International Business Machines Corporation

Utopus Insights, Inc.

Carnegie Mellon University

Globalfoundries Inc.

Cadence Design Systems, Inc.

Utopous Insights, Inc.

Amith Singhee

Fook-Luen Heng

Ashok Pon Kumar Sree Prakash

Ulrich Alfons Finkler

Steven N Hirsch

Mark Alan Lavin

Jun Mei Qu

Wei Wu

Srikanth Govindaraj Tamilselvam

Zhiguo Li

Stuart A Siegel

Haijing Wang

Padmanabha Venkatagiri Seshadri

Aditya Bansal

Krishnasuri Narayanam

Emrah Acar

Jagabondhu Hazra

Umamaheswari C Devi

Kedar Kulkarni

Rob A Rutenbar

Sumanta Mukherjee

Alexey Y Lvov

Shivali Agarwal

Satyam Dwivedi

Surya Shravan Kumar Sajja

Vini Kanvar

Nicholas Ettlinger

Jacob T Griffith

Benjamin Grisz

Carl John Radens

Vijay Ekambaram

Dureseti Chidambarrao

Kuntal Dey

Sarvesh S Patel

Kushal S Patel

Ying Liu

Peter Anton Habitz

Sameep Mehta

Arun Kumar

Pankaj Satyanarayan Dayama

Praveen Jayachandran

Stanislav Polonsky

Jia Qi Li

Fan Jing Meng

Ashok Kumar

Rama Nand Singh

Scott Marshall Mansfield

Mathieu Sinn

Mohit Jain

Venkatraman Ramakrishna

Himanshu Gupta

Pratyush Kumar

Ibrahim M Elfadel

Hongzhou Liu

David Osmond Melville

Akshar Kaul

Wangyang Zhang

Yogish Sabharwal

Liang-Teck Pang

Giriprasad Sridhara

Raghavendra Singh

Devasenapathi Periagraharam Seetharamakrishnan

Harshit Kumar

Chander Govindarajan

Vikas Raykar

Amar Prakash Azad

Nitin Singh

David Scott Wenk

Amol Anil Joshi

Kaushik Das

Chumki Basu

Rodney Phelps

Utkarsh Milind Desai

Saravanan Krishnan

Ayushi Dalmia

Vincent Lonij

Vaibhav S Dantale

Pablo Salvador Loyola Heufemann

Zhi Shuai Han

Francisco Barahona

James E Sundquist

Pamela Castalino

Sonia Singhal

Dallas M Lea

Venkatesan T Chakaravarthy

Megha Nawhal

Akash Nayak

Harikrishnan Balagopal

Rahamim Katan

Divakar R Mysore

Anshul Bhavesh Shah

Gyanendra Sharma

Joao P M Goncalves

James D Ma

Claire F Fang

Aditya Dwivedi

Meenakshi Ravisankar

Raunak Sinha

Ridhi Jain

Radhika Vaddarse

A method, system, and computer program product to manage a network comprising a plurality of interconnected components are described. The method includes obtaining a set of all the components that are part of the network over time, and identifying one or more repeating patterns of components among the set of all the components as corresponding lower-level definitions to generate a hierarchical set of all the components. The method also includes obtaining time-varying information regarding topology and operational values within the network, and creating a representation of the network at a set of times based on the hierarchical set of all the components and the time-varying information.

Network management using hierarchical and multi-scenario graphs

Described are techniques for exposing an endpoint for a software function used by microservices. The techniques include obtaining software classes of a plurality of microservices and analyzing the software classes to identify a software function that is eligible for conversion to an application programming interface (API) format. The techniques further include determining a server microservice, which hosts a software class that includes the software function, and one or more client microservices that use the software function. The techniques further include generating deployable source code implementing an API that exposes the software function hosted by the server microservice to the one or more client microservices. The API uses a software wrapper to encapsulate software function semantics used by a monolith application refactored into the plurality of microservices.

Automated generation of application programming interfaces for microservices

A computer implemented method creates microservices for an application. A number of processor units clusters programs and data structures for the application using runtime metadata to form groups of the programs and data structures. The runtime metadata is obtained from running the application. The number of processor units creates a design for the microservices for the application using the groups of the programs and the data structures.

Microservice creation using runtime metadata

Systems/techniques that facilitate monolith-to-microservice refactoring via source-code-to-domain-model graph comparison are provided. In various embodiments, a system can access source code of a monolithic application and a target domain model corresponding to the monolithic application. In various aspects, the system can refactor the monolithic application into microservices, by aligning a first graph representing the source code with a second graph representing the target domain model.

