningful patterns. This intermediary layer includes pattern recognition modules and behavior analysis algorithms that mediate between raw sensor data and operational decisions, reducing the complexity burden on operators by providing pre-processed insights and automated anomaly identification.
2Device complexity
If manual monitoring and analysis methods are used to identify abnormal behaviors, then system complexity is reduced, but detection speed and response time worsen
Solution Approach 1:
The system performs preliminary actions by continuously collecting, storing, and pre-processing storage system data in a structured format. Historical data is maintained and indexed to enable rapid comparison with current patterns. This preliminary preparation allows the system to quickly identify deviations from normal behavior without requiring complex real-time analysis during actual detection events.
Solution Approach 2:
The patent replaces manual mechanical monitoring processes with automated electronic detection and pattern recognition systems. The abnormal behavior detection method uses algorithmic analysis of data patterns, statistical anomalies, and machine learning models to substitute human operators' manual inspection, thereby significantly increasing detection speed while maintaining manageable system complexity through automation.
3Measurement precision
If comprehensive monitoring of all storage parameters is implemented, then detection precision and reliability are improved, but operational difficulty and time consumption increase
Solution Approach 1:
The detection system applies local quality by focusing monitoring and analysis on specific critical parameters and patterns rather than uniformly treating all storage data equally. The method identifies and prioritizes key indicators such as access frequency thresholds, data pattern anomalies, and performance parameter deviations that are most indicative of abnormal behavior, allowing high-precision detection of critical issues without wasting time analyzing all parameters uniformly.
Solution Approach 2:
The patent implements partial action by selectively monitoring and analyzing the most relevant storage parameters and behaviors rather than comprehensively tracking every possible parameter. The abnormal behavior detection method uses statistical sampling, pattern recognition, and prioritization algorithms to focus on the most indicative parameters, achieving sufficient detection precision while reducing operational time by avoiding unnecessary analysis of less relevant data.
Data Source
AI summary
A method utilized in a computer storage node includes: providing user interface device to be operated by an operator; sensing operation parameter of computer storage node to generate a first detection signal; controlling a display panel of user interface device to display data pattern of first detection signal on display panel according to a time scale; using first portion of first detection signal corresponding to a partial pattern of the data pattern to generate reference signal when the operator uses user interface device to mark a region on display panel to select the partial pattern; and comparing characteristics of the reference signal with characteristics of a processed detection signal to perform a behavior prediction operation.