Memory Anomaly Detection via Garbage Collection Log Analysis
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Solution Overview
Problem
Current methods for detecting memory anomalies in network devices, particularly in software-defined networking (SDN) controllers, are inaccurate due to reliance on memory usage after garbage collection, leading to potential system crashes and network disruptions.
Innovation Solution
A memory anomaly detection method that analyzes log information across a detection time window, incorporating downtime, memory usage before and after garbage collection, and the quantity of garbage collections to determine an anomaly degree, using statistical information and an anomaly degree determining rule derived from historical data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If memory usage after garbage collection is used to determine memory anomaly, then the detection process is simple, but the detection accuracy is low
Solution Approach 1:
The patent segments the memory detection process into multiple dimensions: memory usage before garbage collection, memory usage after garbage collection, downtime duration, and quantity of garbage collections. Each dimension is evaluated separately and then integrated to compute an anomaly degree, thereby improving detection accuracy without overwhelming complexity
Solution Approach 2:
The patent transitions from a single-dimension detection (only memory usage after garbage collection) to multi-dimensional detection by incorporating time window statistics, multiple memory metrics, downtime, and garbage collection frequency. This dimensional expansion significantly improves anomaly detection accuracy
2Measurement precision
If only memory usage after one garbage collection is considered, then the detection method is simple, but the anomaly degree determination is inaccurate
Solution Approach 1:
The patent merges multiple parameters including memory usage before garbage collection, memory usage after garbage collection, downtime, and quantity of garbage collections within a time window. These parameters are combined through statistical processing to determine anomaly degree, achieving accurate detection while managing parameter quantity through integration
3Measurement precision
If statistical information from multiple garbage collections in a time window is used, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies partial action by selecting a representative time window and focusing statistical analysis on that specific period rather than processing all historical data. This approach maintains high detection accuracy while reducing computational power requirements through selective analysis
Data Source
AI summary
A method includes obtaining a first memory log, where the first memory log includes log information of a plurality of garbage collections, and log information of each garbage collection includes a garbage collection time, and includes at least one of a downtime, memory usage after garbage collection, and memory usage before garbage collection, obtaining, based on log information in a first detection time window, first statistical information corresponding to the first detection time window, and determining, based on the first statistical information corresponding to the first detection time window, an anomaly degree corresponding to the log information in the first detection time window.