International Business Machines Corporation

Kyndryl, Inc.

Padmanabha Venkatagiri Seshadri

Vijay Ekambaram

Vijay Kumar Ananthapur Bache

Srikanth K Murali

Saravanan Sadacharam

Ashok Pon Kumar Sree Prakash

Bidhu Ranjan Sahoo

Satyam Dwivedi

Shikhar Kwatra

Amith Singhee

Kedar Kulkarni

Aparna Seshadri

Shinoj Zacharias

Smitkumar Narotambhai Marvaniya

Sumanta Mukherjee

Gandhi Sivakumar

Arvind Kumar

Krishnasuri Narayanam

Jhilam Bera

Caleb Miles

Nupur Aggarwal

Akash Nayak

Harikrishnan Balagopal

Kuntal Dey

Satyanarayana Venkata Nitta

Nitendra Rajput

Timo Kussmaul

Arun Kumar

Ravindranath Kokku

Praveen Jayachandran

Pankaj Satyanarayan Dayama

Uwe Karl Hansmann

Rama Kalyani T Akkiraju

Adam Lee Griffin

Prasenjit Dey

Chalapathy Venkata Neti

Bikram Sengupta

Yogish Sabharwal

Giriprasad Sridhara

Atul Mene

Pooja Aggarwal

Mudit Verma

Jennifer L Szkatulski

Nitin Singh

Samiulla Zakir Hussain Shaikh

Abhishek Bansal

Pablo Salvador Loyola Heufemann

Aditya Sinha

Rengia Ramaiyan Vasudevan

Venkatesan T Chakaravarthy

Anand Shantilal Borse

Vijay Kumar Anantapur Bache

Pravein Govindan Kannan

Thupale Sreedharan

Rajkumar Paka

Bhagyashree Jayaram

Swathi E P

Arun Nagarajan

Amit Kaktwan

Aditya Dwivedi

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

A system includes: a platform; a payload extending from a platform; an adjustable tether connecting the payload to the platform; and a sail extending from the payload. The system further includes a computer program that is executable to relocate the platform to the area of interest based on a threshold based decision which may include providing a tether and sail configuration.

Relocation based coverage area enhancement system

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 method of replicating the volume of stateful-sets by receiving input request types for each container of a number of containers, extracting control flow paths and mapping the input request types to the extracted control flow paths. The method learns control flow path-to-volume mappings by computing, for each control flow path of the number of control flow paths, (i) one or more associations between the control flow path and one or more volumes and (ii) a nature of access of the one or more volumes. For each pod replica a dominant control flow path is computed by computing a most common control flow path and a most common volume corresponding to the most common control flow path. A request for volume management of an identified most common volume is received and the identified most common volume is replicated or a its view is created.

Volume replication of stateful-sets

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: iteratively examining logging data; detecting multiple faults in a computer environment in dependence on the examining of the logging data; generating for respective ones of the detected multiple faults one or more candidate remediation to provide a set of candidate remediations for the computer environment; prioritizing remediations defining the set of candidate remediations from the generating and ordering the remediations in a remediation queue according to an order of the prioritizing; and deploying remediations according to the ordering of remediations in the remediation queue.

Prioritized fault remediation

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

Debris signature-based robotic cleaning device navigation includes operating the robotic cleaning device in a first mode as part of a vacuum cycle, the device including suction ports configurable for different suction power levels and each port having a suction path along which debris entering through the suction port is collected by the device. In the first operating mode the suction ports are operated at a first suction power level. The navigation also includes changing operation of the device to a second mode and in which the suction ports are operated at a greater suction power, measuring a respective amount of debris collected through each suction port, and selecting a direction in which to navigate the robotic cleaning device based on the debris measurements.

Debris signature-based robotic cleaning device navigation

Approaches presented herein enable maneuvering collaborative robots to rescue persons in a hydrological disaster. A plurality of robots are dispersed in a body of water to spread out and seek victims using cooperative foraging techniques within resource constraints. A location of victims located by a robot using sensing techniques is communicated to other robots. A situational assessment is performed using victim location information to determine a number of robots to deploy to the location. The deployed robots are directed to perform coordinated maneuvers to create a connected floatation unit to support floatation of victims for rescue.

Maneuvering collaborative devices for a rescue mission

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

Embodiments provide a computer-implemented method for rendering advertisements in a Mixed Reality space, including: profiling a plurality of users in a smart home sharing the MR space; building a social network graph between the plurality of users; building a user embedding matrix based on the social network graph; recommending a plurality of first advertisements for one or more target users; building a joint embedding matrix across the plurality of users and the plurality of first advertisements; identifying a particular position and a particular time period for the one or more target user, while the remaining users are not viewing the particular position during the particular time period; selecting one or more advertisements from the plurality of first advertisements based on the joint embedding matrix; and rendering the one or more advertisements to the one or more target users at the particular position during the particular time period.

Rendering privacy aware advertisements in mixed reality space

One embodiment provides a method, including: accessing, at an information handling device, a dynamic visual media corpus, wherein the dynamic visual media corpus comprises a plurality of dynamic visual media scripts; segmenting each of the plurality of dynamic visual media scripts into scenes; generating, for each of the plurality of dynamic visual media scripts, a character fingerprint identifying topics corresponding to each character within a corresponding dynamic visual media script, wherein the generating comprises (i) extracting both characters and topics from the dynamic visual media script and (ii) associating each of the topics with a corresponding character, wherein the character fingerprint identifies costumes of a given character and a topic corresponding to each costume; and producing, for each scene within each dynamic visual media script, a scene vector identifying (iii) the topics included within a corresponding scene and (iv) a character fingerprint for each character occurring within the scene.

Automated costume design from dynamic visual media

A method for managing the composition and presentation sequencing of compound visual elements, the method including generating a graph of all possible compound visual element combinations, generating a set of possible visual element presentation sequences according to a depth-first search (DFS) of the graph, generating a score for each member of the set of possible visual element presentation sequences according to visual element attributes, and enumerating a visual element presentation sequence according to a visual element presentation sequence score.

Managing the selection and presentation sequence of visual elements

An approach is provided to manage in-flight drones. The approach identifies a drone at a drone charging station with the identified drone being unauthorized to be at the drone charging station. Responsively, the approach then secures the identified drone and removes the identified drone from the drone charging station.

Managing and protecting charging stations for drones

An application (App)-BOT, scans a context of a user of an application and obtains current scanned user data. The App BOT determines a set of user information entities from the scanned user data containing current information about the user. In a negotiation phase, the App BOT a data access request offer from a mixed reality data (MRD)-BOT. A privacy leak score is estimated that represents a user value attributed to the permission to access the labelled user information entity based on the data access request offer. Responsive to determining that the privacy leak score exceeds a privacy leak score threshold, an acceptance of the data access request offer is sent to the MRD BOT and providing access requested by the data access request. Otherwise, a counteroffer is sent to the offer to the MRD BOT.

Privacy negotiations in a mixed reality environment

Growing community of inventors

Mysore, India

Padmanabha Venkatagiri Seshadri