Heterogeneous File Optimization by Access Pattern for Lower Latency
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Solution Overview
Problem
Existing data optimization techniques result in inefficient resource utilization due to uniform optimization of files, leading to increased latency and resource consumption, especially when frequent access is required, as devices lack the ability to differentiate between regions of a file.
Innovation Solution
Heterogeneous optimization of file regions based on access patterns, where different optimization techniques are applied to varying extents, using optimization designations assigned to regions based on their access patterns, allowing for partial or complete optimization of file regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is fully optimized using de-duplication and compression techniques, then storage efficiency is improved, but access latency increases and resource consumption increases
Solution Approach 1:
The patent applies different optimization strategies to different regions of a file based on their access patterns. Frequently accessed regions are optimized to a lesser extent or not at all, while infrequently accessed regions are heavily optimized. This local differentiation resolves the contradiction by optimizing storage efficiency only where it does not significantly impact access latency.
Solution Approach 2:
The patent divides a file into multiple regions or segments based on access patterns, and applies different optimization techniques to each segment. This segmentation allows the system to balance between storage efficiency and access latency by treating different parts of the file differently according to their specific access characteristics.
2Quantity of substance
If data is fully optimized, then storage efficiency is improved, but device resource utilization deteriorates due to substantial CPU, memory, and disk I/O consumption
Solution Approach 1:
The patent applies optimization resources selectively to specific file regions based on access patterns, rather than uniformly optimizing entire files. This local approach reduces overall resource consumption while maintaining storage efficiency for the most beneficial regions.
Solution Approach 2:
The patent performs partial optimization on only those file regions that benefit most from optimization, rather than applying excessive optimization to entire files. This partial action reduces resource consumption while achieving sufficient storage efficiency improvements.
3Quantity of substance
If uniform optimization is applied to entire files, then storage efficiency is improved, but the system cannot differentiate between optimizable and non-optimizable regions
Solution Approach 1:
The patent enables the system to differentiate between various file regions by analyzing access patterns and applying appropriate optimization strategies to each region. This local quality approach provides adaptability by allowing the system to identify and optimize only those regions that are suitable for optimization.
Solution Approach 2:
The patent uses access pattern monitoring as feedback to determine which regions should be optimized and to what extent. This feedback mechanism enables the system to adaptively differentiate between optimizable and non-optimizable regions, improving both storage efficiency and region differentiation capability.
Data Source
AI summary
Techniques are described herein that are capable of heterogeneously optimizing a file. Heterogeneous optimization involves optimizing regions of a file non-uniformly. For example, the regions of the file may be optimized to different extents. In accordance with this example, a different optimization technique may be used to optimize each region or subset of the regions. In one aspect, optimization designations are assigned to respective regions of a file based on access patterns that are associated with the respective regions. The file may be a database file, a virtualized storage file, or other suitable type of file. Each optimization designation indicates an extent to which the respective region is to be optimized. Each region may be optimized to the extent that is indicated by the respective optimization designation that is assigned to that region.