Data Payload Aggregation 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, especially in systems where storage of small files is inefficient.
Innovation Solution
Implementing a service interface that aggregates data storage requests, reducing incidental requests by including predetermined responses and component requests within the aggregated request, and processing these requests directly within the data storage entity, while using a client interface to determine whether to aggregate or pass requests as individual files or in data containers based on size thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data storage requests for small files are processed individually, then each file can be stored with simple processing, but the transactional overhead per file becomes a greater percentage of the overall processing burden, reducing system efficiency
Solution Approach 1:
The patent combines multiple small file storage requests into a single batch operation. The system aggregates individual file storage requests into a batch job, processes them collectively rather than individually, and applies transactional overhead once for the entire batch. This merging approach directly reduces the proportional transactional overhead and improves storage processing efficiency for small files.
2Productivity
If the system processes each data storage request separately, then request handling is straightforward, but the number of incidental requests and interactions with clients increases, reducing overall system performance
Solution Approach 1:
The patent merges multiple individual data storage requests into a single batch operation. Instead of processing each request separately and generating multiple incidental component requests, the system consolidates them into one batch job that is processed as a unified operation, thereby reducing the total number of component requests and simplifying system complexity.
Solution Approach 2:
The system performs preliminary aggregation of data storage requests before processing. By collecting and batching multiple requests in advance rather than processing them immediately as they arrive, the system prepares the workload in a consolidated form that reduces subsequent processing complexity and minimizes the number of incidental requests generated during execution.
3Ease of operation
If small files are stored as individual files, then file management is simple, but the transactional overhead makes up a greater percentage of the overall processing burden
Solution Approach 1:
The patent merges multiple small file storage operations into a single batch processing operation. While individual file storage remains conceptually simple, the system aggregates these operations so that the transactional overhead is amortized across multiple files, thereby maintaining ease of operation while significantly improving overall processing efficiency.
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.