Monolith-to-microservice refactoring via source-code-to-domain-model graph comparison

A method, computer systems and program product to import non-compliant container images is provided. A processor receives a non-compliant container image, wherein the container image is not compliant with a target computing environment. A processor extracts a one or more embeddings from the non-compliant container image. A processor compares the one or more embeddings from the non-compliant container image to a plurality of one or more embeddings from an image catalog. A processor identifies a similar image from the image catalog based on the comparison of layer embeddings from the non-compliant container image and the similar image. A processor deploys the similar image in the target computing environment.

Generating compliant container images via embeddings

Techniques for recommending microservices to perform the different functions of a legacy architecture are disclosed. In one example, a computer implemented method comprises receiving a plurality of recommendations comprising a plurality of program components as candidates for assignment to a plurality of microservices, and determining roles of respective ones of the plurality of program components. A user interface is provided which is configured to allow a user to modify one or more of the plurality of recommendations based at least in part on the roles. Modifications to the one or more of the plurality of recommendations are analyzed, and one or more metrics are computed based at least in part on the analysis.

Microservices recommendation framework

Methods, systems, and computer program products for generating multi-dimensional host specific storage tiering are provided herein. A computer-implemented method includes maintaining information of a plurality of storage resources connected to at least one host device; configuring, based at least in part on the information, a plurality of partitions of the storage resources, each partition comprising multiple storage tiers, wherein the plurality of partitions is configured to provide physical migration paths between the multiple storage tiers within each of the partitions; detecting a change to the plurality of storage resources; and reconfiguring the plurality of partitions based at least in part on the detected change.

Generating multi-dimensional host-specific storage tiering

Methods, systems, and computer program products for identifying microservices from a monolith application through static code analysis are provided herein. A method includes performing a static code analysis to extract multiple features of a monolith application; partitioning code elements of the monolith application into multiple groups using an agglomerative clustering process, wherein the agglomerative clustering process is based on the extracted multiple features and a set of clustering metrics; obtaining at least one weight corresponding to one or more of: at least one of the multiple features and at least one of the multiple groups; adjusting the groups based on the at least one weight; generating a list of candidate microservices for the monolith application, wherein each candidate microservice in the list corresponds to a different one of the adjusted multiple groups; and outputting the list of candidate microservices to at least one of a system and a user.

Identifying microservices for a monolith application through static code analysis

Methods, systems, and computer program products for data filtering with fuzzy attribute association are provided herein. A computer-implemented method includes obtaining one or more rules, specified by an expert, that define a partial ranking of a plurality of fuzzy pairings between (i) a plurality of item attributes for items in a data catalog and (ii) a plurality of user attributes related to said items; generating an interactive session with the expert to resolve one or more ambiguities in the one or more rules; and deriving a scoring function based at least in part on (i) the one or more rules and (ii) the resolved one or more ambiguities, wherein the scoring function generates a comparative score between any two items of said data catalog for a given one of the users associated with the plurality of attributes.

Data filtering with fuzzy attribute association

A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Weather-driven multi-category infrastructure impact forecasting

A method for guided design generation includes receiving a plurality of image samples for an intended design and generating a first plurality of images by providing a positive latent vector to a first layer out of a plurality of layers and a negative latent vector to remaining layers out of the plurality of layers. Responsive to receiving a first image selection, identifying the first layer out of the plurality of layers generated the first image includes a feature of interest. Determining a feature expression variation for the feature of interest in latent space based on the ranking of a first plurality of generated sample images, wherein the first plurality of generated sample images includes the feature of interest. Responsive to receiving a second plurality of images for a plurality of base features, generating an initial design based on the feature of interest and the plurality of base features.

Guided design generation

One embodiment provides a computer implemented method, including: receiving interaction logs of a plurality of services of an application running on a system; generating an interaction graph identifying interactions between at least the at least one of the plurality of services and other of the plurality of services, wherein the identifying comprises identifying a frequency of interactions between services; determining constraints between at least the at least one of the plurality of services and the other of the plurality of services, wherein the constraints identify services that are dependent upon other services of the plurality of services and a compatibility of services with respect to other services of the plurality of services; and creating a remediation plan for updating the at least one of the plurality of services, wherein the remediation plan identifies a time for updating the at least one of the plurality of services.

Service remediation plan generation

One embodiment provides a computer implemented method, including: receiving an application to be deployed on a container-based environment; obtaining a specification for the application including information regarding a portion of the application contained within a given container; extracting portion information corresponding to connections between different portions of the application and interferences between different portions of the application; identifying affinity indicators and anti-affinity indicators for the portions of the application, wherein the affinity indicators are based upon the connections between different portions of the application and wherein the anti-affinity indicators are based upon the interferences between different portions of the application; and constructing affinity rules and anti-affinity rules for the containers by combining the affinity indicators to construct affinity rules and combining the anti-affinity indicators to construct anti-affinity rules.

Automatic generation of affinity and anti-affinity rules

Growing community of inventors

Bengaluru, India

Amith Singhee