Automated Data Compression Using Idle Counters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data storage systems lack an efficient method for automating the compression and decompression of data based on activity levels, leading to suboptimal storage utilization and performance.
Innovation Solution
A method for automated data compression that uses idle counters and compression criteria to identify inactive data portions, compressing them and storing them on physical storage devices, and decompressing them when activity levels meet certain thresholds, allowing for dynamic adjustment based on access patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is compressed to reduce storage consumption, then storage efficiency is improved, but data access performance deteriorates due to decompression requirements
Solution Approach 1:
The patent implements dynamic compression by monitoring data access patterns through idle counters and activity thresholds. Data portions are automatically compressed when idle and decompressed when access activity increases, creating a dynamic system that adapts to changing data usage patterns rather than using static compression policies
Solution Approach 2:
The system employs automated decision-making through idle counters and compression criteria that self-determine which data portions to compress or decompress without manual intervention. The idle counters track access patterns and automatically trigger compression or decompression actions based on predefined thresholds, enabling the system to self-optimize storage and performance
2Quantity of substance
If automated compression is implemented to optimize storage, then storage utilization is improved, but system complexity increases due to monitoring and management overhead
Solution Approach 1:
The automated compression system uses self-monitoring idle counters and predefined compression criteria to automatically determine which data portions to compress. This self-service mechanism eliminates the need for complex manual monitoring and management, reducing operational complexity while improving storage utilization
Solution Approach 2:
The system manages complexity by changing parameters (idle counter values, activity thresholds, time compression thresholds) rather than restructuring the entire system. These parameter adjustments allow flexible optimization of storage while maintaining manageable system complexity through configurable thresholds
3Measurement precision
If idle counters are used to track data inactivity, then compression decision accuracy is improved, but processing overhead increases due to continuous monitoring
Solution Approach 1:
The system applies partial monitoring by focusing idle counter tracking only on data portions that are candidates for compression, rather than continuously monitoring all data. This selective approach improves compression decision accuracy for relevant data while reducing unnecessary processing overhead on already-compressed or actively-used data
Data Source
AI summary
Described are techniques for automated compression and/or decompression of data. Idle counters are received for data portions which indicate a level of inactivity with respect to idle criteria for the data portions. Compression criteria is received specifying conditions for determining whether a data portion is a candidate for data compression. The compression criteria includes a time compression threshold indicating a minimum amount of time that a data portion has been deemed idle with respect to said idle criteria. A data portion that qualifies as a candidate for data compression has an associated idle counter indicating an inactivity level that is greater than the time compression threshold. Processing determines whether a first data portion is a candidate for data compression in accordance with the compression criteria. If the first data portion is determined as a candidate for data compression, compression processing is performed for the first data portion.