Client-Based Data Payload Batching for Storage Efficiency
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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, allowing direct processing by the data storage entity and minimizing interactions with the client device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data storage requests are processed individually for each small file, then the system maintains simplicity in request handling, but the transactional overhead increases significantly reducing overall efficiency
Solution Approach 1:
The patent combines multiple individual data storage requests into a single aggregated request that includes multiple data payloads. This merging approach reduces the number of separate transactions and their associated overhead, thereby improving data storage efficiency while reducing time loss.
Solution Approach 2:
The system performs preliminary aggregation of multiple data storage requests before processing them. By pre-grouping requests that will be handled together, the system minimizes the repeated overhead of individual request processing and improves overall productivity.
2Adaptability or versatility
If the system processes each data storage request separately, then the request handling remains simple, but the scaling ability of the system deteriorates
Solution Approach 1:
By merging multiple requests into aggregated requests, the system reduces the total number of processing operations required, which improves scaling ability. The added complexity of aggregation is offset by the reduction in overall transaction volume.
Solution Approach 2:
The aggregated request structure serves multiple functions: it consolidates multiple data payloads, reduces transactional overhead, and maintains a standardized processing interface. This multi-functionality improves system adaptability while managing complexity.
3Reliability
If multiple component requests are transacted with the data storage entity, then complete data storage operations can be performed, but the number of interactions increases reducing efficiency
Solution Approach 1:
The patent merges multiple component requests into a single aggregated request that maintains the completeness of data storage operations. This approach preserves reliability by ensuring all necessary operations are performed while improving productivity through reduced interaction count.
Solution Approach 2:
The system performs preliminary aggregation of component requests before submitting them to the data storage entity. This pre-processing ensures that all necessary operations are included in the aggregated request, maintaining operational completeness while reducing the number of interactions required.
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.