International Business Machines Corporation

Globalfoundries Inc.

Ashok Pon Kumar Sree Prakash

Vijay Ekambaram

Malolan Chetlur

Amith Singhee

Sharad C Sundararajan

Vikas V Joshi

Padmanabha Venkatagiri Seshadri

Mohit Jain

Pratyush Kumar

Sarbajit Kumar Rakshit

Srikanth Govindaraj Tamilselvam

Praveen Jayachandran

Krishnasuri Narayanam

Yogish Sabharwal

Ashish Kumar Mathur

Sumanta Mukherjee

Karthikeyan Dakshinamurthy

Venkatesan T Chakaravarthy

Surya Shravan Kumar Sajja

Akash Nayak

Harikrishnan Balagopal

John Michael Ganci, Jr

Gandhi Sivakumar

Kuntal Dey

Arun Kumar

Saravanan Sadacharam

Ulrich Alfons Finkler

Scott Marshall Mansfield

Smitkumar Narotambhai Marvaniya

Vikrant Nandakumar

Venkatraman Ramakrishna

David Osmond Melville

Umamaheswari C Devi

Giriprasad Sridhara

Raghavendra Singh

Abhishek Malvankar

Chander Govindarajan

Harshit Kumar

Vikas Raykar

Sambaran Bandyopadhyay

Ramachandra Kota

Satyam Dwivedi

Steven N Hirsch

Saritha Vinod

Zainul Mohammed Charbiwala

Ayushi Dalmia

Pablo Salvador Loyola Heufemann

Megha Nawhal

Pravein Govindan Kannan

Digbalay Bose

Anshul Bhavesh Shah

Gyanendra Sharma

Nisha Goswami

Aditya Dwivedi

Meenakshi Ravisankar

Shachee Mishra Gupta

Methods, systems, and computer program products for cold-start service placement over on-demand resources are provided herein. A computer-implemented method includes obtaining a performance requirement profile comprising performance requirements of a service that vary over time; determining a plurality of incarnations for the service, wherein each incarnation is associated with a level of performance provided by the incarnation for the service, resource requirements of the incarnation, and a type of computing node the incarnation is configured to execute on; identifying computing nodes having different types and different resource capacities; jointly scheduling (i) the computing nodes and (ii) one or more of the incarnations on the computing nodes over a time interval such that a cumulative level of performance of the incarnations scheduled at each timepoint in the time interval satisfies the performance requirement profile of the service.

Cold-start service placement over on-demand resources

Using interaction profiling data and deployment state data of a software application, an observability module configuration plan is generated, the configuration plan comprising an event to be observed and an observability parameter. Using infrastructure state estimation data of the infrastructure on which the software application is executing, the observability module configuration plan is adjusted, the adjusting resulting in an adjusted reconfiguration plan, the adjusted reconfiguration plan comprising a second event to be observed, the second event associated with a node failure within the infrastructure. A set of observability modules is reconfigured according to the adjusted reconfiguration plan, the reconfiguring comprising causing an observability module in the set of observability modules to be activated to monitor the event.

Adaptive observability module reconfiguration

A computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by one or more processors to cause the one or more processors to construct an interaction graph based in part on a distributed application of a cloud platform comprised of a plurality of services; extract one or more sub-graphs from the interaction graph by mining the interaction graph using one or more feasibility validation tests; estimate a resource pressure metric for the one or more sub-graphs, wherein the resource pressure metric is at least one of a prospective resource pressure metric and a retrospective resource pressure metric, the resource pressure metric being associated with each service on one or more respective dependent services; construct a degradation plan for a least-cost sub-graph in accordance with the estimated resource pressure metric; and apply the degradation plan to the distributed application and the cloud platform.

Degradation planning and mitigation in hybrid cloud environments

Container image optimization is provided for a deployment environment. An indication of a plurality of container images to be deployed in a deployment to provide a plurality of services within a computing environment is obtained. The plurality of container images of the deployment has a total layer size of a plurality of image layers of the plurality of container images. At least one container image of the plurality of container images is reconfigured to adjust the total layer size for the deployment. The reconfiguring is based on one or more deployment characteristics of the deployment and includes replacing at least one image layer of a container image of the at least one container image with one or more other image layers configured based on the plurality of container images to be deployed.

Container image optimization for a selected deployment environment

A processor may receive a route request from a user. The processor may receive data associated with predicted routes of one or more nearby vehicles. The processor may select a recipient vehicle from the one or more nearby vehicles, where the recipient vehicle is selected based on recipient selection criteria. The processor may determine one or more driving change requests for the recipient vehicle. The processor may send the one or more driving change requests and a proposed token to the recipient vehicle. The processor may provide a token to the recipient vehicle in response to the recipient vehicle implementing the driving change requested.

Cooperative operation of vehicles

Described are techniques for shared library customization. The techniques include generating a customized library for a container image associated with an application and a shared library. The customized library includes a subset of the shared library with functionalities utilized by the application retained in the customized library and functionalities not utilized by the application removed from the customized library. The techniques further include executing the application on a deployed container image, where the application utilizes the customized library.

Shared library customization

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

An artificial intelligence (AI) platform to support optimization of workload scheduling in a distributed computing environment. Unstructured data corresponding to one or more application artifacts related to a workload in the distributed computing environment is leveraged. NLP is applied to the unstructured data to identify one or more host requirements corresponding to the application artifacts. One or more hosts in the computing environment compatible with the identified host requirements are selectively identified and compatibility between the application artifacts and the identified hosts is assessed. The workload is selectively scheduled responsive to the selective host identification based on the assessed compatibility. The scheduled workload is selectively executed on at least one of the selectively identified hosts responsive to the assessment workload compatibility.

Optimization of workload scheduling in a distributed shared resource environment

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 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

A system, method, and computer program product for implementing software modernization and refactoring is provided. The method includes analyzing source code. In response, components and associated interconnections of the source code are identified and a runtime associated with a software application is analyzed. Likewise, components and associated interconnections of the runtime are identified and architectural data is analyzed with respect to the source code and runtime. In response, a software and hardware model associated with operation of the server and software application is generated and the software and hardware model is correlated with results of analyzing the architectural data, source code, and runtime. A dashboard graphical user interface and refactoring model code associated with a modernization and refactoring process configured to generate refactored code are generated and the refactoring model code is executed. In response, refactored code of the software application is generated thereby operationally modifying the software application.

Software application refactoring and modification

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

In an approach for an indoor survey data collection, a processor generates reference points based on a site map and an accuracy requirement. A processor collects data at each reference point through a data collecting agent. A processor detects an outlier at the reference points using a feedback from the data collecting agent during the data collection and a database. A processor eliminates the detected outlier and rectifies the data.

Indoor survey data collection

One embodiment provides a computer implemented method, including: receiving an application including a plurality of services to be hosted on a remote services environment; obtaining, a resource profile identifying a usage of a resource by a given service of the plurality of services over a period of time; splitting, based upon the resource profile corresponding to a given service, the given service into a plurality of service slices; selecting, for each of the plurality of service slices an incarnation fulfilling a resource demand requirement and a service-performance-offering, wherein an incarnation has a total demand value based upon a resource capacity of a node at the remote services environment; and assigning, for each of the plurality of service slices the incarnation selected to at least one node within the remote services environment based upon the resource capacity of the at least one node.

Incarnation selection for application hosting on remote services environment

Growing community of inventors

Bengaluru, India

Ashok Pon Kumar Sree Prakash