Abstract: In an approach for optimizing job preemption using a scheduling mechanism that considers dynamic and static information, a processor, responsive to a pending job being selected to be run, determines that no computing hosts within a computing cluster have available resources capable of running the pending job and non-preemptive measures will not provide required resources for the pending job. A processor identifies candidate jobs for preemption and dynamic information and static information for each candidate job. A processor ranks the candidate jobs for preemption based on the dynamic information and the static information for each candidate job. A processor attempts to preempt a top N candidate jobs whose released resources in combination with the available resources of the computing cluster satisfy the required resources of the pending job. A processor, responsive to successfully preempting the top N candidate jobs, initiates the pending job.

Abstract: Some embodiments of the present disclosure are directed to systems, computer-readable media, and computer-implemented methods for dynamic computing resource management. Some embodiments are directed to identifying a computing resource common between a first workload and a second workload, replacing a label associated with the first workload on the identified computing resource with a label associated with the second workload, executing the second workload using the identified computing resource. Other embodiments may be disclosed or claimed.

Abstract: Embodiments of the present invention provide an approach for optimizing usage and providing recommendations to users in a hybrid cloud environment. Specifically, user configuration data and deadline for job execution for a job to be executed is collected. A broker queries available queues to determine a wait time corresponding to each of the available queues for the job based on the user configuration data. The wait times are compared to the deadline for job execution. If the deadline cannot be met, a machine learning module suggests modifications to the user configuration to reduce wait times and meet the deadline.

Abstract: A computer-implemented method for labeling and managing cloud computing resources includes receiving one or more computing jobs in a job queue and obtaining resource requirements for a first one of the one or more computing jobs. Nodes are placed into a cluster for the resource requirements from one or more cloud providers and the nodes are labelled to correspond to the first one of the one or more computing jobs. The first one of the one or more computing jobs from the job queue and is executed after the labelled aggregated resources are ready.

Abstract: A computer-implemented method, a computer system and a computer program product display a projection of driving risk to a vehicle from activity in the surrounding area. The method includes acquiring a vehicle path from the vehicle. The method also includes capturing driving conditions from the surrounding area using a sensor and recognizing an object in the surrounding area that is transmitting relevant data. The method further includes identifying a high-risk object in the surrounding area by calculating a risk score for the object relative to the vehicle based on the vehicle path and intended movements of the object and classifying the object as the high-risk object when the risk score is above a risk threshold for the vehicle. Lastly, the method includes generating an augmented reality display of the surrounding area using an augmented reality device, wherein the augmented reality display of the surrounding area indicates the high-risk object.

Abstract: A plurality of ledger nodes within a blockchain infrastructure is continuously updated via a set of conduit nodes. The set of conduit nodes share a secure provision ledger. The secure provision ledger includes a set of updates for the blockchain infrastructure. Each conduit node within the set propagates the set of updates to a subset of the plurality of ledger nodes.

Abstract: A computer-implemented method of determining installation compatibility includes identifying one or more entities of an uninstalled operator. The identified one or more entities of the uninstalled operator are parsed and information is extracted from the one or more entities. An existing operator installed on a target container cluster is parsed and information extracted from the entities of the existing operator. The extracted information from the uninstalled operator is compared with the extracted information from the existing operator. A disruption risk to operation of the target container cluster is ranked based on the comparing of the extracted information of the uninstalled operator with the extracted information of the existing operation.

Abstract: 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.

Abstract: Disclosed embodiments provide techniques for compute job allocation in a virtualized computing environment. A first list of compute jobs that are currently executing in a virtualized environment is obtained. For each job in the first list, a job description file is obtained. An entity extraction process is performed on the job description file to extract a plurality of job entities. Multiple clusters are created that correspond to the compute jobs in the first list. A second list of compute jobs that are currently queued for execution is obtained. Compute jobs in the second list are assigned to a cluster from the plurality of clusters, and the virtualized environment is reused for execution of a compute job from the second list based on the assigned cluster.

