International Business Machines Corporation

Red Hat, Inc.

Chen Wang

Huamin Chen

Lingfei Wu

Alaa Salah Youssef

Ali Kanso

Bo Wen

Stefania Costache

Jeremy R Fox

Fang Lu

Dakuo Wang

Malgorzata Steinder

Lei Yu

Abhishek Malvankar

Diana Jeanne Arroyo

Erhan Bilal

Ricardo Noriega De Soto

Yehuda Sadeh-Weinraub

Dennis Keefe

Tonghoon Suk

Marquita May Ellis

Asser Narsreldin Tantawi

A method and system allocating computing resources according to a trained machine learning model that includes receiving a request to accommodate a particular workload that involves a first threshold amount of computing resources implemented by at least one computing cluster. An identified a set of computing clusters, that have computing resources available for allocating from the identified set of computing clusters to the at least one computing cluster to satisfy the first threshold amount of computing resources, is received from a database. A trained deep-reinforcement learning model is applied to generate a policy for allocating the available computing resources from the identified set of computing clusters to the at least one computing cluster. It is verified that the generated policy satisfies a threshold according to one or more predetermined criteria. The computing resources are allocated from the set of computing clusters to the at least one computing cluster.

Allocating computing resources using a machine learning model

Methods and systems for log message aggregation include determining a first similarity distance score for a first incoming message by comparing the first incoming message to one or more stored templates. It is determined that the first incoming message imperfectly matches a matched template of the one or more stored templates, based on the first similarity distance score. A token in the imperfectly matched template is replaced with a wildcard, to reduce the first similarity distance score.

Automatic online log template mining

A software container is obtained for execution on one of a plurality of compute nodes. A processing device schedules the software container on a first compute node of the plurality of compute nodes based on a comparison of a thermal state of the first compute node to a defined threshold.

Scheduler incorporating thermal awareness

A computer-implemented method for visual question generation includes training an alignment module to analyze an image, an answer hint, and a visual hint with respect to the image. A k-nearest neighbors (KNN) graph is constructed by performing an aligned embedding for each region of the image. A node embedding component is generated by using a graph embedding component of the KNN graph. A visual question is generated by sequence decoding each image and graph of the image.

Visual question generation with answer-awareness and region-reference

A computer implemented method synthesizes electroencephalograph signals. A number of processor units creates a training dataset comprising real electroencephalograph signals, speech signals correlating to the real electroencephalograph signals, and a set of human characteristics for the real electroencephalograph signals. The number of processor units trains a generative adversarial network using the training dataset to create a trained generative adversarial network. The trained generative adversarial network generates synthetic electroencephalograph signals in response to receiving new speech signals.

Electroencephalograph signal generation speech in a generative adversarial network

An embodiment for improved authentication of chatbot users for chat history recovery. The embodiment may record a first chat conversation between a first chat service operator and a first user, wherein the first chat conversation includes a first-step authentication and a chat history. The embodiment may detect a request to resume the first chat conversation. The embodiment may, in response to detecting the request to resume the first chat conversation, initiate a recovery process by sending a random-generated token to the first-step authentication. The embodiment may receive verification of the random-generated token. The embodiment may generate, by a large language model, one or more authentication questions based on content of the chat history. The embodiment may authenticate the first user based on one or more correct answers to the one or more authentication questions. The embodiment may resume the first chat conversation including the chat history.

Cross-platform chatbot user authentication for chat history recovery

A request is received over a network. The request is directed to a service provided by an application of a cluster infrastructure. The cluster infrastructure includes the application and a control plane to schedule execution of the application. Responsive to receiving the request, the control plane of the cluster infrastructure is transitioned from a stopped state to an active state. The request is transferred to the cluster infrastructure.

Cluster infrastructure with on-demand activation

It is determined that data stored on a storage device associated with a computing device powered by a power source is to be transmitted to a remote computing device. A power state of the power source is determined. An encoding mechanism is selected from a plurality of different encoding mechanisms based on the power state of the power source. The data is encoded based on the encoding mechanism to generate encoded data. The encoded data is transmitted to the remote computing device.

Energy aware data replication

Computer workloads can be assigned to nodes of a distributed computing environment based on energy consumption modes employed by the nodes. In one example, a system can determine a first tag assigned to a workload. The first tag can indicate a compatibility of the workload with one or more energy consumption modes employable by one or more nodes of a distributed computing environment. The system can also determine a second tag assigned to a node of the distributed computing environment. The second tag can indicate an energy consumption mode employed by the node. The system can then determine a correspondence between the first tag and the second tag indicating that the workload is compatible with the energy consumption mode employed by the node. Based on determining the correspondence between the first tag and the second tag, the system can assign the workload to the node.

Assigning computer workloads to nodes based on energy consumption modes of the nodes

One or more parameters corresponding to a targeted energy characteristic of a computing client device are received. A ratio of types of storage devices allocated to the computing client device is adjusted based on the one or more parameters. A storage volume for the computing client device is provisioned according to the ratio of the types of the storage devices.

Resource configuration based on energy expenditure

Techniques regarding identifying candidate knowledge graph subgraphs in a question answering over knowledge graph task 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 question answering over knowledge graph component that encodes graph structure information of a knowledge graph subgraph and a question graph into neural network embeddings.

Querying knowledge graphs with sub-graph matching networks

Computer-implemented methods for mitigating temperature induced performance variation in a cloud computing system are provided. Aspects include distributing a plurality of microservices among a plurality of compute nodes in a cloud computing system and monitoring a temperature of processors executing each of the plurality of microservices on each of the plurality of compute nodes. Aspects also include calculating a distribution of the temperatures of the processors including a mean temperature and identifying a first group of computing nodes from the plurality of compute nodes having temperatures within a threshold deviation from the mean temperature. Aspects further include controlling an operation of a cooling system of each of the first group of computing nodes and redistributing one or more of the plurality of microservices disposed on a remaining group of computing nodes that are not part of the first group of computing nodes.

Mitigating temperature induced performance variation

Systems, computer-implemented methods, and computer program products that can facilitate performance biased resource scheduling based on runtime performance of a certain workload type on one or more nodes 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 performance component that assigns performance points to different nodes based on execution of one or more workload types. The computer executable components can further comprise a scheduler extender component that modifies a scheduling decision to run a workload type on a node based on the performance points.

Performance biased resource scheduling based on runtime performance

Methods, computer program products, and/or systems are provided that perform the following operations: obtaining data indicative of a node failure; obtaining data associated with nodes and pods started on each node; generating a causation score for each pod associated with a failed node, wherein each pod associated with the failed node is designated as a candidate pod for the node failure; determining pod rescheduling for each candidate pod associated with the failed node based, at least in part, on a pod ranking of the causation score for each pod; and providing the pod rescheduling to a node cluster to restart each pod associated with the failed node.

Identifying harmful containers

Technology for using a bi-directed graph convolutional neural network ('BGCNN') to convert RDF data into natural language text. Some embodiments perform RDF-to-Text generation by learning graph-augmented structural neural encoders, consisting of: (a) bidirected graph-based meta-paths encoder; (b) bidirected graph convolutional networks encoder, and (c) separated attention mechanism for combining encoders and decoder to translate RDF triplets to natural language description.

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Chappaqua, NY, United States of America

Chen Wang