Content Teleportation Network Using Hash Matches
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Solution Overview
Problem
The challenge is to reduce the network bandwidth consumption when transferring large virtual-machine images and other disk images, as they can be quite large and consume significant network resources during distribution.
Innovation Solution
The solution involves a content teleportation network that uses hash files to identify matching segments between a source and target node, allowing only necessary segments to be transferred, thereby minimizing the data moved across the network. This process includes generating source and target hashes, comparing them to assemble a copy at the target node, and using probabilistic filters and index structures to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual-machine images are transferred over the network, then the content can be distributed to other physical hosts, but the network bandwidth consumption increases significantly
Solution Approach 1:
The virtual-machine image is divided into multiple segments, and hash values are computed for each segment. The target node compares these hash values with its existing segments to identify matches. Only non-matching segments are transferred, significantly reducing network bandwidth consumption while maintaining complete content distribution capability.
Solution Approach 2:
Hash values for all segments are computed in advance before the transfer process begins. The target node pre-computes hash values for its existing segments and creates a hash index structure. This preliminary hashing enables rapid identification of matching segments without requiring actual content comparison during transfer.
2Loss of energy
If hash comparisons are performed to identify matching segments, then network bandwidth is reduced, but processing power and time are consumed
Solution Approach 1:
The content is segmented into fixed-size blocks, and hash values are computed independently for each segment. This segmentation allows parallel processing of hash comparisons across multiple segments, reducing total processing time while maintaining accurate identification of matching segments.
Solution Approach 2:
Hash values for all segments are pre-computed and stored in hash index structures at both source and target nodes before the actual transfer begins. This preliminary hashing eliminates the need for time-consuming hash computations during the transfer process, significantly reducing processing time.
Solution Approach 3:
Instead of comparing actual segment content, the system copies and compares only the hash values (which are much smaller in size). This approach dramatically reduces the amount of data that needs to be processed during comparison while maintaining the accuracy of match identification.
3Loss of energy
If only necessary segments are transferred instead of complete images, then bandwidth usage is minimized, but the complexity of identifying and assembling segments increases
Solution Approach 1:
Hash index structures are pre-built at the target node, organizing hash values by segment number and maintaining sorted order. This preliminary organization enables efficient binary search and direct lookup during the transfer process, significantly reducing the complexity of segment identification compared to unorganized hash storage.
Solution Approach 2:
The system transfers only the necessary information (hash values and segment identifiers) rather than complete segment content during the identification phase. This copying of metadata enables complex identification logic to be performed with minimal data transmission and processing overhead.
Data Source
AI summary
Files, e.g., disk-image files can be teleported from a source node of a network to a target node in that a copy of file can be assembled at least in part using file parts found on the target node. Source hashes can be generated based on segments of the source file. The source hashes can be sent by the source node and received by the target node. The target node compares each source hash with target hashes of segments of files on the target node. When a comparison results in a match, the file copy can include a copy of the matching target segment or include a reference to the matching segment. For higher performance, fingerprints of the source hash and the target hashes can be compared, with hash comparisons being performed in the event of a fingerprint match. The target fingerprints can be arranged in a cuckoo filter or other probabilistic filter.