Automated mocking of computer system deployments

Patent metadata

Field	Value
Publication number	US-12001321-B2
Application number	US-202217929463-A
Country	US
Kind code	B2
Filing date	Sep 2, 2022
Priority date	Oct 19, 2020
Publication date	Jun 4, 2024
Grant date	Jun 4, 2024

Abstract

Automated mocking of computer system deployments is facilitated. A method as described herein can include associating, by a first system operatively coupled to a processor, respective properties of a first deployment of a second system on a first computing device with respective automation mapping functions; executing, by the first system, the automation mapping functions in an order defined by dependencies between respective ones of the automation mapping functions, resulting in a series of system modeling tasks and an order associated with the series of system modeling tasks; and performing, by the first system, the series of system modeling tasks in the order associated therewith, resulting in a second deployment of the second system being created on a second computing device that is distinct from the first computing device.

First claim

What is claimed is:

1. A system, comprising:
a processor; and
a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:
associating properties of a first deployment of a computing system on a first computing device with respective automation mapping functions;
scaling a first portion of the properties of the first deployment, associated with a first one of the automation mapping functions, according to a first scaling factor;
scaling a second portion of the properties of the first deployment, associated with a second one of the automation mapping functions, according to a second scaling factor;
executing the automation mapping functions in an order defined by dependencies between the automation mapping functions, resulting in a series of deployment modeling tasks; and
performing deployment modeling tasks, of the series of deployment modeling tasks , resulting in a second deployment of the computing system being created on a second computing device that is not the first computing device, wherein the performing of the deployment modeling tasks is based on bound variable inputs, associated with the dependencies between the automation mapping functions, and deployment variable inputs, given by the properties of the first deployment as scaled by the first scaling factor and the second scaling factor.

2. The system of claim 1 , wherein the first computing device is associated with a first number of first computing nodes, wherein the second computing device is associated with a second number of second computing nodes, and wherein the operations further comprise:
determining a scaling factor, selected from a group comprising the first scaling factor and the second scaling factor, based on a ratio of the first number to the second number.

3. The system of claim 1 , wherein the operations further comprise:
obtaining deployment data from the first computing device, wherein the properties of the first deployment are based on the deployment data.

4. The system of claim 3 , wherein the deployment data comprises data of at least one category selected from a group comprising physical configuration data for the first computing device, software configuration data for software utilized by the computing system on the first computing device, and environmental interaction data associated with the computing system on the first computing device.

5. The system of claim 1 , wherein the second computing device is selected from a group of computing devices comprising a physical computing device and a virtualized computing device.

6. The system of claim 1 , wherein the operations further comprise:
applying a simulated client load to the second deployment of the computing system on the second computing device.

7. A method, comprising:
associating, by a first system comprising a processor, respective properties, of a first deployment of a second system on a first computing device, with respective automation mapping functions;
scaling, by the first system, a first subset of the respective properties of the first deployment, associated with a first one of the automation mapping functions, according to a first scaling factor;
scaling, by the first system, a second subset of the respective properties of the first deployment, associated with a second one of the automation mapping functions, according to a second scaling factor;
executing, by the first system, the automation mapping functions in an order defined by dependencies between respective ones of the automation mapping functions, resulting in a series of system modeling tasks; and
performing, by the first system, the series of system modeling tasks, resulting in a second deployment of the second system being created on a second computing device that is different from the first computing device, wherein the performing of the series of system modeling tasks is based on bound variable inputs, associated with the dependencies between the automation mapping functions, and deployment variable inputs, given by the properties of the first deployment as scaled by the first scaling factor and the second scaling factor.

8. The method of claim 7 , wherein at least one of the first scaling factor and the second scaling factor is based on a difference between a first number of computing nodes associated with the first computing device and a second number of computing nodes associated with the second computing device.

9. The method of claim 7 , further comprising:
collecting, by the first system, deployment data from the first computing device; and
determining, by the first system, the respective properties of the first deployment based on the deployment data.

10. The method of claim 9 , wherein the deployment data comprises data comprises at least one of physical configuration data for the first computing device, software configuration data for software utilized by the second system on the first computing device, or environmental interaction data associated with the second system on the first computing device.

11. The method of claim 7 , further comprising:
applying, by the first system, a simulated client load to the second deployment of the second system on the second computing device.

12. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
associating properties of a first deployment of a data storage system, as implemented on a first computing site, to automation mapping functions according to deployment data associated with the first deployment;
scaling a first portion of the properties of the first deployment, associated with a first one of the automation mapping functions, using a first scaling factor;
scaling a second portion of the properties of the first deployment, associated with a second one of the automation mapping functions, using a second scaling factor;
executing the automation mapping functions in an order defined by dependencies between respective ones of the automation mapping functions, resulting in an ordered series of deployment transfer tasks; and
executing the ordered series of deployment transfer tasks, resulting in a second deployment of the data storage system being created on a second computing site that is distinct from the first computing site, wherein the executing of the ordered series of deployment transfer tasks is based on bound variable inputs, associated with the dependencies between the respective ones of the automation mapping functions, and deployment variable inputs, given by the properties of the first deployment as scaled by the first scaling factor and the second scaling factor.

13. The non-transitory machine-readable medium of claim 12 , wherein the first computing site is associated with a first number of first computing nodes, wherein the second computing site is associated with a second number of second computing nodes, and wherein the operations further comprise:
determining a scaling factor, selected from a group comprising the first scaling factor and the second scaling factor, based on a ratio of the first number to the second number.

14. The non-transitory machine-readable medium of claim 12 , wherein the operations further comprise:
collecting deployment data associated with the first deployment of the data storage system; and
determining the properties of the first deployment of the data storage system based on the deployment data.

15. The non-transitory machine-readable medium of claim 14 , wherein the deployment data comprises data selecte

Assignees

• Emc Ip Holding Co Llc

Inventors

• Bryan Matthew
• Kumar Shiv Shankar
• Chandra Anurag
• Patil Shubham

Classifications

• G06F11/3698 Primary

  Environments for analysis, debugging or testing of software · CPC title

• G06F11/3664 Primary

  Physics · mapped topic

• G06F8/60

  Software deployment · CPC title

• G06F11/3692

  for test results analysis · CPC title

• G06F11/3688

  for test execution, e.g. scheduling of test suites · CPC title