Adaptive Virtual Memory Compression by Object Type
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Solution Overview
Problem
General-purpose compression algorithms used in virtual memory management are not optimized for different types of memory objects, leading to suboptimal compression performance for various data types such as images, text, audio, and video.
Innovation Solution
A method that selects a compression algorithm based on object characteristics, including operating system object tags, sampled content, and history of compression characteristics, to tailor compression to specific object types, thereby improving compression efficiency and memory utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general-purpose compression algorithm is used for all memory objects, then the system maintains simplicity and universality, but compression performance is suboptimal for different data types
Solution Approach 1:
The patent segments the compression system by dividing memory objects into different categories (e.g., text, image, audio, video, database) and applying specific compression algorithms to each category. This segmentation allows the system to achieve optimal compression performance for each data type while maintaining manageable complexity through structured classification.
Solution Approach 2:
The patent implements dynamic algorithm selection where the compression algorithm is chosen based on the characteristics of each memory object. The system dynamically determines the appropriate algorithm by examining object metadata, sampling content, or using history-based predictors, allowing adaptability without requiring a static complex architecture for every possible data type.
2Productivity
If multiple specialized compression algorithms are maintained for different object types, then compression efficiency improves, but system complexity and overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-classifying memory objects into categories using object metadata, OS tags, or content sampling before compression. This preliminary classification determines the appropriate compression algorithm in advance, allowing the system to achieve high compression ratios without complex real-time decision-making during the actual compression process.
Solution Approach 2:
The system employs self-service mechanisms where the compression algorithm selection is automatically determined based on object characteristics without requiring external intervention. History-based predictors and automated content analysis enable the system to self-optimize algorithm selection, reducing the perceived complexity for users while maintaining high compression efficiency.
3Measurement precision
If content sampling is performed to determine object characteristics, then accurate algorithm selection is achieved, but processing time increases
Solution Approach 1:
The patent applies partial action by sampling only a portion of the object content rather than analyzing the entire object. This selective sampling provides sufficient information for accurate algorithm selection while significantly reducing the time overhead compared to complete content analysis, achieving a practical balance between precision and performance.
Data Source
AI summary
A method and apparatus of a device that compresses an object stored in memory is described. In an exemplary embodiment, the device receives an indication that the object is to be compressed. The device further selects one of a plurality of compression algorithms based on at least a characteristic of the object. In addition, the device compresses the object in-memory using the selected compression algorithm.