Bank of America Corporation

Karthik Rajan Venkataraman Palani

Madhusudhanan Krishnamoorthy

Iruvanti John Dinakar

Thangaselvi Arichandrapandian

Venkatasubramanian Narayanaswamy

Aravind Ravichandran

Systems, computer program products, and methods are described herein for determining software dependencies and software change impacts on computer processing. The present disclosure is configured to identify at least one computing component within a network environment; identify historical data for the at least one computing component; apply the historical data to a dependency quotient model; generate, by the dependency quotient model, a dependency quotient based on the at least one computing component, the historical data, and a relationship between the at least one computing component and at least one secondary computing component; and generate, based on the dependency quotient, a system architecture interface component, wherein the system architecture interface component comprises data of the at least one computing component and the at least one secondary computing component within the network environment.

Systems and methods for determining software dependencies and software change impacts on computer processing

Various aspects of the disclosure relate to infrastructure integration and migration of computing services between different computing infrastructure platforms. A system may utilize artificial intelligence (AI) and/or machine learning (ML) models configured to resolve a problem with allocating services between a monolithic infrastructure and one or more cloud computing systems. The infrastructure integration framework and migration system gathers data from a plurality of sources and consolidating the results in a central database. The infrastructure integration framework and migration system analyzes existing architecture operation of the enterprise network to gather information to be stored in the central database. The AI and/or ML models may be trained using the cloud system information and/or the existing architecture information. Automated predictions are ranked and migration may automatically be initiate by the infrastructure integration framework and migration system when certain threshold conditions are met.

Infrastructure integration framework and migration system

Apparatus and methods to automatically create and implement update and migration workflows are provided. A digital twin auto-coding orchestrator may receive an updated version of a software program. The auto-coding orchestrator may gather infrastructure hardware data and version data. The auto-coding orchestrator may analyze the data. The auto-coding orchestrator may generate a workflow and code to update the older version to the updated version with the least amount of disruption and downtime. The auto-coding orchestrator may implement the workflow and update the software program to the updated version.

Digital twin auto-coding orchestrator

A method for monitoring and management of a cloud-based computing system is provided. The method includes sending a first data stream to a first pod of a first worker node of a cloud-based computing system. First logs are received from the first pod of the first worker node, which are generated by the first pod while processing the first data stream. A first age of the first pod is determined. In response to the first age being less than a first age threshold, a first failure chance and a first failure timeline are determined for the first pod based on the first logs. In response to the first failure chance being greater than a first failure threshold, a first report is sent to a primary node of the cloud-based computing system, which includes the first failure chance, the first failure timeline, and a first template for the first pod.

Monitoring and management of a cloud-based computing system

A system accesses a disk image of a first software container and collected sensor data for a computer server. The system sequentially analyzes the sequence of layers of the disk image, and generates, based on the sequential analysis of the sequence of layers of the disk image, an auto coding sequence. The auto coding sequence includes a sequence of instructions for creating a new disk image. The system determines, based on the collected sensor data and the sequential analysis of the sequence of layers of the disk image, a sequential list of software needed for the computer server. The system determines, using the sequential list of software needed for the computer server, a plurality of infra requirements for a new software container. The system generates the new software container and the new disk image using the auto coding sequence and the plurality of infra requirements.

Container image optimizer

A memory stores a log that includes first, second, and third datasets each containing server information collected at different times. The processor forms vectors from the datasets, calculates distance vectors between them and uses the distance vectors and pairwise angles between distance vectors to predict a dataset for a first future time. When the first future time arrives, the processor collects the server information, stores it in a fourth dataset, and calculates a variance between that dataset and the predicted dataset. Using the variance, distance vectors, and angles, the processor determines a second predicted dataset corresponding to a second future time, from which it determines a server usage prediction. If the server usage prediction is below a decommission threshold, the processor decommissions the server. If the server usage prediction is above a capacity threshold, the processor generates a report advising an administrator to upgrade or remove projects from the server.

Growing community of inventors

Chennai, India

Karthik Rajan Venkataraman Palani