US 12,118,396 B2Granted

Computing resource autoscaling based on predicted metric behavior

AssigneeMicrosoft Technology Licensing, LLC

InventorsAdi Eldar, Shahar Davidovich

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Abstract

Methods, systems, apparatuses, and computer-readable storage mediums described herein are configured to automatically allocate or deallocate computing resources based on a prediction of performance metrics behavior. For instance, the historical behavior of compute metrics (or a time series obtained therefor) is analyzed to detect a seasonality (i.e., a seasonal pattern) and a trend therefor. A prediction of the metrics' behavior for a future time frame is determined based on the seasonality and the trend. Based on the prediction, computing resources are allocated or deallocated at or prior to the future time frame occurring. For example, if a prediction is made that a particular metric will increase, additional compute resources are allocated to handle the increase ahead of the predicted metric increase. If a prediction is made that a particular metric will decrease, compute resources are deallocated at the time the metric is predicted to decrease.

Claims16

Dependent claims

A system comprising:

at least one processor circuit;
and
at least one memory that stores program code that, when executed by the at least one processor circuit, causes the system to perform operations comprising:
obtaining a first time series of data values, for a first time frame, corresponding to a first metric associated with a first type of process performed by a central processing unit (CPU);
obtaining a second time series of data values, for the first time frame, corresponding to a second metric associated with a second type of process performed by the CPU;
detecting a first seasonal time period and a first trend of the first time series of data values that occurs in the first seasonal time period;
detecting a second seasonal time period and a second trend of the second time series of data values that occurs in the second seasonal time period;
generating a first prediction for the first metric for a second time frame, subsequent to the first time frame, based on the first seasonal time period and the first trend;
generating a second prediction for the second metric for the second time frame based on the second seasonal time period and the second trend;
determining that at least one other type of process is performed by the CPU;
performing, using a linear regression model, a linear regression using a combination of the first prediction and the second prediction to compensate for the at least one other type of process performed by the first CPU, wherein the linear regression calculates a third prediction related to usage of the first CPU for the second time frame;
and
automatically initiating allocation or deallocation of a CPU core based on the third prediction.

10 more elements

A method configured to automatically allocate or deallocate computing resources, comprising:

obtaining a first time series of data values, for a first time frame, corresponding to a first metric associated with a first type of process performed by a central processing unit (CPU);
obtaining a second time series of data values, for the first time frame, corresponding to a second metric associated with a second type of process performed by the CPU;
detecting a first seasonal time period and a first trend of the first time series of data values that occurs in the first seasonal time period;
detecting a second seasonal time period and a second trend of the second time series of data values that occurs in the second seasonal time period;
generating a first prediction for the first metric for a second time frame, subsequent to the first time frame, based on the first seasonal time period and the first trend;
generating a second prediction for the second metric for the second time frame based on the second seasonal time period and the second trend;
determining that at least one other type of process is performed by the CPU;
performing, using a linear regression model, a linear regression using a combination of the first prediction and the second prediction to compensate for the at least one other type of process performed by the first CPU, wherein the linear regression calculates a third prediction related to usage of the first CPU for the second time frame;
and
automatically initiating an allocation or a deallocation of a CPU core based on the third prediction.

7 more elements

A computer-readable storage medium having program instructions recorded thereon that, when executed by a processor circuit, causes a system to perform operations comprising:

obtaining a first time series of data values, for a first time frame, corresponding to a first metric associated with a first type of process performed by a central processing unit (CPU);
obtaining a second time series of data values, for the first time frame, corresponding to a second metric associated with a second type of process performed by the CPU;
detecting a first seasonal time period and a first trend of the first time series of data values that occurs in the first seasonal time period;
detecting a second seasonal time period and a second trend of the second time series of data values that occurs in the second seasonal time period;
generating a first prediction for the first metric for a second time frame, subsequent to the first time frame, based on the first seasonal time period and the first trend;
generating a second prediction for the second metric for the second time frame based on the second seasonal time period and the second trend;
determining that at least one other type of process is performed by the CPU;
performing, using a linear regression model, a linear regression using a combination of the first prediction and the second prediction to compensate for the at least one other type of process performed by the first CPU, wherein the linear regression calculates a third prediction related to usage of the first CPU for the second time frame;
and
automatically initiating an allocation or a deallocation of a CPU core based on the third prediction.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/317,571Filed 11 May 2021
Published: US 2022/0374273 A124 Nov 2022
Granted: 15 Oct 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification5 codes

G06F 9/5027
To service a request: the resource being a machine, e.g. CPUs, Servers, Terminals
G06F 16/2465
Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries: query processing support for facilitating data mining…
G06F 16/254
Integrating or interfacing systems involving database management systems: extract, transform and load [ETL] procedures, e.g. ETL data flows in…
G06F 2209/5019
Indexing scheme relating to G06F9/50: workload prediction

Family

US 2022/0374273 A1Pre-grant publication · published 24 Nov 2022
US 12,118,396 B2This patent

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

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