Virtual Disk Upload Sparseness Preservation
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Solution Overview
Problem
In cloud computing, uploading virtual hard disks with sparseness optimizations results in unnecessary data blocks with nulls being created and uploaded, wasting bandwidth and computing resources, as existing technologies lack efficient conversion methods to preserve sparseness during file uploads.
Innovation Solution
Integrating on-the-fly conversion and upload tools that leverage sparseness metadata to optimize data transfer by identifying and omitting null data blocks, allowing for parallel uploads and automatic conversion of dynamic and differencing disk formats to native cloud formats, thereby preserving sparseness and reducing unnecessary data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If sparseness optimizations are used in local disk format, then storage space is reduced, but bandwidth and computing resources are wasted when null blocks are uploaded to cloud service
Solution Approach 1:
The patent extracts only the necessary data blocks (those containing actual data) from the local virtual hard disk file and uploads them to the cloud service, while leaving out the null blocks. The block allocation table is used to identify which blocks contain data and which are null, allowing selective upload of only meaningful data blocks.
Solution Approach 2:
The patent performs preliminary analysis of the local file's sparseness characteristics before upload, using the block allocation table to pre-identify data-containing blocks versus null blocks. This preliminary action enables the upload process to skip null blocks entirely, avoiding wasted bandwidth and computing resources.
2Adaptability or versatility
If format conversion is performed before upload, then cloud service compatibility is improved, but upload time increases due to conversion overhead
Solution Approach 1:
The patent performs preliminary analysis of the local file format and sparseness characteristics before upload, allowing the system to prepare the appropriate conversion strategy in advance. This preliminary action includes identifying the cloud service target format and pre-planning the conversion process.
Solution Approach 2:
The patent implements dynamic format conversion that adapts to the specific characteristics of the local file and target cloud service. Rather than a static conversion process, the system dynamically adjusts the conversion approach based on the file's sparseness pattern and the cloud service's requirements, optimizing the conversion efficiency.
3Reliability
If all data blocks are uploaded to cloud service, then data completeness is ensured, but bandwidth and computing resources are wasted on null blocks
Solution Approach 1:
The patent extracts only the essential data blocks from the local virtual hard disk file based on the block allocation table, which identifies which blocks contain actual data. By uploading only these extracted data blocks and not all blocks, the system ensures data completeness while avoiding the waste of bandwidth and computing resources on null blocks.
Solution Approach 2:
The patent uses the block allocation table as feedback information to guide the upload process. The block allocation table provides information about which blocks contain data and which are null, allowing the system to make informed decisions about what to upload, thereby ensuring data completeness while optimizing resource usage.
4Productivity
If block allocation table is referenced for sparseness identification, then upload optimization is achieved, but performance hit occurs due to table referencing overhead
Solution Approach 1:
The patent performs preliminary loading of the block allocation table into memory before the upload process begins. This preliminary action allows the system to have fast access to sparseness information during the upload process, reducing the performance hit that would occur from repeated disk I/O operations to read the block allocation table.
Data Source
AI summary
Uploading storage data. A method includes determining a file protocol for a local file including determining sparseness for the local file. Based on the sparseness for the local file, the local file is uploaded to a remote service, while preserving sparseness by not uploading data that the remote service can imply based on the sparseness.