Automated idle environment shutdown

Patent metadata

Field	Value
Publication number	US-11714658-B2
Application number	US-201916556596-A
Country	US
Kind code	B2
Filing date	Aug 30, 2019
Priority date	Aug 30, 2019
Publication date	Aug 1, 2023
Grant date	Aug 1, 2023

Abstract

Methods, systems, and apparatus, including computer-readable media, for automated idle environment shutdown. In some implementations, activity of a server environment is monitored over a period of time. A measure of user-initiated activity of the server environment is determined based on the monitored activity of the server environment over the period of time. The level of user-initiated activity over the period of time is determined to be less than a threshold level. In response to determining that the level of user-initiated activity over the period of time is less than the threshold level, shut down of the server environment is initiated.

First claim

What is claimed is:

1. A method performed by one or more computers, the method comprising:
monitoring, by the one or more computers, a server environment to detect activity of the server environment over a period of time;
determining, by the one or more computers, an amount of the detected activity that corresponds to user requests or active user sessions over the period of time;
determining, by the one or more computers, that the amount of the detected activity corresponding to user requests or active user sessions is below a first threshold level;
generating, by the one or more computers, a usage prediction regarding an amount of future activity of the server environment predicted to be caused by user requests or active user sessions over a future time period, wherein generating the usage prediction comprises providing information indicating activity of the server environment to a machine learning model trained to provide output indicating a prediction of future user-initiated activity of the server environment, wherein the output of the machine learning model indicates at least one of: a prediction indicating an amount of use predicted to occur over the future time period; a likelihood that future use of at least a minimum level will occur over the future time period; or a predicted time when use is predicted to reach a particular level;
determining, by the one or more computers, that the usage prediction indicates a level of future activity from user requests or active user sessions that is less than a second threshold level, comprising determining that the output of the machine learning model indicates that user-initiated activity of the server environment is likely to be less than the second threshold level for at least a portion of the future time period; and
in response to (i) determining based on the output of the machine learning model that the amount of the detected activity corresponding to user requests or active user sessions over the period of time is below the first threshold level and (ii) determining that the usage prediction indicates a level of future activity from user requests or active user sessions that is less than the second threshold level, initiating, by the one or more computers, shutdown of the server environment.

2. The method of claim 1 , further comprising:
after shutting down the server environment, determining, based at least in part on prior usage of the server environment, that at least a minimum level of user-initiated activity is likely to occur; and
powering on the server environment in response to determining that at least the minimum level of user-initiated activity is likely to occur.

3. The method of claim 2 , comprising storing state information of the server environment, including data for one or more tasks running in the server environment, in non-volatile storage before shutting down the server environment; and
wherein re-starting the server environment comprises loading the state information and resuming the one or more tasks that were running in the server environment before shutting down the server environment.

4. The method of claim 1 , wherein the server environment is a virtual server environment, and shutting down the server environment comprises stopping the virtual server environment.

5. The method of claim 1 , wherein the server environment comprises one or more server computers, and shutting down the server environment comprises powering down the one or more server computers.

6. The method of claim 1 , wherein monitoring the server environment comprises monitoring at least one of: central processing unit utilization, memory usage, an amount of network bandwidth utilized, an amount of input or output operations, a number of tasks running, types of tasks running, priority levels of tasks running, a number of users logged in to the server environment, a number of users actively using the server environment, or a level of interactivity of running tasks.

7. The method of claim 1 , wherein monitoring the server environment comprises measuring utilization of the server environment over the period of time; and
wherein determining the amount of the detected activity that corresponds to user requests or active user sessions over the period of time comprises determining a portion of the measured utilization that supports active user sessions that receive user input at a rate that satisfies a predetermined threshold.

8. A system comprising:
one or more computers; and
one or more computer-readable media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
monitoring, by the one or more computers, a server environment to detect activity of the server environment over a period of time;
determining, by the one or more computers, an amount of the detected activity that corresponds to user requests or active user sessions over the period of time;
determining, by the one or more computers, that the amount of the detected activity corresponding to user requests or active user sessions is below a first threshold level;
generating, by the one or more computers, a usage prediction regarding an amount of future activity of the server environment predicted to be caused by user requests or active user sessions over a future time period, wherein generating the usage prediction comprises providing information indicating activity of the server environment to a machine learning model trained to provide output indicating a prediction of future user-initiated activity of the server environment, wherein the output of the machine learning model indicates at least one of: a prediction indicating an amount of use predicted to occur over the future time period; a likelihood that future use of at least a minimum level will occur over the future time period; or a predicted time when use is predicted to reach a particular level;
determining, by the one or more computers, that the usage prediction indicates a level of future activity from user requests or active user sessions that is less than a second threshold level, comprising determining that the output of the machine learning model indicates that user-initiated activity of the server environment is likely to be less than the second threshold level for at least a portion of the future time period; and
in response to (i) determining based on the output of the machine learning model that the amount of the detected activity corresponding to user requests or active user sessions over the period of time is below the first threshold level and (ii) determining that the usage prediction indicates a level of future activity from user requests or active user sessions that is less than the second threshold level, initiating, by the one or more computers, shutdown of the server environment.

9. The system of claim 8 , wherein the operations further comprise:
after shutting down the server environment, determining, based at least in part on prior usage of the server environment, that at least a minimum level of user-initiated activity is likely to occur; and
powering on the server environment in response to determining that at least the minimum level of user-initiated activity is likely to occur.

10. The system of claim 9 , comprising storing state information of the server environment, including data for one or more tasks running in the server environment, in non-volatile storage before shutting down the server environment; and
wherein re-starting the server environment comprises loading the state information and resuming the one or more tasks that were running in the server environment before shutting down the server environment.

11. The s

Assignees

• Microstrategy Inc

Inventors

• Gardner Richard
• Myers Clayton
• Smith Andrew
• Lang Timothy
• Shen Hao

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06F9/4416 Primary

  Network booting; Remote initial program loading [RIPL] · CPC title

• G06F11/3438

  monitoring of user actions (tracking the activity of the user H04L67/535) · CPC title

• G06N20/00

  Machine learning · CPC title