Memory Spike Detection via Watermark and Standard Deviation Analysis
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Solution Overview
Problem
Existing systems fail to effectively detect memory spikes in computer systems, which can lead to system instability and potential full reboots, as they do not account for temporary large increases in memory usage that may not contribute to long-term trends.
Innovation Solution
A system that uses configurable criteria, such as watermarks and standard deviations, to detect spikes in memory usage across different types of memory (private, shared, and total) by analyzing current usage and instance counts, allowing for immediate reporting and corrective actions like process termination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing memory monitoring systems are used, then system operation continues, but memory spikes are not detected leading to system instability and full reboots
Solution Approach 1:
The patent changes the monitoring parameter from long-term memory trends to short-term memory usage spikes. By focusing on temporary increases in memory usage that exceed a threshold (e.g., 80% of total memory) within a specific time window (e.g., 5 minutes), the system can detect memory spikes that traditional monitoring would miss. This parameter change enables reliable detection of unstable memory conditions without requiring historical trend analysis.
Solution Approach 2:
The patent introduces an intermediary component (memory monitoring module) that sits between the memory system and the operating system. This intermediary continuously monitors memory usage and detects spikes before they cause system instability, allowing for early intervention through corrective actions (process termination, memory allocation adjustments) without waiting for system failure.
2Loss of time
If historical data analysis is required for memory monitoring, then long-term trends are captured, but immediate memory spike detection is delayed
Solution Approach 1:
The patent extracts the detection of memory spikes from the analysis of long-term memory trends. By separating short-term spike detection from historical trend analysis, the system can immediately identify and respond to memory spikes without waiting for historical data accumulation. The monitoring system focuses specifically on temporary memory usage increases while ignoring long-term trends, achieving rapid detection response.
Solution Approach 2:
The patent performs preliminary monitoring of memory usage continuously in the background, maintaining a ready state that can immediately detect and respond to memory spikes. This preliminary action ensures that when a memory spike occurs, the system is already positioned to detect it within seconds rather than waiting for historical data collection cycles to complete.
3Measurement precision
If comprehensive memory monitoring is implemented, then all memory usage is tracked, but system complexity increases
Solution Approach 1:
The patent segments memory monitoring into distinct components: total memory monitoring, process-specific memory monitoring, and spike detection logic. By dividing the monitoring system into these segments, each handling a specific aspect of memory usage, the system achieves comprehensive tracking without creating a monolithic complex system. Each segment can be independently configured and managed, reducing overall system complexity.
Solution Approach 2:
The patent implements self-service monitoring where the memory monitoring system automatically detects spikes, determines appropriate corrective actions, and executes them without external intervention. The system serves itself by continuously monitoring its own memory usage and taking autonomous corrective measures (such as terminating problematic processes), eliminating the need for complex external management infrastructure.
Data Source
AI summary
The system determines data which indicate memory usage by a process and information related to a number of instances of the process. The system determines for the process: a first average amount of memory; a first standard deviation; a first value indicating a difference between a current amount of memory used by the process and the first average amount; a second average number of process instances; a second standard deviation; and a second value indicating a difference between a current number of process instances and the second average number. The system performs a corrective action to address a memory spike associated with the process responsive to comparing at least one of: the current amount of memory used by the process with a watermark; the first value with the first standard deviation scaled; the current number of instances with a watermark; and the second value with the second standard deviation scaled.