Abstract: A method for providing an application to facilitate authenticated file transfers across a plurality of computing platforms is disclosed. The method includes receiving, from a client application, a call to perform an action, the call including a message to transfer a file; validating, by using an identity federation service, the call based on the message; authorizing the call and the client application based on a result of the validating; accessing a networked repository via a secured communication protocol when the call and the client application is authorized; and initiating the action.

Abstract: Various methods, apparatuses/systems, and media for implementing a smart failover module is disclosed. A processor detects an application specific system fault or degradation event in a first availability zone (AZ) on which an application is running during normal runtime of the application; determines, in response to detecting the application specific system fault or degradation event, whether the application includes an active-passive application infrastructure in which the first AZ is paired with a passive AZ; enables traffic, in connection with running or deployment of the application, on the passive availability zone in response to determining that the application includes an active-passive application infrastructure; and disables traffic from the first AZ on which the application specific system fault or degradation has been detected in response to determining that the application does not include an active-passive application infrastructure and/or in response to enabling traffic on the passive AZ.

Abstract: Various methods, apparatuses/systems, and media for implementing a smart cloud deployment module is disclosed. A datacenter stores information data of all availability zones (AZs) associated with deployment of an application. A processor builds an availability zone (AZ) application programming interface (API) and calls the AZ API to retrieve the information data from the datacenter; dynamically and automatically determines, in response to calling the AZ API, prior to deploying the application, whether a first AZ defined in a deployment pipeline among the AZs within the datacenter is healthy for deployment of the application; and deploys the application utilizing the deployment pipeline when it is determined that the first AZ defined in the deployment pipeline is healthy. The processor also checks for another healthy AZ within the same datacenter or within other regions outside of the datacenter when it is determined that the first AZ is not healthy.

Abstract: Various methods, apparatuses/systems, and media for automatically detecting discrepancy between preset data and input data are provided. A processor implements a platform and language agnostic smart data discrepancy detection module that automatically detects discrepancy between preset data and input data (i.e., larger than the preset data) during data processing of a commercial transaction involving a gratuity, particularly when a credit card payment is being made. The processor also implements an optimized processes of conducting a commercial transaction that includes a gratuity data based on user preferences that are obtainable from an application during execution thereof, and automatically alerts the user when data discrepancy is detected, i.e., when the gratuity data is more than preset gratuity data set by the user on the application.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: A method for facilitating automated application management based on activity is disclosed. The method includes monitoring metrics that correspond to an application, the metrics including a network traffic metric; determining, for the application, whether the metrics satisfy a predetermined threshold, the predetermined threshold including an idle time threshold; deactivating the application when the corresponding metrics satisfy the predetermined threshold; receiving, from a client application, a request for the deactivated application; initiating a health check for the application in response to the request; and activating the application based on a result of the health check.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: Various methods, apparatuses/systems, and media for automatically establishing a communication between two or more applications that do not share a compatible authentication model are disclosed. A receiver receives a request from a first application to communicate with a second application, wherein the first application supports a first authentication model and the second application supports a second authentication model which is incompatible with the first authentication model. A processor utilizes a configurable gateway layer, in response to receiving the request, to mediate a communication between the first application and the second application; and routes the request from the first application to the configurable gateway layer. The configurable gateway layer translates the first authentication model to the second authentication model.

Abstract: Various methods, apparatuses/systems, and media for automatically establishing a communication between two or more applications that do not share a compatible authentication model are disclosed. A receiver receives a request from a first application to communicate with a second application, wherein the first application supports a first authentication model and the second application supports a second authentication model which is incompatible with the first authentication model. A processor utilizes a configurable gateway layer, in response to receiving the request, to mediate a communication between the first application and the second application; and routes the request from the first application to the configurable gateway layer. The configurable gateway layer translates the first authentication model to the second authentication model.

Abstract: Various methods, apparatuses/systems, and media for automatically fixing health of a certain component of an application are disclosed. A processor sets a corresponding predetermined threshold value for health of each component of a plurality of components of an application required for running the application; constantly monitors health of the plurality of components. When it is determined, in response to constantly monitoring, that the health of a certain component has reached to a value that is equal to the corresponding predetermined threshold value of the certain component, the processor automatically calls corresponding application programming interface (API) to trigger automated processes that includes collecting all necessary data corresponding to fixing of the health of the certain component related to the application from a plurality of data sources; creating a data file on demand that includes the necessary data; and fixing the health of the certain component based on the data file.

Abstract: A method for providing an application to facilitate authenticated file transfers across a plurality of computing platforms is disclosed. The method includes receiving, from a client application, a call to perform an action, the call including a message to transfer a file; validating, by using an identity federation service, the call based on the message; authorizing the call and the client application based on a result of the validating; accessing a networked repository via a secured communication protocol when the call and the client application is authorized; and initiating the action.

Abstract: Various methods, apparatuses/systems, and media for implementing a smart failover module is disclosed. A processor detects an application specific system fault or degradation event in a first availability zone (AZ) on which an application is running during normal runtime of the application; determines, in response to detecting the application specific system fault or degradation event, whether the application includes an active-passive application infrastructure in which the first AZ is paired with a passive AZ; enables traffic, in connection with running or deployment of the application, on the passive availability zone in response to determining that the application includes an active-passive application infrastructure; and disables traffic from the first AZ on which the application specific system fault or degradation has been detected in response to determining that the application does not include an active-passive application infrastructure and/or in response to enabling traffic on the passive AZ.

Abstract: Various methods, apparatuses/systems, and media for implementing a smart cloud deployment module is disclosed. A datacenter stores information data of all availability zones (AZs) associated with deployment of an application. A processor builds an availability zone (AZ) application programming interface (API) and calls the AZ API to retrieve the information data from the datacenter; dynamically and automatically determines, in response to calling the AZ API, prior to deploying the application, whether a first AZ defined in a deployment pipeline among the AZs within the datacenter is healthy for deployment of the application; and deploys the application utilizing the deployment pipeline when it is determined that the first AZ defined in the deployment pipeline is healthy. The processor also checks for another healthy AZ within the same datacenter or within other regions outside of the datacenter when it is determined that the first AZ is not healthy.