Shared Image Deployment via Cluster Hashing
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Solution Overview
Problem
Existing anti-malware solutions face challenges in efficiently detecting and repairing malware infections on computers, particularly due to the large size of software images required for installation, which can consume significant bandwidth and time during deployment.
Innovation Solution
A system for efficient shared image deployment is introduced, which determines the differences between components on a client and a master image, and only sends necessary files or clusters, using hash operations to identify and replicate the required content, thereby reducing bandwidth usage and deployment time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full software images are deployed to clients, then malware detection and repair capabilities are ensured, but bandwidth consumption and deployment time increase significantly
Solution Approach 1:
The software image is divided into multiple clusters of files, allowing the system to deploy only the necessary clusters to clients rather than the complete image. This segmentation enables selective deployment based on client needs, reducing bandwidth consumption while maintaining effective malware detection and repair capabilities.
Solution Approach 2:
Instead of deploying the complete software image to all clients, the system deploys only the partial clusters of files that are actually needed for malware detection and repair functions. This partial action approach reduces unnecessary data transfer while ensuring sufficient functionality is provided.
2Reliability
If full software images are deployed to clients, then malware detection and repair capabilities are ensured, but deployment time increases significantly
Solution Approach 1:
By segmenting the software image into clusters, the system can deploy only the necessary portions to clients, significantly reducing deployment time. The clustered structure allows for selective transmission of only those file clusters required for malware detection and repair, rather than waiting for complete image deployment.
Solution Approach 2:
The system performs preliminary identification of which file clusters are needed for malware detection and repair functions, allowing deployment to focus only on those essential clusters from the outset, rather than deploying everything and then utilizing only what is needed.
3Measurement precision
If hash operations are performed on all files to determine differences, then accurate deployment decisions are made, but processing time and computational resources increase
Solution Approach 1:
Files are organized into clusters, and hash operations are performed at the cluster level rather than on individual files. This segmentation approach maintains accurate determination of what needs to be deployed while significantly reducing the total number of hash operations required, thereby decreasing processing time and computational resource consumption.
Data Source
AI summary
Technologies for distribution of a shared image include determining results of a first hash operation applied to a plurality of elements of an image of a software installation, determining results of the first hash operation applied to the plurality of contents of a client, comparing results of the first hash operation applied to the plurality of elements of the image with the results of the first hash operation applied to the plurality of contents of the client, determining that one or more of the plurality of elements of the image are unavailable on the client based on the comparison of the results of the first hash operation applied to the plurality of elements of the image with the results of the first hash operation applied to the plurality of contents of the client, and causing the transmission of the elements to the client.