Device-Specific Chunked Hash Tuning for Sync Throughput
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Solution Overview
Problem
Conventional cloud storage systems use fixed hash chunk sizes, which restrict computational throughput and lead to inefficiencies in synchronization, file backup, and file integrity confirmation due to hardware and runtime environment variations.
Innovation Solution
Implement device-specific chunked hash size tuning, where hashing parameters are dynamically adjusted based on hardware and runtime parameters to optimize chunk sizes for each computing device, allowing for re-tuning in response to environmental changes or user requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed hash chunk size is used, then system simplicity and ease of implementation are maintained, but computational throughput is limited and cannot take advantage of faster processing hardware
Solution Approach 1:
The patent implements dynamic hash chunk size adjustment by detecting hardware capabilities and runtime conditions, then automatically tuning the chunk size to match the processing capacity. The system transitions from a static fixed-size approach to a dynamic adaptive approach where the chunk size changes based on detected performance characteristics and environmental factors, thereby resolving the contradiction between simplicity and throughput.
Solution Approach 2:
The system changes the hashing parameter (chunk size) based on detected hardware capabilities and runtime conditions. By measuring processing speed and adjusting the chunk size parameter accordingly, the system optimizes computational throughput without requiring complex manual configuration, thus resolving the contradiction between productivity and device complexity.
2Adaptability or versatility
If a fixed hash chunk size is used across all devices, then consistency and ease of operation are maintained, but the system cannot adapt to hardware and runtime environment variations
Solution Approach 1:
The hashing system performs self-tuning by automatically detecting its own hardware capabilities and runtime environment, then adjusting its chunk size parameter without external intervention. This self-service approach maintains operational simplicity while achieving adaptability, as the system configures itself based on detected conditions rather than requiring manual setup or complex user input.
Solution Approach 2:
The system implements feedback mechanisms by detecting hardware capabilities and runtime conditions, then using this information to adjust the hash chunk size. This closed-loop approach enables the system to adapt to different device environments while maintaining ease of operation, as the adaptation occurs automatically based on detected feedback from the environment.
3Productivity
If hash chunk size is increased to improve processing efficiency, then computational throughput improves, but network bandwidth consumption and processing resource usage increase
Solution Approach 1:
The system optimizes the chunk size parameter by detecting hardware capabilities and runtime conditions, then adjusting the parameter to achieve the most efficient processing. This prevents both overly small chunks (which waste resources on overhead) and overly large chunks (which consume excessive bandwidth and memory), thereby resolving the contradiction between productivity and resource consumption.
Solution Approach 2:
The system dynamically adjusts chunk size based on detected processing capabilities and runtime conditions, allowing it to optimize throughput while preventing excessive resource consumption. The dynamic adjustment ensures that chunk size matches actual system capacity, resolving the contradiction between improving productivity and minimizing energy loss.
Data Source
AI summary
Device-specific chunked hash size tuning to maximize synchronization throughput is described. A synchronization client application or similar program may employ hashing to detect changes to content of remotely stored files and synchronize those (as opposed to synchronizing all files, for example). Instead of using static hash chunk sizes for all client applications of a cloud storage service, the synchronization client application may determine the size of hash buffer by baselining throughput of hashing on each synchronization device and finding the number of bytes hashed in a given amount of time. Thus, hash chunk size may be optimized on a machine by machine basis.