Predictive model-based intelligent system for automatically scaling and managing provisioned computing resources

Patent metadata

Field	Value
Publication number	US-2019243691-A1
Application number	US-201815887889-A
Country	US
Kind code	A1
Filing date	Feb 2, 2018
Priority date	Feb 2, 2018
Publication date	Aug 8, 2019
Grant date	—

Abstract

A system for automatically scaling provisioned resources includes an input interface and a processor. The input interface is configured to receive an estimate of a required number of processing threads. The processor is configured to determine required resources for processing the required number of processing threads using a model; provision the required resources; indicate to execute client tasks using the provisioned resources; determine server telemetry or logging data for the provisioned resources; provide the server telemetry or the logging data to the model; determine a resource utilization score based at least in part on the server telemetry or the logging data; determine a provisioning performance reward based at least in part on the resource utilization score; and adjust model parameters using the provisioning performance reward.

First claim

What is claimed is:

1 . A system for automatically scaling provisioned resources, comprising:
an input interface configured to:
receive an estimate of a required number of processing threads; and
a processor configured to:
determine required resources for processing the required number of processing threads using a model;
provision the required resources;
indicate to execute client tasks using the provisioned resources;
determine server telemetry or logging data for the provisioned resources;
provide the server telemetry or the logging data to the model;
determine a resource utilization score based at least in part on the server telemetry or the logging data;
determine a provisioning performance reward based at least in part on the is resource utilization score; and
adjust model parameters using the provisioning performance reward.

2 . The system of claim 1 , wherein indicating to execute client tasks causes the client tasks to execute.

3 . The system of claim 1 , wherein the required number of processing threads comprises a required number of processing threads for a server type of a plurality of server types.

4 . The system of claim 1 , wherein the client tasks comprise client tasks for a client of a plurality of clients.

5 . The system of claim 1 , wherein the resource utilization score is based at least in part on a nonlinear function of the performance metric data.

6 . The system of claim 1 , wherein the resource utilization score is based at least in part on asymmetric function of the server telemetry or the logging data.

7 . The system of claim 1 , wherein the provisioner performance reward comprises:
a first value in response to the resource utilization score indicating the provisioned resources are underutilized; a second value in response to the resource utilization score indicating the provisioned resources are correctly utilized; and a third value in response to the resource utilization score indicating the provisioned resources are overutilized.

8 . The system of claim 1 , wherein a next determination of required resources is based at least in part on the server telemetry or the logging data provided to the model.

9 . The system of claim 1 , wherein the model comprises a neural network.

10 . The system of claim 9 , wherein the neural network comprises one or more of: a neuron layer, a plurality of neuron layers, a dense neuron layer, a dropout neuron layer, a feedforward layer, a recurrent layer, a long short term memory layer, a rectified linear unit activation function, or a tan h activation function.

11 . The system of claim 1 , wherein adjusting the model using the resource utilization score comprises using reinforcement learning.

12 . The system of claim 1 , wherein the estimate of the required number of processing threads is based at least in part on historical usage data.

13 . The system of claim 1 , wherein the estimate of the required number of processing threads is determined using a randomized packing algorithm.

14 . The system of claim 1 , wherein a determination of required resources is made at a regular interval.

15 . The system of claim 14 , wherein the regular interval comprises every minute, every 5 minutes, every 10 minutes, or every hour.

16 . The system of claim 1 , wherein determining the server telemetry or the logging data comprises determining a set of performance metrics.

17 . The system of claim 16 , wherein determining the server telemetry or the logging data comprises processing the set of performance metrics.

18 . The system of claim 17 , wherein processing the set of performance metrics comprises one or more of: scaling one or more performance metrics, computing a linear function of one or more performance metrics, aggregating performance metrics, computing a nonlinear function of one or more performance metrics, thresholding one or more performance metrics, processing the set of performance metrics using a model, processing the set of performance metrics using a machine learning model, or processing the set of metrics using a neural network.

19 . A method for automatically scaling provisioned resources, comprising:
receiving an estimate of a required number of processing threads; determining, using a processor, required resources for processing the required number of processing threads using a model; provisioning the required resources; indicating to execute client tasks using the provisioned resources; determining server telemetry or logging data for the provisioned resources; providing the server telemetry or the logging data to the model; determining a resource utilization score based at least in part on the server telemetry or the logging data; determining a provisioning performance reward based at least in part on the resource is utilization score; and adjusting model parameters using the provisioning performance reward.

20 . A computer program product for automatically scaling provisioned resources, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:
receiving an estimate of a required number of processing threads; determining required resources for processing the required number of processing threads using a model; provisioning the required resources; indicating to execute client tasks using the provisioned resources; determining server telemetry or logging data for the provisioned resources; providing the server telemetry or the logging data to the model; determining a resource utilization score based at least in part on the server telemetry or the logging data; determining a provisioning performance reward based at least in part on the resource utilization score; and adjusting model parameters using the provisioning performance reward.

Assignees

• Workday Inc

Inventors

• Labute Montiago Xavier
• Qu teng
• Stratton James Michael
• Duan Xiaoqun Joyce
• Boyd Alex James

Classifications

• G06F9/5077 Primary

  Logical partitioning of resources; Management or configuration of virtualized resources (specific details on emulation or internal functioning of virtual machines G06F9/455) · CPC title

• G06N3/08

  Learning methods · CPC title

• G06F9/505

  considering the load · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/045

  Combinations of networks · CPC title