Abstract: In an approach for augmenting contextual timeline markers on a virtual video conversation, a processor records a virtual meeting on a virtual meeting platform with one or more participants. Responsive to the virtual meeting chair ending the virtual meeting with one or more incomplete tasks, a processor creates an augmented recording of the virtual meeting, wherein the augmented recording includes a timeline composed of a plurality of timeline markers, wherein a timeline marker is associated with a conversation that occurs at a particular contextual conversation point during the virtual meeting. A processor enables one or more post-meeting collaboration enabled conversations between the virtual meeting chair and one or more nonparticipants, wherein the one or more incomplete tasks are completed. A processor requests feedback on a timeline marker response associated with the one or more incomplete tasks from a timeline marker creator. A processor applies the feedback received.

Abstract: Determining network communication parameters by analyzing network packets at the communication endpoints to determine number of requests and responses; and determining from the number of requests and responses which are follow up requests. The follow up requests are characterized as communications being performed with a pull model. Notifications are provided of applications employing pull model communications with the client for conversion to push model.

Abstract: In an approach for augmenting contextual timeline markers on a virtual video conversation, a processor records a virtual meeting on a virtual meeting platform with one or more participants. Responsive to the virtual meeting chair ending the virtual meeting with one or more incomplete tasks, a processor creates an augmented recording of the virtual meeting, wherein the augmented recording includes a timeline composed of a plurality of timeline markers, wherein a timeline marker is associated with a conversation that occurs at a particular contextual conversation point during the virtual meeting. A processor enables one or more post-meeting collaboration enabled conversations between the virtual meeting chair and one or more nonparticipants, wherein the one or more incomplete tasks are completed. A processor requests feedback on a timeline marker response associated with the one or more incomplete tasks from a timeline marker creator. A processor applies the feedback received.

Abstract: Determining network communication parameters by analyzing network packets at the communication endpoints to determine number of requests and responses; and determining from the number of requests and responses which are follow up requests. The follow up requests are characterized as communications being performed with a pull model. Notifications are provided of applications employing pull model communications with the client for conversion to push model.

Abstract: Predictive scaling of containers is provided based on obtaining, by one or more processors, prior transaction data on one or more of a user's prior transactions on one or more transaction-centric applications, and analyzing by the processor(s), the prior transaction data to predict whether a current transaction of the user on an application is deterministic or non-deterministic as to one or more functional layers of multiple functional layers of the application. Based on predicting that the current transaction is deterministic as to the functional layer(s) of the application, the processor(s) determines compute resource requirements for the deterministic transaction, and launches one or more containers in advance to run the functional layer(s) based, at least in part, on the computed resource requirements of the deterministic transaction.

Abstract: For incident management, parsing, responsive to an incident ticket being opened relative to a first application. The parsing identifying a set of incident data. Identifying, using a dependency graph, a set of applications, wherein each application in the set of applications is dependent on the first application through at least one dependency relationship. Notifying, responsive to the incident ticket, a subset of a set of users of a second application about the incident ticket related to the first application, the second application being a member of the set of applications, the subset of the set of users of the second application performing a type of transaction with the second application wherein the type of transaction is indicated in a dependency relationship between the first application and the second application. Preventing a user in the subset of users from creating a second incident ticket.

Abstract: For incident management, parsing, responsive to an incident ticket being opened relative to a first application. The parsing identifying a set of incident data. Identifying, using a dependency graph, a set of applications, wherein each application in the set of applications is dependent on the first application through at least one dependency relationship. Notifying, responsive to the incident ticket, a subset of a set of users of a second application about the incident ticket related to the first application, the second application being a member of the set of applications, the subset of the set of users of the second application performing a type of transaction with the second application wherein the type of transaction is indicated in a dependency relationship between the first application and the second application. Preventing a user in the subset of users from creating a second incident ticket.

Abstract: An approach is provided for automating an adoption of cloud services. A machine learning model is devAppliceloped to learn application source code accessed from a source code repository. The machine learning model is trained by building training data that includes the application source code and technical labels. Services are extracted from the application source code and a context of the application source code is derived. Using a decision tree model, a mapping of the extracted services to respective cloud services is determined. Based on the mapping, portions of the application source code are identified that are recommended to be replaced by the respective cloud services.

Abstract: Predictive scaling of containers is provided based on obtaining, by one or more processors, prior transaction data on one or more of a user's prior transactions on one or more transaction-centric applications, and analyzing by the processor(s), the prior transaction data to predict whether a current transaction of the user on an application is deterministic or non-deterministic as to one or more functional layers of multiple functional layers of the application. Based on predicting that the current transaction is deterministic as to the functional layer(s) of the application, the processor(s) determines compute resource requirements for the deterministic transaction, and launches one or more containers in advance to run the functional layer(s) based, at least in part, on the computed resource requirements of the deterministic transaction.