Dismiss
InnovationQ will be updated on Sunday, Oct. 22, from 10am ET - noon. You may experience brief service interruptions during that time.
Browse Prior Art Database

Inferring patch dependencies based on analytical analysis of historical and future data

IP.com Disclosure Number: IPCOM000238646D
Publication Date: 2014-Sep-09
Document File: 4 page(s) / 41K

Publishing Venue

The IP.com Prior Art Database

Abstract

Method and System for inferring patch dependencies based on analytical analysis of historical and future data, for the purposes of the most efficient patch application and problem identification on one or more computer systems.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 27% of the total text.

Page 01 of 4

Inferring patch dependencies based on analytical analysis of historical and future data

Disclosed is a device (system, etc) for inferring patch dependencies based on the analytical analysis of historical and future data, for the purposes of the most efficient patch application and problem determination on one or more computer systems.

One key use of this analysis is with the IBM* Endpoint Manager (IEM) application. IEM uses relevance language to determine if a software patch (or application) needs to be installed on a target computer. A simple example of relevance language would be something as follows:

version of file "C:\BigFix\BigFixEnterpriseServer\FillDB.exe" <= "2.0.4.24"

A patch is applicableif the relevance evaluates to true. So for example, a patch will only be installed if the existing version of the file is less than the one contained in the patch.

Each patch will typically have it's own self contained relevance that will determine if a patch is to be installed on a target computer. As such each patch is independent of the other.

Relevance statements can be combined to form complicated structures using AND and OR statements. The more complicated the relevance statement, the more time it takes to evaluate on the target computer.

The Problem

Certain dependencies may exist between patches and these dependencies are not apparent when a particular patch is created, and dealing with patches on an individual basis can result in inefficiencies and sometimes problems surfacing.

For example, the following situations may exist:

• If application of one patch makes another patch relevant, then these patches should be applied together and in the correct sequence for more efficient processing.

• If application of one patch makes another patch non-relevant (supersedes) then we can ignore the other patch (it doesn't need to be applied), resulting in more efficient processing.


• Circular dependencies - Installing one patch negates the installation of patch previously applied (back-levels)

This invention proposes : • Using the results of patch relevance evaluation on an endpoint to automatically calculate the dependencies between patches to be applied to identify efficiencies and problems • Aggregate these results in a central system and use this information in future patch application across other computers.

• Extending analytics to patch analysis and application across computers, using dependency information of components across servers to determine how to patch computers in the correct order for a given business service, supporting proper sequential patch application to components of a business service.

• Provide a look-ahead patch analysis, where the system can determine if a particular patch is applied to a target computer that there will be additional patches that will then need to be applied (which weren't as of yet identified to be applied), resulting in more efficient patching by applying more known patches to be...