Selecting resources for automatic modeling using forecast thresholds

US2016269239A1 · US · A1

Patent metadata
FieldValue
Publication numberUS-2016269239-A1
Application numberUS-201514645647-A
CountryUS
Kind codeA1
Filing dateMar 12, 2015
Priority dateMar 12, 2015
Publication dateSep 15, 2016
Grant date

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  1. Title

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  2. Abstract

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  5. First independent claim

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Abstract

Official abstract text for this publication.

A method includes retrieving capacity utilization data for a plurality of resources and applying a linear regression analysis on the capacity utilization data. The method further includes projecting, using a processor, the capacity utilization data through a future time based on results of the linear regression analysis. The method additionally includes determining a deviation from a predetermined threshold range in the projected capacity utilization data for a first resource, and, in response to determining the deviation, determining, for each of a plurality of resource configurations, future capacity utilization of the first resource based on a non-linear capacity consumption model corresponding to the resource configuration. The method also includes applying a selected resource configuration from the plurality of resource configurations to the first resource to prevent the first resource from deviating from the predetermined threshold range.

First claim

Opening claim text (preview).

What is claimed is: 1 . A method, comprising: retrieving capacity utilization data for a plurality of resources; applying a linear regression analysis on the capacity utilization data; projecting, using a processor, the capacity utilization data through a future time based on results of the linear regression analysis; determining a deviation from a predetermined threshold range in the projected capacity utilization data for a first resource; in response to determining the deviation, determining, for each of a plurality of resource configurations, future capacity utilization of the first resource based on a non-linear capacity consumption model corresponding to the resource configuration; and applying a selected resource configuration from the plurality of resource configurations to the first resource to prevent the first resource from deviating from the predetermined threshold range. 2 . The method of claim 1 , wherein the first resource is a virtual resource associated with a particular service, and wherein at least one of the plurality of resource configurations comprises configuration information regarding other virtual resources associated with the particular service. 3 . The method of claim 1 , further comprising: receiving a growth rate associated with a workload of the first resource, and wherein determining the future capacity utilization of the first resource further comprises modifying the workload of the first resource by the growth rate. 4 . The method of claim 1 , wherein the first resource is a server, and wherein the selected resource configuration comprises a virtual resource placement map for the server. 5 . The method of claim 1 , wherein the selected resource configuration comprises a consolidation of physical servers associated with the plurality of resources. 6 . The method of claim 1 , further comprising: applying a set of related configuration changes to the plurality of resources based on the selected resource configuration. 7 . The method of claim 1 , wherein projecting the capacity utilization data comprises projecting CPU capacity utilization and memory capacity utilization for each of the plurality of resources. 8 . A computer configured to access a storage device, the computer comprising: a processor; and a non-transitory, computer-readable storage medium storing computer-readable instructions that when executed by the processor cause the computer to perform: retrieving capacity utilization data for a plurality of resources; applying a linear regression analysis on the capacity utilization data; projecting, using a processor, the capacity utilization data through a future time based on results of the linear regression analysis; determining a deviation from a predetermined threshold range in the projected capacity utilization data for a first resource; in response to determining the deviation, determining, for each of a plurality of resource configurations, future capacity utilization of the first resource based on a non-linear capacity consumption model corresponding to the resource configuration; and applying a selected resource configuration from the plurality of resource configurations to the first resource to prevent the first resource from deviating from the predetermined threshold range. 9 . The computer of claim 8 , wherein the first resource is a virtual resource associated with a particular service, and wherein at least one of the plurality of resource configurations comprises configuration information regarding other virtual resources associated with the particular service. 10 . The computer of claim 8 , wherein the computer-readable instructions further cause the computer to perform: receiving a growth rate associated with a workload of the first resource, and wherein determining the future capacity utilization of the first resource further comprises modifying the workload of the first resource by the growth rate. 11 . The computer of claim 8 , wherein the first resource is a server, and wherein the selected resource configuration comprises a virtual resource placement map for the server. 12 . The computer of claim 8 , wherein the selected resource configuration comprises a consolidation of physical servers associated with the plurality of resources. 13 . The computer of claim 8 , wherein the computer-readable instructions further cause the computer to perform: applying a set of related configuration changes to the plurality of resources based on the selected resource configuration. 14 . The computer of claim 8 , wherein projecting the capacity utilization data comprises projecting CPU capacity utilization and memory capacity utilization for each of the plurality of resources. 15 . A computer program product comprising: a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code comprising: computer-readable program code configured to retrieve capacity utilization data for a plurality of resources; computer-readable program code configured to apply a linear regression analysis on the capacity utilization data; computer-readable program code configured to project, using a processor, the capacity utilization data through a future time based on results of the linear regression analysis; computer-readable program code configured to determine a deviation from a predetermined threshold range in the projected capacity utilization data for a first resource; computer-readable program code configured to, in response to determining the deviation, determine, for each of a plurality of resource configurations, future capacity utilization of the first resource based on a non-linear capacity consumption model corresponding to the resource configuration; and computer-readable program code configured to apply a selected resource configuration from the plurality of resource configurations to the first resource to prevent the first resource from deviating from the predetermined threshold range. 16 . The computer program product of claim 15 , wherein the first resource is a virtual resource associated with a particular service, and wherein at least one of the plurality of resource configurations comprises configuration information regarding other virtual resources associated with the particular service. 17 . The computer program product of claim 15 , wherein the computer-readable program code further comprises: computer-readable program code configured to receive a growth rate associated with a workload of the first resource, and wherein determining the future capacity utilization of the first resource further comprises modifying the workload of the first resource by the growth rate. 18 . The computer program product of claim 15 , wherein the first resource is a server, and wherein the selected resource configuration comprises a virtual resource placement map for the server. 19 . The computer program product of claim 15 , wherein the selected resource configuration comprises a consolidation of physical servers associated with the plurality of resources. 20 . The computer program product of claim 15 , wherein the computer-readable program code further comprises: computer-readable program code configured to apply a set of related configuration changes to the plurality of resources based on the selected resource configuration.

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Classifications

  • involving simulating, designing, planning or modelling of a network · CPC title

  • by checking functioning · CPC title

  • comprising specially adapted graphical user interfaces [GUI] · CPC title

  • Network utilisation, e.g. volume of load or congestion level · CPC title

  • characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability (for optimising operational conditions of wireless networks H04W24/02) · CPC title

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What does patent US2016269239A1 cover?
A method includes retrieving capacity utilization data for a plurality of resources and applying a linear regression analysis on the capacity utilization data. The method further includes projecting, using a processor, the capacity utilization data through a future time based on results of the linear regression analysis. The method additionally includes determining a deviation from a predetermi…
Who is the assignee on this patent?
Ashby Jr John Wiley, Varadaraju Balaji, Ca Inc
What technology area does this patent fall under?
Primary CPC classification H04L41/0823. Mapped technology areas include Electricity.
When was this patent published?
Publication date Thu Sep 15 2016 00:00:00 GMT+0000 (Coordinated Universal Time) (A1). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 2 related publications on this page (citations in our corpus or others sharing the same primary CPC).