Data Payload Clustering for Storage Systems
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Solution Overview
Problem
Distributed computing and data storage systems face inefficiencies due to high transactional overhead for small files, leading to reduced overall efficiency and scaling issues, particularly in systems where storage of small files is inefficient.
Innovation Solution
Implementing a system where data storage requests are aggregated to reduce incidental requests, with a service interface processing these requests to preempt additional calls by including predetermined responses, and aggregating data payloads into containers based on size thresholds, allowing for direct execution or processing without extraneous interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data storage requests are processed individually, then each request can be handled immediately, but the transactional overhead increases significantly for small files
Solution Approach 1:
The patent combines multiple individual data storage requests into a single batched request. Instead of processing each small file request separately with full transactional overhead, the system aggregates multiple requests and processes them together as one unit, significantly reducing the total overhead while maintaining processing speed.
2Reliability
If the system processes all requests with full transactional overhead, then each request is handled completely, but the overall system efficiency decreases
Solution Approach 1:
Multiple independent requests are merged into a single batched operation that maintains the reliability of individual request completion while improving overall system efficiency by reducing redundant processing overhead.
Solution Approach 2:
The system performs preliminary actions by pre-processing and validating multiple requests before executing them in a batch, ensuring that each request within the batch is ready for reliable execution while maintaining high efficiency.
3Adaptability or versatility
If small files are stored individually, then each file can be accessed independently, but the processing burden per file becomes inefficient
Solution Approach 1:
The system merges multiple small file storage operations into a single batched operation, maintaining the ability to access individual files independently while dramatically improving processing efficiency by eliminating per-file overhead.
Solution Approach 2:
The batched request is segmented into individual file operations that can be executed independently, allowing each file to be accessed separately while the bulk operation provides efficiency benefits.
4Reliability
If the system handles each request with complete transactional processing, then data integrity is maintained, but scaling ability is adversely affected
Solution Approach 1:
The system combines multiple data integrity保障措施 into a single batched transaction, maintaining data integrity for all files in the batch while enabling the system to scale to handle much larger volumes of requests without proportionally increasing overhead.
Data Source
AI summary
A data storage system implements aggregation, bifurcation, and/or reduction techniques to improve the efficiency of processing data storage requests. Data storage requests and/or their associated payloads may be aggregated based on one or more parameters. Data to be the stored and the associated commands may be separated so as to optimize a system's throughput and latency for each. Furthermore, extraneous commands and requests may be reduced or eliminated based on heuristics associated with the requests and the data.