AUTOMATIC COMPUTE ENVIRONMENT SCHEDULING USING MACHINE LEARNING

Number: 20230305901
Published: 2023-09-28
Filed: 2022-03-28
Assignee: Disney Enterprises, Inc.
Inventors: Vinson; Katherine S. et al.
CPC: G06F9/5094; G06F9/4893; G06N20/00; G06F9/5055; G06F9/4881
Verdict: Set aside IT compute scheduling, generic
Source: Google Patents · FreePatentsOnline

Abstract

Certain aspects of the present disclosure provide techniques for automatic compute environment scheduling using machine learning (ML). This includes identifying a first compute resource among a plurality of compute resources operating in a compute infrastructure, where the first compute resource is in a first operational state. It further includes determining, based on comparing a first time with a compute resources schedule generated using an ML model, that the first compute resource should be placed in a second operational state different from the first operational state. It further includes determining whether the compute resources schedule should be disregarded, and either (1) in response to determining that the compute resources schedule should not be disregarded, placing the first compute resource in the second operational state, or (2) in response to determining that the compute resources schedule should be disregarded, allowing the first compute resource to remain in the first operational state.

Background

BACKGROUND

Many businesses maintain multiple compute environments for their software applications. For example, a business may maintain a testing environment and a production environment. The testing environment is commonly used to validate and quality check changes to software applications before the software is deployed to production, where real users (e.g., internal or external users) can interact with the application.

These environments, however, can be cost and resource inefficient to maintain. For example, a business may maintain a testing environment 24 hours a day under the assumption that development and testing teams will need to use the environment at all hours. As another example, a business may maintain a production environment 24 hours a day under the assumption that users will access the production environment at all hours. In reality, these environments are often only needed during certain times of the day (e.g., when users are awake to access the environment). Running the environments outside of the times when the environments are actually necessary wastes compute resources and power, and can be unnecessarily expensive both computationally and monetarily (e.g., when compute resources are paid for based on operational time). SUMMARY

Embodiments include a method. The method includes identifying a first compute resource among a plurality of compute resources operating in a compute infrastructure, where the first compute resource is in a firs

Claims

1. A method, comprising: identifying a first compute resource among a plurality of compute resources operating in a compute infrastructure, wherein the first compute resource is in a first operational state; determining, based on comparing a first time with a compute resources schedule generated using a machine learning (ML) model, that the first compute resource should be placed in a second operational state different from the first operational state; determining whether the compute resources schedule should be disregarded; and (1) in response to determining that the compute resources schedule should not be disregarded, placing the first compute resource in the second operational state, or (2) in response to determining that the compute resources schedule should be disregarded, allowing the first compute resource to remain in the first operational state. || 11. A non-transitory computer-readable medium containing computer program code that, when executed by operation of one or more computer processors, performs operations comprising: identifying a first compute resource among a plurality of compute resources operating in a compute infrastructure, wherein the first compute resource is in a first operational state; determining, based on comparing a first time with a compute resources schedule generated using a machine learning (ML) model, that the first compute resource should be placed in a second operational state different from the first operational state; determining whether the compute resources schedule should be disregarded; and (1) in response to determining that the compute resources schedule should not be disregarded, placing the first compute resource in the second operational state, or (2) in response to determining that the compute resources schedule should be disregarded, allowing the first compute resource to remain in the first operational state. || 17. A system, comprising: a computer processor; and a memory having instructions stored thereon which, when executed on the computer processor, performs operations comprising: identifying a first compute resource among a plurality of compute resources operating in a compute infrastructure, wherein the first compute resource is in a first operational state; determining, based on comparing a first time with a compute resources schedule generated using a machine learning (ML) model, that the first compute resource should be placed in a second operational state different from the first operational state; determining whether the compute resources schedule should be disregarded; and (1) in response to determining that the compute resources schedule should not be disregarded, placing the first compute resource in the second operational state, or (2) in response to determining that the compute resources schedule should be disregarded, allowing the first compute resource to remain in the first operational state.