Inline Digest and Entropy Processing for CPU Cache Efficiency
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Solution Overview
Problem
Current data storage systems face inefficiencies in data deduplication and compression processes, particularly in managing workloads and determining entropy thresholds, which can lead to suboptimal performance and resource utilization during data processing operations.
Innovation Solution
A method that computes data deduplication digests and entropy values in parallel, synchronizing these processes to ensure completion before proceeding with the next data chunk, and dynamically adjusts entropy thresholds based on the current workload to optimize deduplication and compression decisions inline with the I/O path processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data deduplication and compression processing are performed sequentially, then processing logic is simple, but processing time increases and throughput decreases
Solution Approach 1:
The patent divides data processing into discrete chunks and processes each chunk through parallel digest computation and entropy calculation operations. By segmenting the processing workflow into independent parallel tasks that can be synchronized, the system achieves higher throughput without excessive complexity increase.
Solution Approach 2:
The patent implements dynamic entropy threshold adjustment based on current workload conditions. The entropy threshold is not fixed but adapts dynamically to system state, allowing the processing system to optimize its behavior under different load conditions and improve overall productivity.
2Stability of the object's composition
If entropy threshold is fixed, then processing decisions are consistent, but performance is suboptimal under varying workload conditions
Solution Approach 1:
The patent makes the entropy threshold dynamic by adjusting it based on current workload measurements. When workload is high, the threshold increases to favor deduplication; when workload is low, the threshold decreases to enable more compression opportunities, thus optimizing performance under varying conditions.
Solution Approach 2:
The system implements feedback mechanisms where processing results and workload metrics are monitored and used to adjust the entropy threshold. This closed-loop control allows the system to adapt to changing conditions while maintaining reasonable consistency in processing decisions.
3Productivity
If parallel processing is implemented without synchronization, then processing speed increases, but data consistency deteriorates
Solution Approach 1:
The patent processes data in discrete chunks with clear boundaries, allowing parallel operations on different chunks without interference. This segmentation enables speed improvement through parallelism while maintaining consistency within each chunk through controlled synchronization points.
Solution Approach 2:
The patent introduces synchronization mechanisms as intermediaries between parallel processing streams. These synchronization points ensure that digest computation and entropy calculation results are properly coordinated before being used for deduplication and compression decisions, maintaining data consistency.
Data Source
AI summary
Techniques for CPU cache efficiency may include performing concurrent processing, such as for first and second data operations, in a synchronized manner that prevents loading the same data chunk into the CPU cache more than once. Processing may include synchronizing the first and second data operations with respect to a first data chunk to ensure that both complete prior to proceeding with performing such processing on a second data chunk. The first and second data operations for a chunk may be deduplication digest computation and entropy computation performed inline as part of the data path. If the chunk cannot be deduplicated, the entropy value may be used with an adaptive entropy threshold to determine whether to compress the chunk inline. The entropy value may be determined using less than all bytes in the chunk. The chunk's entropy value may be determined based on a data set entropy value.