International Business Machines Corporation

John Joseph Rofrano

Maja Vukovic

Anup Kalia

Jinho Hwang

Jin Xiao

Constantin Mircea Adam

Brian L Peterson

Chen Lin

Shang Qing Guo

Sai Zeng

Saurabh Sinha

Raghav Batta

Jie Ma

Birgit Monika Pfitzmann

Ya Bin Dang

Frederick Y Wu

Christopher Charles Young

Yaoping Ruan

Nikolaos Anerousis

Xiaolan Zhang

Florian Graf

Lijun Mei

Kristiann J Schultz

Hongtan Sun

Milton A Bonilla

Malik Jackson

David Kohen

David Louis Kaminsky

Marco Aurelio Stelmar Netto

David Mark Ogle

Jeffrey Alan Frey

Christopher Ward

Elizabeth Daly

Li Jun Mei

Asit Dan

Muhammed Fatih Bulut

Milton H Hernandez

Oznur Alkan

Shripad Nadgowda

Kun Bai

Michael D Williams

Marcos Dias De Assuncao

Andrew Neil Trossman

Senthil Kumar Kumarasamy Mani

Lakshminarayanan Renganarayana

James R Doran

Kamal Bhattacharya

Frederick Yung-Fung Wu

John Elliott Arwe

Kunwadee Sripanidkulchai

Mihir Choudhury

Hendrik Wagner

Frederick Y-f Wu

James R Barros

Lambert Pouguem Wassi

An approach to optimized migration of user assets to the cloud using artificial intelligence is presented. This approach may include user input and artificial intelligence trained with historical knowledge to generate rules. Migration models may be generated from the rules. A user may verify the migration models were successful. A task portfolio may be generated from the verified wave migration models. Runbook applications may be generated from the task portfolio and the migration may be executed using the runbooks.

Artificial intelligence optimized cloud migration

Systems, computer-implemented methods, and computer program products to facilitate generation of microservices from a monolithic application based on runtime traces are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a model component that learns cluster assignments of classes in a monolithic application based on runtime traces of executed test cases. The computer executable components can further comprise a cluster component that employs the model component to generate clusters of the classes based on the cluster assignments to identify one or more microservices of the monolithic application.

Generation of microservices from a monolithic application based on runtime traces

Techniques regarding discovering configuration information for one or more computer applications are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a configuration component that can discover configuration information associated with a containerized computer application. The configuration information can be characterized by a set of environment attributes extracted by querying a source code of the containerized computer application.

Configuration discovery of computer applications

Systems and techniques that facilitate compliance enforcement via service discovery analytics are provided. In various embodiments, a system can comprise a receiver component that can access one or more declarative deployment manifests associated with a computing application. In various instances, the system can comprise a dependency component that can build a dependency topology based on the one or more declarative deployment manifests. In various cases, the dependency topology can indicate dependencies among one or more computing objects that are declared by the one or more declarative deployment manifests. In various aspects, the system can comprise a compliance component that can determine, based on the dependency topology, whether the computing application satisfies one or more compliance standards.

Compliance enforcement via service discovery analytics

Technology for dividing a monolith computer program into a plurality of functions and creating a microservice for each function of the model's computer program. The micro-services are semantically and functionally equivalent, taken collectively, to the model's computer program from which they came. In some embodiments, the monolith computer program is written in an object oriented programming language suitable for execution by a virtual machine (VM). In some embodiments, the microservices are in representational state transfer (REST) form and respectively corresponding application programming interface (API).

Generation of equivalent microservices to replace existing object-oriented application

Systems, computer-implemented methods, and computer program products that can facilitate computing environment migration plan recommendation based on one or more latent entity computing property preferences are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that employs a model to discover a latent computing property preference of an entity operating in a first computing environment. The computer executable components can further comprise a recommendation component that recommends a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity. In some embodiments, the recommendation component recommends discovered latent computing property preferences of the entity to construct the computing environment migration plan.

