Adaptive Compression of Systems Management Data With Minimal Error
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data compression methods for systems management data in enterprise computing environments are inefficient due to the dynamic nature of the data, leading to excessive loss of detail and impractical manual configuration of compression settings, especially when dealing with diverse and complex systems.
Innovation Solution
A dynamic compression system that adaptively selects and adjusts compression algorithms and settings based on the characteristics of incoming metric data, using a pre-compressor to determine appropriate compression constraints and pass them to a compressor, allowing for real-time changes in compression algorithms and settings to optimize storage efficiency while minimizing error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless compression is used to preserve data detail, then data accuracy is improved, but storage efficiency deteriorates due to high overhead
Solution Approach 1:
The patent dynamically changes compression parameters including the selection of different compression algorithms and adjustment of deadband values based on the characteristics of the data being compressed. This allows the system to adapt to varying data patterns and achieve optimal balance between compression ratio and data accuracy for different system management metrics.
2Device complexity
If a single compression algorithm is applied to all data, then device complexity is reduced, but adaptability to diverse data characteristics deteriorates
Solution Approach 1:
The patent implements a universal compression framework that can handle multiple types of system management data with different characteristics using a single system. The pre-compressor evaluates data characteristics and selects appropriate compression algorithms from a set of available options, making the system versatile enough to handle diverse metrics while maintaining a unified architecture.
Solution Approach 2:
The patent introduces dynamic selection of compression algorithms and parameters based on the characteristics of the incoming data. The system continuously adapts its compression strategy by evaluating data patterns, variability, and importance, switching between different compression techniques as needed to optimize performance for each specific data stream.
3Manufacturing precision
If manual configuration of compression settings is implemented, then compression precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements a self-configuring compression system where the pre-compressor automatically evaluates incoming data characteristics and selects appropriate compression parameters without requiring manual intervention. The system monitors data patterns, variability, and importance metrics to autonomously adjust compression settings, eliminating the need for manual configuration while maintaining optimal compression precision.
Solution Approach 2:
The patent incorporates feedback mechanisms where the compression system continuously monitors the characteristics of compressed data and adjusts its parameters accordingly. The pre-compressor uses feedback from data analysis to refine compression settings in real-time, ensuring optimal performance adapts to changing data patterns without manual reconfiguration.
4Quantity of substance
If geometric averaging compression is applied to reduce data volume, then storage efficiency is improved, but loss of information increases
Solution Approach 1:
The patent applies different compression strategies to different portions of the data based on their local characteristics. The pre-compressor identifies regions of high variability or importance in the data stream and applies less aggressive compression to those regions, while using more aggressive compression on stable or less critical data portions. This localized approach preserves important data details while achieving overall volume reduction.
Data Source
AI summary
A method, system, and medium for compressing systems management information in a historical data store. Dynamically determining the appropriate compression algorithm to apply based on the type of data being compressed and stored. As further input is received for any particular measurement, the appropriate compression algorithm will be automatically selected from the set of available compression algorithms or be defined by a user configuration parameter. The amount of historical data stored with the minimal amount of data loss is optimized by the system dynamically changing the compression algorithm used for the given input data over a particular time span. The system engineer is therefore presented with the pertinent information for monitoring, administrating and diagnosing system activities.