System and methods for improved adoption of cloud container profiles

Patent metadata

Field	Value
Publication number	US-11698803-B2
Application number	US-201916541996-A
Country	US
Kind code	B2
Filing date	Aug 15, 2019
Priority date	Aug 15, 2018
Publication date	Jul 11, 2023
Grant date	Jul 11, 2023

Abstract

System and methods providing for categorizing individual virtual machines, as well as the associated application that they form by working in concert, into groups based on the feasibility of hosting the processes that occur on a virtual machine within a container, as well as the relative difficulty of doing so on a virtual machine and application level. The data used to create these scores is collected from the individual machines, at regular intervals through the use of an automated scoring engine that collects and aggregates the data. Said data is then analyzed by the system, that with the aid of passed in configuration data, is configured to generate the scores to allows for an educated and focused effort to migrate from hosting applications on virtual machines to hosting applications on containers.

First claim

What is claimed is:

1. A system for determining a subset of candidate virtual machine computing instances for transition into container-based computing instances from a set of virtual machine computing instances, the system comprising:
a processor and computer memory, the processor configured to:
receive one or more data sets representing (i) characteristics of operation of each of virtual machine computing instance of the set of virtual machine computing instances, the one or more data sets including at least one of processor usage, memory usage, or network usage, and (ii) an integrated development environment (IDE) of each virtual machine computing instance;
process the one or more data sets to output a metric representative of a relative ease of containerization for each virtual machine computing instance;
generate a data structure storing a subset of candidate virtual machine computing instances determined based on the one or more virtual machine computing instances having a metric greater than a predefined threshold; and
segment the subset of candidate virtual machine computing instances into one or more container groups each corresponding to one or more container devices, each container device having a shared operating system shared across all containers associated with the container group hosted by the container device;
wherein the data structure is processed by a downstream computing process for transitioning the subset of candidate virtual machine computing instances to be hosted on one or more corresponding container devices based on the identified one or more container groups; and
wherein the segmenting of the subset of candidate virtual machine computing instances into one or more container groups each corresponding to one or more container devices is based at least on an inferred role of the corresponding candidate virtual machine instance determined based at least on the IDE of the corresponding candidate virtual machine instance.

2. The system of claim 1 , wherein the data structure stores the data field representations of the subset of candidate virtual machine computing instances in a prioritized order based on a corresponding metric representative of a relative ease of containerization, the prioritized order; and
wherein the transitioning of the subset of candidate virtual machine computing instances to be hosted on one or more corresponding container devices is conducted in the prioritized order.

3. The system of claim 1 , wherein the processing the one or more data sets to output the metric representative of the relative ease of containerization for each virtual machine computing instance includes using one or more regular expression strings.

4. The system of claim 1 , wherein the processing the one or more data sets to output the metric representative of the relative ease of containerization for each virtual machine computing instance includes using a machine learning data architecture configured to process the one or more data sets using a maintained unsupervised machine learning model based at least on one or more chargeback models representing cost savings.

5. The system of claim 4 , wherein the processing the one or more data sets to output a metric representative of a relative ease of containerization for each virtual machine computing instance includes inferring a role of virtual machine based on the IDE of the virtual machine computing instance.

6. The system of claim 1 , wherein the transmitting of the control signals for transitioning the subset of candidate virtual machine computing instances includes provisioning the one or more container devices and the corresponding shared operating systems.

7. The system of claim 1 , wherein the segmenting of the subset of candidate virtual machine computing instances into one or more container groups each corresponding to one or more container devices is based at least on an estimated resource requirement of the corresponding candidate virtual machine instance determined based at least on the IDE of the corresponding candidate virtual machine instance.

8. A method for determining a subset of candidate virtual machine computing instances for transition into container-based computing instances from a set of virtual machine computing instances, the method comprising:
receiving one or more data sets representing (i) characteristics of operation of each of virtual machine computing instance of the set of virtual machine computing instances, the one or more data sets including at least one of processor usage, memory usage, or network usage, and (ii) an integrated development environment (IDE) of each virtual machine computing instance;
processing the one or more data sets to output a metric representative of a relative ease of containerization for each virtual machine computing instance;
generating a data structure storing a subset of candidate virtual machine computing instances determined based on the one or more virtual machine computing instances having a metric greater than a predefined threshold; and
segmenting the subset of candidate virtual machine computing instances into one or more container groups each corresponding to one or more container devices, each container device having a shared operating system shared across all containers associated with the container group hosted by the container device;
wherein the data structure is processed by a downstream computing process for transitioning the subset of candidate virtual machine computing instances to be hosted on one or more corresponding container devices based on the identified one or more container groups; and
wherein the segmenting of the subset of candidate virtual machine computing instances into one or more container groups each corresponding to one or more container devices is based at least on an inferred role of the corresponding candidate virtual machine instance determined based at least on the IDE of the corresponding candidate virtual machine instance.

9. The method of claim 8 , wherein the data structure stores the data field representations of the subset of candidate virtual machine computing instances in a prioritized order based on a corresponding metric representative of a relative ease of containerization, the prioritized order; and
wherein the transitioning of the subset of candidate virtual machine computing instances to be hosted on one or more corresponding container devices is conducted in the prioritized order.

10. The method of claim 8 , wherein the processing the one or more data sets to output the metric representative of the relative ease of containerization for each virtual machine computing instance includes using one or more regular expression strings.

11. The method of claim 8 , wherein the processing the one or more data sets to output the metric representative of the relative ease of containerization for each virtual machine computing instance includes using a machine learning data architecture configured to process the one or more data sets using a maintained unsupervised machine learning model based at least on one or more chargeback models representing cost savings.

12. The method of claim 11 , wherein the processing the one or more data sets to output a metric representative of a relative ease of containerization for each virtual machine computing instance includes inferring a role of virtual machine based on the IDE of the virtual machine computing instance.

13. The method of claim 8 , wherein the transmitting of the control signals for transitioning the subset of candidate virtual machine computing instances includes provisioning the one or more container devices and the corresponding shar

Assignees

• Royal Bank Of Canada

Inventors

• Abbott Jacob
• Beck James
• Du Jacquelyn

Classifications

• G06F16/2228

  Indexing structures · CPC title

• G06F9/45533 Primary

  Hypervisors; Virtual machine monitors · CPC title

• G06F9/455 Primary

  Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title