Histogram-Based Chatter Suppression in Storage Event Logs
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Solution Overview
Problem
Conventional event management systems in storage systems face challenges with excessive logging of unimportant messages, leading to large log files that consume storage space and increase transmission times, due to the limited suppression of chatter messages, which complicates administrators' ability to find relevant information.
Innovation Solution
An event management system uses histogram analysis to calculate a suppression level based on log file growth rate, determines a cutoff point for non-chattering events, and suppresses frequent events deemed as chatter, limiting logging to one entry per N minutes, where N is determined by the log file growth rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional event management systems log all event messages, then complete system information is recorded, but log files become excessively large and consume storage space
Solution Approach 1:
The patent extracts and removes chatter messages (repeated, unimportant event notifications) from the log files while retaining important event information. This is achieved through pattern recognition that identifies repetitive message sequences and selectively filters them out, thus reducing log file size while preserving essential system information for troubleshooting and analysis.
Solution Approach 2:
The patent segments event messages into different categories based on their importance and repetition patterns. By dividing the log content into significant events and chatter messages, the system can apply different handling strategies to each segment, allowing complete logging of important events while suppressing repetitive unimportant messages.
2Loss of information
If all event messages are logged, then no information is lost, but administrators must sort through thousands of superfluous messages to find relevant entries
Solution Approach 1:
The patent extracts chatter messages from the event log and removes them, leaving only significant events. This extraction process automatically filters out repetitive, unimportant notifications that clutter the logs, making it much easier for administrators to quickly identify and respond to actual system issues without wading through thousands of irrelevant messages.
Solution Approach 2:
The patent introduces an intermediary filtering mechanism that processes event messages before they are logged. This intermediary layer analyzes incoming events, determines their significance, and selectively logs only important events while suppressing chatter, thereby improving administrator efficiency without losing critical information.
3Reliability
If the number of logged messages increases, then more system events are captured, but transmission times to vendor increase
Solution Approach 1:
The patent extracts and removes chatter messages from the event log before transmission to the vendor. By eliminating repetitive, unimportant messages through pattern recognition and filtering, the system reduces the total volume of data to be transmitted, thereby decreasing transmission time while still capturing all essential event information needed for troubleshooting.
4Device complexity
If conventional suppression logs only duplicate messages twice in a row, then some chatter is reduced, but suppression fails when messages are intermingled with other events
Solution Approach 1:
The patent introduces an intermediary intelligent filtering mechanism that goes beyond simple consecutive duplicate detection. This mediator analyzes the content and patterns of event messages, identifies chatter even when intermingled with other events, and applies sophisticated suppression rules that maintain reliability while handling complex logging scenarios.
Solution Approach 2:
The patent changes the parameters of message suppression from simple consecutive counting to pattern-based identification. By analyzing message content, frequency, and temporal patterns rather than just counting consecutive duplicates, the system achieves more effective chatter suppression that works even when messages are interspersed with other events.
Data Source
AI summary
A system and method suppresses chattering events in a storage system by determining a suppression level based upon a rate of growth of log files. An event management system of the storage system calculates a histogram and a set of statistics including a mean and standard deviation associated with non-chattering events. A cutoff point is determined for the non-chattering events and is utilized for each received event to determine if the received event is a chattering event. Events determined to be a chattering event are suppressed according to a predefined suppression rate.