Granted Patent B2

US 12,563,065 · App. 18/230,839 · Granted Feb 24, 2026

Inventors: Manimaran Sundaravel (Chennai, IN); Maneesh Sethia (Telangana, IN)

Assignee: Bank of America Corporation

H04L63/1425H04L63/1458H04L63/1483H04L2463/142

Abstract

Claims (55)

1 . A computing platform comprising:

at least one processor;

a communication interface communicatively coupled to the at least one processor; and

memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:

train, using historical node performance information and historical application parameter information, a node selection model, wherein training the node selection model configures the node selection model to select nodes for application cloud deployment;

receive a request to deploy an application to a cloud network;

input, into the node selection model, parameters of the application and operating conditions of a plurality of nodes of the cloud network, wherein inputting the parameters and the operating conditions into the node selection model causes the node selection model to output a node, of the plurality of nodes of the cloud network, wherein the application is deployed to the node;

queue, along with other applications scheduled for deployment to the plurality of nodes, the application for deployment to the node;

after identifying that the application is first in the queue, deploy the application to the node of the cloud network, wherein deploying the application to the node of the cloud network causes creation, at the node, of a container corresponding to the application;

update, based on the parameters of the application, the operating conditions of the plurality of nodes of the cloud network, and the node, the node selection model; and

generate, based on the operating conditions, a graphical representation of the operating conditions and the plurality of nodes, wherein each operating condition for each node is represented by an intersection of a node row and an operating condition column.

2 . The computing platform of claim 1 , wherein the historical node performance information includes one or more of: computer processing unit (CPU) usage, memory usage, available network bandwidth, or available disk capacity.

3 . The computing platform of claim 1 , wherein the parameters of the application include one or more of: computer processing unit (CPU) availability requirements, memory requirements, network bandwidth availability requirements, or available disk capacity requirements.

4 . The computing platform of claim 1 , wherein the request to deploy the application to the cloud network comprises a request to containerize the application.

5 . The computing platform of claim 1 , wherein outputting, by the node selection model, the node, comprises:

identifying, based on the parameters of the application and the operating conditions of the plurality of nodes, a subset of the plurality of nodes that fails to satisfy processing requirements for the application; and

filtering, from the plurality of nodes, the subset of the plurality of nodes, to produce a filtered plurality of nodes, wherein selection of the node comprises selecting one of the filtered plurality of nodes.

6 . The computing platform of claim 5 , wherein selecting the node comprises:

scoring, using the node selection model, each of the filtered plurality of nodes;

ranking, based on the scores, the filtered plurality of nodes; and

selecting a highest ranked node of the ranked filtered plurality of nodes.

7 . The computing platform of claim 1 , wherein the node selection model comprises a deep reinforcement learning model.

8 . The computing platform of claim 1 , wherein darker shading of the intersection indicates less availability for the corresponding operating condition for the corresponding node and lighter shading of the intersection indicates greater availability for the corresponding operating condition for the corresponding node.

9 . The computing platform of claim 1 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:

train, using historical threat detection information, a threat detection model, wherein training the threat detection model configures the threat detection model to detect container threats;

input, into the threat detection model, the operating conditions of the plurality of nodes, wherein inputting the operating conditions of the plurality of nodes causes the threat detection model to identify a threat to at least one container deployed at the plurality of nodes;

execute, based on identification of the threat, a security action to protect the at least one container; and

update, based on the operating conditions of the plurality of nodes and the threat, the threat detection model.

10 . The computing platform of claim 9 , wherein the threat detection model comprises a deep reinforcement learning model.

11 . The computing platform of claim 9 , wherein executing the security action comprises:

identifying, using the node selection model, an alternative node, wherein the at least one container is deployed to the alternative node; and

modifying deployment of the at least one container to shift the at least one container from a first node, corresponding to the threat, to a second node.

12 . The computing platform of claim 9 , wherein the threat comprises one or more of: a spoofing attack, a tampering attack, or a denial of service attack.

13 . The computing platform of claim 9 , wherein training the threat detection model comprises setting, for each of the operating conditions, a threat threshold, wherein identifying the threat comprises identifying that at least one of the operating conditions exceeds a corresponding threat threshold.

14 . The computing platform of claim 9 , wherein training the threat detection model comprises generating correlations between historical operating condition patterns and known threats, wherein identifying the threat comprises identifying that a current pattern of the operating conditions matches one of the historical operating condition patterns.

15 . A method comprising:

at a computing platform comprising at least one processor, a communication interface, and memory:

training, using historical node performance information and historical application parameter information, a node selection model, wherein training the node selection model configures the node selection model to select nodes for application cloud deployment;

receiving a request to deploy an application to a cloud network;

inputting, into the node selection model, parameters of the application and operating conditions of a plurality of nodes of the cloud network, wherein inputting the parameters and the operating conditions into the node selection model causes the node selection model to output a node, of the plurality of nodes of the cloud network, wherein the application is deployed to the node;

queueing, along with other applications scheduled for deployment to the plurality of nodes, the application for deployment to the node;

after identifying that the application is first in the queue, deploying the application to the node of the cloud network, wherein deploying the application to the node of the cloud network causes creation, at the node, of a container corresponding to the application;

updating, based on the parameters of the application, the operating conditions of the plurality of nodes of the cloud network, and the node, the node selection model; and

generating, based on the operating conditions, a graphical representation of the operating conditions and the plurality of nodes, wherein each operating condition for each node is represented by an intersection of a node row and an operating condition column.

16 . The method of claim 15 , wherein the historical node performance information includes one or more of: computer processing unit (CPU) usage, memory usage, available network bandwidth, or available disk capacity.

17 . The method of claim 15 , wherein the parameters of the application include one or more of: computer processing unit (CPU) availability requirements, memory requirements, network bandwidth availability requirements, or available disk capacity requirements.

18 . The method of claim 15 , wherein the request to deploy the application to the cloud network comprises a request to containerize the application.

19 . One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

train, using historical node performance information and historical application parameter information, a node selection model, wherein training the node selection model configures the node selection model to select nodes for application cloud deployment;

receive a request to deploy an application to a cloud network;

input, into the node selection model, parameters of the application and operating conditions of a plurality of nodes of the cloud network, wherein inputting the parameters and the operating conditions into the node selection model causes the node selection model to output a node, of the plurality of nodes of the cloud network, wherein the application is deployed to the node;

queue, along with other applications scheduled for deployment to the plurality of nodes, the application for deployment to the node;

after identifying that the application is first in the queue, deploy the application to the node of the cloud network, wherein deploying the application to the node of the cloud network causes creation, at the node, of a container corresponding to the application;

update, based on the parameters of the application, the operating conditions of the plurality of nodes of the cloud network, and the node, the node selection model; and

generate, based on the operating conditions, a graphical representation of the operating conditions and the plurality of nodes, wherein each operating condition for each node is represented by an intersection of a node row and an operating condition column.

Assignments (1)

Continuity (1)

References Cited (34)