Latent computing property preference discovery and computing environment migration plan recommendation

A system, program product, and method for use with an artificial intelligence (AI) platform to dynamically amend a knowledge base responsive to query evaluating and processing. A received or detected query is subject to natural language processing to identify, annotate, and map one or more query tokens against a knowledge base. The query tokens are evaluated against the knowledge base to identify one or more query tokens absent from the knowledge base and leverage a neural network to predict a probability relationship between the query tokens absent from the knowledge base and one or more tokens populated in the knowledge base. The natural language (NL) query is translated to a structured query language (SQL) and the SQL query is executed and evaluated, and the knowledge base is selectively and dynamically amended subject to the SQL evaluation.

Automated validity evaluation for dynamic amendment

Embodiments relate to a system, program product, and method for training a contrastive neural network (CNN) in an active learning environment. A neural network is pre-trained with labeled data of a historical (first) dataset. The CNN is trained for a new (second) dataset by applying the new dataset and contrasting the new dataset against the historical dataset to extract novel patterns. Weights of a knowledge operator from the pre-trained neural network are borrowed. Features novel to the new dataset are learned, including updating weights of the knowledge operator. The borrowed knowledge operator weights are combined with the updated knowledge operator weights. The CNN is leveraged to predict one or more labels for the new dataset as output data.

Contrastive neural network training in an active learning environment

Systems and techniques that facilitate automated recommendation of microservice decomposition strategies for monolithic applications are provided. In various embodiments, a community detection component can detect a disjoint code cluster in a monolithic application based on a code property graph characterizing the monolithic application. In various aspects, the code property graph can be based on a temporal code evolution of the monolithic application. In various embodiments, a topic modeling component can identify a functional purpose of the disjoint code cluster based on a business document corpus corresponding to the monolithic application. In various embodiments, a microservices component can recommend a microservice to replace the disjoint code cluster based on the functional purpose.

Microservice decomposition strategy of monolithic applications

A method, system, and computer program product for decomposing monolithic applications to form microservices are provided. The method identifies a set of classes within a monolithic application. A set of horizontal clusters are generated by performing horizontal clustering to the set of classes to decompose the classes based on a first functionality type. The method generates a set of vertical clusters by performing vertical clustering to the set of classes to decompose the classes based on a second functionality type. A subset of classes occurring in a common horizontal cluster and vertical cluster are identified as a functional unit. The method merges one or more functional units to form a microservice.

Forming microservices from monolithic applications

A system can include a memory that stores computer-executable components and a processor, operably coupled to the memory, that can execute the computer-executable components stored in the memory. The computer-executable components can include a model generator component that can execute a search function on an application based on a set of application component topics to identify application components of the application that facilitate execution of the application. A configuration property extractor component can analyze the application based on the application components and can determine configuration properties associated with operating the application components. An application builder component can process the application components and the configuration properties and can generate instructions that facilitate execution of the application in a target computing environment.

Automated transformation of applications to a target computing environment

Systems, computer-implemented methods, and computer program products to facilitate transformation of an enterprise application into a cloud native container are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that employs a model to discover attributes of an enterprise application. The computer executable components can further comprise a transformation component that generates artifacts based on the attributes to transform the enterprise application into a cloud native container.

Transformation of an enterprise application into a cloud native application

A compliance of a system is managed. Internal parameters of the system are received. One or more compliance control documents that relate to the system are identified. Each compliance control document is decomposed into one or more auditable units. Auditable units that are relevant to the system, are identified. Relevant auditable units are mapped to a corresponding available executable code. Each mapped executable code is executed. A risk associated with each relevant auditable unit that is deemed to be not compliant is identified. For each compliance control document, a compliance risk is calculated based on the identified risk. A report of compliance risk for each document is created to be displayed on a user interface of a computing device.

Automated fixups based on partial goal satisfaction

Growing community of inventors

Mahopac, NY, United States of America

John Joseph Rofrano