Threshold-Based Data Compression for Memory and Sync Bandwidth
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Solution Overview
Problem
Organizations face challenges in managing memory storage and communication bandwidth due to increasing computing demands, often constrained by aging hardware and software resources, leading to depleted resources and communication delays, especially in high-traffic scenarios like financial trading systems.
Innovation Solution
Implementing a memory management system that actively monitors and compresses data structures by calculating compression parameters based on the size of data elements, reducing memory usage and communication bandwidth requirements, thereby minimizing the need for additional resources and reducing communication delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in uncompressed format to maintain data integrity and accessibility, then data quality and reliability are preserved, but memory storage capacity is depleted quickly and additional storage resources are required
Solution Approach 1:
The patent applies parameter changes by transitioning data from an uncompressed state to a compressed state, altering the physical form of the data while preserving its informational content. The compression process changes the storage density parameter, allowing the same logical data to occupy less physical memory space. This resolves the contradiction by enabling sufficient storage capacity while maintaining data integrity through reversible compression algorithms.
2Reliability
If data structures are synchronized in real-time between computing systems to ensure data consistency, then data reliability is maintained, but communication bandwidth is consumed and communication delays occur
Solution Approach 1:
The patent extracts only the essential synchronization information by compressing data structures before transmission. Instead of transmitting complete uncompressed data structures, the system transmits compressed representations that contain the critical information needed for synchronization. This extraction principle reduces communication bandwidth consumption while maintaining data consistency across systems.
3Quantity of substance
If data structures are compressed to reduce memory usage and communication requirements, then storage capacity and bandwidth efficiency are improved, but data processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-compressing data structures before they are stored or transmitted. The compression operation is performed in advance, converting raw data into a compressed format that can be efficiently stored and transmitted. This preliminary compression step, while adding initial processing complexity, simplifies subsequent operations by working with smaller, more manageable data structures that require less memory and bandwidth.
Data Source
AI summary
Systems, devices and methods for adaptive compression of stored information includes a memory management computing device programmed to monitor a size of a plurality of data structures stored in a data repository. The computing device compares the size of each of a plurality of data structures to a predetermined threshold. When a size of an uncompressed data structure meets the threshold, the memory management computing device calculates a value of a first compression parameter based on a value of a first parameter and a value of a second parameter of each data element of the uncompressed data structure, calculates a value of a second compression parameter based the value of the first parameter of each data element of the uncompressed data structure, generates a compressed data structure based on the value of the first compression parameter and the second compression parameter; and replaces, in the data repository, the uncompressed data structure with the compressed data structure.