Inline Data Compression Control for Storage Throughput Bottlenecks
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Solution Overview
Problem
Conventional storage systems lack a framework to dynamically assess and adjust inline data compression performance, leading to inefficient storage utilization and IO operations, as users cannot measure the impact of inline data compression on storage system performance.
Innovation Solution
A method is introduced to manage inline data compression by determining a system throughput rate range, assessing performance based on this range, and adjusting compression settings to optimize storage system efficiency and IO performance, utilizing both software and hardware compression techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If inline data compression is enabled in storage systems, then storage utilization and efficiency are improved, but system IO performance and throughput may deteriorate due to the additional processing overhead
Solution Approach 1:
The patent implements dynamic adjustment of inline data compression by continuously monitoring system throughput rate and compressibility metrics, then adapting compression settings in real-time. When throughput exceeds thresholds or compressibility is low, compression is dynamically disabled or reduced to prevent performance degradation, while enabling it when conditions are favorable
Solution Approach 2:
The system changes operational parameters by adjusting compression settings based on measured system throughput rate ranges and data compressibility assessments. It modifies compression intensity, enables/disables compression selectively, and tunes system behavior based on quantified performance metrics and compressibility thresholds
2Quantity of substance
If inline data compression is performed on all data, then storage efficiency is maximized, but system complexity increases due to the need for performance monitoring and dynamic adjustment mechanisms
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor system throughput rate, measure data compressibility, and assess the impact of inline compression on overall system performance. This feedback loop enables automatic adjustment of compression settings based on real-time system state, eliminating the need for complex manual configuration while maintaining optimal efficiency
Solution Approach 2:
The system performs self-assessment by automatically measuring its own throughput rate and compressibility characteristics, then self-adjusts compression settings without external intervention. The storage system monitors itself and makes autonomous decisions about when to enable, disable, or tune compression based on its own performance metrics
Data Source
AI summary
A method is usd in managing inline data compression in storage systems. Multiple requests are received to write data to a data object stored in a storage system. A range is determined for a system throughput rate for the storage system. Based on the range, performance of performing inline data compression of the data is assessed. Compressed data is produced prior to storing the data in the data object. Based on the assessment, performing inline data compression in the storage system is affected.