International Business Machines Corporation

Venkata Vinay Kumar Parisa

Biswajit Mohapatra

Birgit Monika Pfitzmann

Amitkumar Manoharrao Paradkar

Matthew A Markley

Yunwu Huang

John K Baker

Moises I Galvan

Prasanta Kumar Pal

Bruce G Taylor

Shweta Jain

Corey Allen

Debasis Roy Choudhuri

Indrajit Bhattacharya

Laurence Swift

Methods, computer program products, and/or systems are provided that perform the following operations: identifying an application marker for a source application; mapping the application marker to a set of micro-patterns provided in a micro-pattern repository, wherein a micro-pattern defines a set of actions to be performed to modernize a source application component for a target platform; generating a set of potential modernization pathways for the source application, wherein a potential modernization pathway is based, at least in part, on an aggregation of one or more micro-patterns included in the set of micro-patterns mapped to the application marker; determining a recommended modernization pathway from the set of potential modernization pathways based, at least in part, on micro-pattern optimization; and providing the recommended modernization pathway for source application modernization execution, wherein the source application modernization execution includes executing each micro-pattern included in the recommended modernization pathway.

Micro-pattern based application modernization assessment

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.

Data analysis for predictive scaling of container(s) based on prior user transaction(s)

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.

Incident management for triaging service disruptions

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.

Automating an adoption of cloud services

Methods and systems for finding a migration target include building a constrained, layered graph from a catalog of possible configuration combinations and a distance function for each layer in the graph; and determining an available path through the graph, said path having a minimum overall distance from a source instance, by performing an optimized search that selectively evaluates available paths through the graph based on cumulative distances of paths through the graph from the source instance.

Efficient configuration combination selection in migration

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Secunderabad, India

Venkata Vinay Kumar Parisa