US 2018/0025289 A1 · Performance Provisioning Using Machine Learning Based Automated Workload Classification

Abstract

Various aspects may include methods, computing devices implementing such methods, and non-transitory processor-readable media storing processor-executable instructions implementing such methods for improving battery life with performance provisioning using machine learning based automated workload classification. Various aspects may include creating a machine learning model based at least in part on computing device metrics, training the machine learning model using performance provisioning rules for work groups; classifying a new work item for a software application into a work group using the trained machine learning model, and applying resource provisioning rules for the work group to the new work item.

Claims30

Dependent claims

A method for resource provisioning using work classification, comprising:

generating, by a processor of a computing device, a work group;
classifying, by the processor, a new work item for a software application into the generated work group using a work classification model;
selecting, by the processor, a set of provisioning rules for the work item based, at least in part, on the work group to which the work item was classified;
and
executing, by the processor, the work item according to the selected set of provisioning rules.

2 more elements

A computing device, comprising:

a processor configured with processor-executable instructions to perform operations comprising:
generating a work group;
classifying a new work item for a software application into the generated work group using a work classification model;
selecting a set of provisioning rules for the work item based, at least in part, on the work group to which the work item was classified;
and
executing the work item according to the selected set of provisioning rules.

3 more elements

A computing device, comprising:

means for generating a work group;
means for classifying a new work item for a software application into the generated work group using a work classification model;
means for selecting a set of provisioning rules for the work item based, at least in part, on the work group to which the work item was classified;
and
means for executing the work item according to the selected set of provisioning rules.

2 more elements

A non-transitory processor-readable medium having stored thereon processor-executable instructions configured to cause a processor of a computing device to perform operations comprising:

generating a work group;
classifying a new work item for a software application into the generated work group using a work classification model;
selecting a set of provisioning rules for the work item based, at least in part, on the work group to which the work item was classified;
and
executing the work item according to the selected set of provisioning rules.

2 more elements

Dependencies are inferred from the claim text.

Facts

Application

15/634,215Filed 27 Jun 2017

Priority

20 Jul 2016

Published

25 Jan 2018

Term ends

Not estimatedThe source records don’t support an estimate.

Classification9 codes

G06F 9/5061
Allocation of resources, e.g. of the central processing unit [CPU]: partitioning or combining of resources
G06F 9/50
Multiprogramming arrangements: allocation of resources, e.g. of the central processing unit [CPU]
G06F 9/5027
To service a request: the resource being a machine, e.g. CPUs, Servers, Terminals
G06N 20/00
Machine learning

Family

Application62/364,451Us provisional application · filed 20 Jul 2016

Application15/257,491Continuation in part · filed 6 Sep 2016

Application15/634,215Continuation in part

US 2018/0024859 A1Published application

US 2018/0025289 A1This patent

From the US filing history. Foreign family members aren’t listed.

Performance Provisioning Using Machine Learning Based Automated Workload Classification

Life of this patent

Performance Provisioning Using Machine Learning Based Automated Workload Classification

Life of this patent

Abstract

Claims30