Mixed Data Stream Generation With Configurable Compression
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Solution Overview
Problem
Existing data stream generation algorithms are limited in their applicability and effectiveness due to issues such as inadequate speed, incompressibility, and lack of deduplication capabilities, which restrict their usefulness in various applications.
Innovation Solution
The method involves mixing incompressible and compressible data streams to generate a data stream with configurable compression properties, allowing for the creation of data streams with specific compressibility and commonality, suitable for testing, analysis, and diagnostic purposes, using a mixer that combines data streams based on user-specified parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If existing data stream generation algorithms are used, then data streams can be generated, but they are incompressible and lack deduplication capabilities
Solution Approach 1:
The patent combines multiple data stream generation algorithms (e.g., LFSR-based generators, counter-based generators, hash-based generators) into a hybrid system that can produce data streams with configurable compressibility and deduplication properties, resolving the contradiction between achieving compressibility and maintaining versatility
2Loss of substance
If data streams are generated with high compressibility, then storage efficiency improves, but generation speed may be reduced
Solution Approach 1:
The system dynamically selects and configures generation algorithms based on desired compressibility targets, allowing the data generation process to adapt its complexity and speed characteristics in real-time, thus resolving the trade-off between compressibility and generation speed
3Reliability
If data streams are generated with configurable compression properties, then testing and diagnostic effectiveness improves, but system complexity increases
Solution Approach 1:
The patent creates a universal data stream generation system that can produce multiple types of data streams (compressible, incompressible, deduplicatable) using a single configurable framework, reducing the need for multiple separate systems while maintaining reliability for various testing scenarios
Data Source
AI summary
One example method includes receiving a mixed data stream that was created using a first data stream and a second data stream, the mixed data stream having a compressibility of N, where N is a compressibility merging parameter, and the mixed data stream has a compressibility that is between a compressibility of the first data stream and a compressibility of the second data stream, providing the mixed data stream to an application and/or hardware, observing and recording a response of the application and/or hardware to the mixed data stream, and analyzing the response of the response of the application and/or hardware to the mixed data stream.