Abstract: In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

Abstract: In some examples, a server may determine a specification associated with a software module that is to be integrated with a software system. The specification identifies how the software module interacts with the software system. The server may execute a machine learning module to perform an analysis of the specification. The machine learning module may suggest at least one modification to at least a first portion of the specification and may automatically modify at least a second portion of the specification. The server may convert the specification to one or more application programming interface (API) calls and provide a system interface that includes the one or more API calls to enable the software module to interact with the software system. The API calls may include calls to a data integration API, a file transfer API, a messaging API, a database API, or any combination thereof.

Abstract: Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.

Abstract: In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

Abstract: An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The processing platform is configured to abstract a plurality of partner platforms and a plurality of enterprise applications to extract a plurality of connectivity parameters associated with respective ones of the partner platforms and the enterprise applications, to manage connections between the partner platforms and the enterprise applications by implementing connectivity parameters, wherein the implementing provides the partner platforms with centralized access to the enterprise applications via a broker layer, to select one or more of the connectivity parameters to be used in connection with routing of data between a given partner platform and a given enterprise application, and to route the data between the given partner platform and the given enterprise application via the broker layer using the selected one or more connectivity parameters.

Abstract: A method is disclosed including: obtaining one or more values of a system metric, the system metric being associated with a hardware resource of a computing device; detecting whether the system metric is approaching a threshold, the threshold being associated with a key performance indicator (KPI) of the computing device, the detecting being performed based on the obtained values of the system metric; calculating a predicted value of the system metric in response to detecting that the system metric is approaching the threshold, the predicted value of the system metric being calculated by using a linear predictor that is trained using unevenly-sampled training data; detecting whether the predicted value of the system metric exceeds the threshold; and reconfiguring the computing device to prevent the system metric from reaching the predicted value in response to detecting that the predicted value exceeds the threshold.

Abstract: An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The at least one processing platform is configured to provide a plurality of applications with centralized access to a plurality of message oriented middleware (MOM) servers via a connectivity layer, to establish a connection between a given one of the plurality of applications and a given one of the plurality of MOM servers via the connectivity layer, and to exchange data between the given one of the plurality of applications and the given one of the plurality of MOM servers via the connectivity layer.

Abstract: In some examples, a server may determine a specification associated with a software module that is to be integrated with a software system. The specification identifies how the software module interacts with the software system. The server may execute a machine learning module to perform an analysis of the specification. The machine learning module may suggest at least one modification to at least a first portion of the specification and may automatically modify at least a second portion of the specification. The server may convert the specification to one or more application programming interface (API) calls and provide a system interface that includes the one or more API calls to enable the software module to interact with the software system. The API calls may include calls to a data integration API, a file transfer API, a messaging API, a database API, or any combination thereof.

Abstract: An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The at least one processing platform is configured to provide a plurality of applications with centralized access to a plurality of message oriented middleware (MOM) servers via a connectivity layer, to establish a connection between a given one of the plurality of applications and a given one of the plurality of MOM servers via the connectivity layer, and to exchange data between the given one of the plurality of applications and the given one of the plurality of MOM servers via the connectivity layer.

Abstract: An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The processing platform is configured to abstract a plurality of partner platforms and a plurality of enterprise applications to extract a plurality of connectivity parameters associated with respective ones of the partner platforms and the enterprise applications, to manage connections between the partner platforms and the enterprise applications by implementing connectivity parameters, wherein the implementing provides the partner platforms with centralized access to the enterprise applications via a broker layer, to select one or more of the connectivity parameters to be used in connection with routing of data between a given partner platform and a given enterprise application, and to route the data between the given partner platform and the given enterprise application via the broker layer using the selected one or more connectivity parameters.

Abstract: Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.

Abstract: A method is disclosed including: obtaining one or more values of a system metric, the system metric being associated with a hardware resource of a computing device; detecting whether the system metric is approaching a threshold, the threshold being associated with a key performance indicator (KPI) of the computing device, the detecting being performed based on the obtained values of the system metric; calculating a predicted value of the system metric in response to detecting that the system metric is approaching the threshold, the predicted value of the system metric being calculated by using a linear predictor that is trained using unevenly-sampled training data; detecting whether the predicted value of the system metric exceeds the threshold; and reconfiguring the computing device to prevent the system metric from reaching the predicted value in response to detecting that the predicted value exceeds the threshold.