Network Discovery Data Transfer via Attribute Digest Comparison
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Solution Overview
Problem
Current network discovery scans often transfer large amounts of data, as they collect and update attribute values for all devices and entities within a network environment, even if only a few values have changed, leading to inefficiencies due to the lack of differentiation between changed and unchanged data.
Innovation Solution
Implementing a discovery scan process that groups attributes into collection groups based on change frequency, using digest values to identify unchanged attributes and exclude them from data transfer, with internal servers within the network environment collecting and updating only changed attribute values, thereby reducing data transfer and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all attribute values are collected and transferred during network discovery scans, then complete network inventory is maintained, but data transfer volume increases significantly
Solution Approach 1:
The patent extracts only the changed attribute values from the complete set of network device attributes. By identifying and transferring only the subset of attributes that have changed since the last scan, the system maintains complete network inventory information while significantly reducing the volume of data that needs to be transferred across the network.
Solution Approach 2:
The patent segments the network discovery data into individual attribute values that can be independently tracked and transferred. By dividing the complete network inventory into discrete attribute components, the system can selectively identify and transfer only those segments (attributes) that have changed, rather than transferring the entire dataset.
2Reliability
If frequent discovery scans are performed to maintain up-to-date network inventory, then data freshness is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts only the changed attribute values from each discovery scan and transfers only these differences to the CMDB. This allows frequent scans to be performed maintaining data freshness, while the actual bandwidth consumption is reduced because only the subset of changed data is transmitted rather than the complete dataset on each scan.
Solution Approach 2:
The patent implements periodic discovery scans at scheduled intervals to maintain up-to-date network inventory. By combining periodic scanning with differential data transfer, the system achieves reliable data freshness while minimizing bandwidth consumption during each periodic cycle.
3Loss of information
If comprehensive attribute collection is performed across all network devices, then network visibility is enhanced, but processing time increases
Solution Approach 1:
The patent extracts only the changed attribute values from the comprehensive set of collected data. By identifying and processing only the subset of attributes that have changed since the last scan, the system maintains enhanced network visibility while significantly reducing the time required to process and transfer data.
Solution Approach 2:
The patent performs partial processing by collecting all attribute values for complete network visibility but then selectively processing only the changed portions for transfer and updating. This partial action approach maintains comprehensive visibility while reducing the actual processing time required for data transfer and CMDB updates.
Data Source
AI summary
A collection group of computer system attributes is identified, and a first digest value associated with the collection group is obtained. A second digest value associated with the collection group is determined by performing a discovery scan of the collection group, wherein the second digest value is based on discovered values of the computer system attributes. A determination is made that the first digest value is different from the second digest value and an indication is prepared that one or more attribute values of the collection group have changed from a previously collected data of the collection group. The indication and at least a subset of the discovered values of the computer system attributes are provided.