Content-Defined Chunking for Autonomous Vehicle Data Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The frequent downloading of large binary data files in autonomous vehicle ecosystems puts a significant strain on network infrastructure, often exceeding contractual bandwidth limits and requiring full file downloads even when only minor updates are needed, which is inefficient and costly.
Innovation Solution
Implementing a content-defined chunking (CDC) algorithm to divide large binary files into variable-sized chunks, storing metadata for each chunk, and using a content-addressable storage (CAS) to identify and download only the necessary chunks for updates, allowing for efficient reconstruction of the file without requiring a full download.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full binary data files are downloaded frequently, then data updates are ensured, but network bandwidth is excessively consumed and infrastructure strain increases
Solution Approach 1:
The patent segments binary data files into fixed-size chunks and creates a manifest file containing chunk hashes. This allows the system to download only the necessary chunks rather than complete files, significantly reducing network bandwidth consumption while ensuring data completeness through hash verification.
Solution Approach 2:
The patent creates hash copies of data chunks and stores them in a content-addressable storage system. When updates are needed, the system queries these hash copies to identify which chunks already exist locally, downloading only the missing or updated chunks rather than redundant full file copies.
2Reliability
If full binary data files are downloaded, then data integrity is ensured, but download time increases
Solution Approach 1:
By segmenting files into chunks with individual hashes, the system can verify integrity of each chunk independently and download only the necessary portions, reducing overall download time while maintaining complete data integrity through hash-based verification.
Solution Approach 2:
The system performs preliminary hashing of local chunks and queries the manifest file before initiating downloads. This preliminary action identifies which chunks are already present and which need to be downloaded, eliminating unnecessary download time while ensuring integrity of the complete dataset.
3Ease of manufacture
If fixed-size chunking is used, then implementation is simple, but download efficiency is reduced when only minor updates are needed
Solution Approach 1:
The patent uses fixed-size chunking for simple implementation while complementing it with a manifest file containing hashes. This combination maintains implementation simplicity but enables efficient identification and download of only the specific chunks that have changed, significantly improving download efficiency for minor updates.
Data Source
AI summary
A computer-implemented method, comprising: dividing a first binary image into a plurality of variable-sized chunks, wherein the first binary image is an aggregate of a plurality of files, and wherein the dividing does not depend on file boundaries; and computing hashes of the variable-sized chunks, and storing the hashes in a content addressable storage (CAS) with the hashes as keys.