Abstract: Mechanisms are provided for improving performance of container images. Container image chunks are generated from a container image file and input into one or more trained machine learning (ML) computer models, trained to classify container image chunks with regard to a plurality of container image performance characteristic classifications. For each container image chunk it is determined whether the a corresponding classification is negative, and in response to the classification being negative, an entry in a knowledge base having patterns of content matching content in the container image chunk is identified to determine one or more reasons for modification of the chunk specified in the entry. A notification output is generated specifying the container image chunks, their corresponding container image performance characteristic classifications, and the reasons for modification of the chunks.

Abstract: A computer-implemented method for configuring a blockchain network, a computer program product for integrating device identity into a permissioning framework of a blockchain network, and a blockchain network. One embodiment may comprise registering a device at a delineate node of a blockchain network, creating, by a processor of the delineate node based on the registering, a profile for the device; an performing, by the processor of the delineate node, a pass-through service for the device. The registering may include receiving, by a network interface, an immutable device identity from the device.

Abstract: 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.

Abstract: An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

Abstract: An example operation may include one or more of: receive a first set of annotation information, determine a first approval decision for the first set of annotation information, record the first approval decision in a blockchain, retrieve a first set of approval decisions from the blockchain, generate a consensus based on the first set of approval decisions, and record the consensus in the blockchain.

Abstract: Aspects of the present disclosure relate to generating and visualizing subgroups of participants within web-based conferences. Participant data for each of a plurality of participants attending a web-based conference can be obtained. The participant data can be analyzed for each of the plurality of participants. Each of the participants can be mapped to a subgroup of two or more subgroups based on the analysis. The two or more subgroups can then be visualized within the web-based conference to the participants.

Abstract: An embodiment includes tokenizing a code segment generated by an integrated development environment responsive to user input, the tokenizing resulting in a tokenized code segment. The embodiment generates a search query by analyzing the tokenized code segment using a trained neural network. The embodiment executes a querying process that searches a knowledge base using the search query for a hardware requirement associated with the code segment, and detects a search result that conveys the hardware requirement as including a specialized hardware resource. The embodiment generates a time allotment associated with execution of the code segment using a second trained neural network that predicts the time allotment based on the tokenized code segment. The embodiment issues an instruction associated with the code segment to a hardware scheduler causing the hardware scheduler to allocate the specialized hardware resource for an amount of time corresponding to the time allotment.

Abstract: Aspects of the present disclosure relate to generating and visualizing subgroups of participants within web-based conferences. Participant data for each of a plurality of participants attending a web-based conference can be obtained. The participant data can be analyzed for each of the plurality of participants. Each of the participants can be mapped to a subgroup of two or more subgroups based on the analysis. The two or more subgroups can then be visualized within the web-based conference to the participants.

Abstract: An artificial intelligence (AI) platform to support selective replacement of one or more image layers of a container image build. A metadata file is subject to natural language processing and one or more corresponding vector representations are created and subject to evaluation by a set of artificial neural networks (ANNs). A first ANN assesses each vector representation with respect to compliance and operability, and the second ANN selectively assesses the vector representation(s) with respect to similarity with one or more compliant vector representations. In response to the assignment of the second score, at least one vector representation of the received metadata file is selectively replaced with at least one compliant vector representation. The metadata file is selectively provisioned with the selectively replaced vector representation(s).

Abstract: A processor may run a background process to identify a first task being initiated by a first user on a device, where the first task is associated with a first application. The processor may identify the first user of the device. The processor may analyze one or more interactions of the first user associated with the first application on the device. The processor may allocate, based at least in part on identification of the first user, identification of the first task, or analysis of the one or more interactions of the first user, computing resources to one or more hardware components on the device.

Abstract: Embodiments are provided for intelligent recommendations for program code. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an evaluation component that determines that first program code pertains to a defined category representing a defined cost to execute the first program code by a cloud computing service. The computer-executable components also can include a recommendation component that generates a recommendation for second program code that satisfies a similarity criterion with respect to the first program code. The second program code pertains to a category representing a cost to execute the second program code by the cloud computing service, where the cost is less than the defined cost.