Abstract: One example method includes deploying an application in a distributed computing environment. Telemetry data is collected that corresponds with the deployment of an application. The telemetry data is received by a machine learning model that was trained with test telemetry data to determine whether the deploying is successful or failed. A successful inference results in continued deployment and a fail inference results in a rollback of the application.

Abstract: Techniques are disclosed for processing unit virtualization with scalable over-provisioning in an information processing system. For example, the method accesses a data structure that maps a correspondence between a plurality of virtualized processing units and a plurality of abstracted processing units, wherein the plurality of abstracted processing units are configured to decouple an allocation decision from the plurality of virtualized processing units, and further wherein at least one of the virtualized processing units is mapped to multiple ones of the abstracted processing units. The method allocates one or more virtualized processing units to execute a given application by allocating one or more abstracted processing units identified from the data structure. The method also enables migration of one or more virtualized processing units across the system.

Abstract: Distributing quantum jobs are disclosed. When a quantum processing unit is underutilized or when wait times are long, quantum jobs may be distributed from the job queue of one vendor to another vendor. This improves utilization and reduces wait times.

Abstract: One example method includes receiving a hybrid/classical algorithm, determining a runtime characteristic of the hybrid/classical algorithm, based on the runtime characteristic, checking a memory availability for execution of the hybrid/classical algorithm, when adequate memory is not available to support execution of the hybrid/classical algorithm, modifying a classical/quantum memory fabric to provide enough memory to support execution of the hybrid/classical algorithm, and orchestrating the hybrid classical/quantum algorithm to an execution environment that includes the classical/quantum memory fabric.

Abstract: Global optimization of quantum jobs in a multi-cloud or multi-edge environment is disclosed. The quantum jobs of multiple vendors are consolidated in a telemetry plane. The quantum jobs are evaluated based on user intents, quantum job characteristics, and quantum processing unit characteristics. The quantum jobs are then assigned to the quantum systems of the vendors based on the evaluation.

Abstract: Quantum job prioritization is disclosed. Quantum jobs may be stored as placeholders in a job queue associated with a quantum processing unit. The quantum jobs are prioritized to improve the usage of the quantum processing unit. Prioritizing quantum jobs allows the quantum processing unit to execute quantum jobs in different orders rather than on an application basis. This allows grace periods to be used for executing quantum jobs.

Abstract: One example method includes receiving, by a hybrid classical-quantum computing system, data from a node of a data confidence fabric, processing the data to create processed data, generating one or more confidence scores relating to the processed data, and making the one or more confidence scores and the processed data available to an end user. The hybrid classical-quantum computing system may also be a node of the data confidence fabric and may perform classical and/or quantum computing operations on the data.

Abstract: One example method includes identifying an accelerator service instance associated with a workload, calling a service broker associated with the accelerator service instance to obtain information needed to use the accelerator service instance, receiving an accelerator call, and accelerator job information concerning an accelerator job, from the workload, in response to the accelerator call, spinning up a new process dedicated to the accelerator job, as part of the new process, running the accelerator job using either the accelerator service instance, or a locally available accelerator, and returning data, generated by running the accelerator job, to the workload.

Abstract: Techniques are disclosed for processing unit virtualization with scalable over-provisioning in an information processing system. For example, the method accesses a data structure that maps a correspondence between a plurality of virtualized processing units and a plurality of abstracted processing units, wherein the plurality of abstracted processing units are configured to decouple an allocation decision from the plurality of virtualized processing units, and further wherein at least one of the virtualized processing units is mapped to multiple ones of the abstracted processing units. The method allocates one or more virtualized processing units to execute a given application by allocating one or more abstracted processing units identified from the data structure. The method also enables migration of one or more virtualized processing units across the system.

Abstract: One example method includes receiving job configuration information from a user with a quantum computing job to be performed, receiving quantum computing information from a quantum computing service vendor, generating, based on the quantum computing information, a vendor score for the quantum computing service vendor, and transmitting the vendor score to the user. The quantum computing information received from the quantum computing service vendor may include information about an accuracy of results produced by execution of a quantum circuit or other quantum hardware operated by the quantum computing service vendor.

Abstract: One example method includes evaluating a function invoked by a request that is received at a local classical computing execution environment, and the request also implies performance of a quantum computing function in a quantum computing execution environment, based on an outcome of the evaluating, determining whether or not the function should be run in the local classical computing execution environment, or whether the function should be run in a separate classical computing execution environment, and when the determining indicates that the function should be run in the separate classical computing execution environment, forwarding the request to the separate classical computing environment for execution of the function. The local classical computing execution environment, the separate classical computing execution environment, and the quantum computing execution environment, are respective first, second, and third, tiers of a hybrid computing execution environment.

Abstract: A system for accelerator functions as a service is disclosed. The system may receive a job that includes a computer (CPU) portion and an accelerator portion. When the accelerator portion is performed, an execution time associated with a time for the accelerator to return results is determined. Resources or a portion thereof allocated to the job, or the CPU portion are freed or reallocated to another job at least during the execution time. The job is queued to receive resources when the results are received from the accelerator.

Abstract: Quantum processing unit slicing is disclosed. The qubits of a quantum processing unit are sliced or grouped to accommodate multiple independent and separate quantum jobs. The quantum jobs are matched, based on user tolerances related to at least number of shots, and the quantum jobs are then merged and performed. The results for each of the specific quantum jobs concurrently performed are extracted from the overall results of the quantum processing unit.

Abstract: Edge security is disclosed. Hosts included in a distributed infrastructure are equipped with data processing units that may be centrally managed using a control plane. Security policies can be selectively distributed to the hosts and localized at the hosts. Reactions to security violations can be initiated at the hosts in substantially real-time. The security policies may take various forms, including rule-based security policies and inference mode-based security policies.

Abstract: Attribute-based workload placement and orchestration in a computing environment including nodes is disclosed. A workload, when received at a scheduling engine, is given a workload score determined from the workload's attributes. Using the workload attributes, along with node attributes and/or data attributes, the workload is placed with one of the nodes. The node is selected based on how the workload attributes compare with the node attributes and/or the data attributes and based on the node score.

Abstract: One example method includes transmitting a request for a container image to a registry, receiving metadata associated with the container image, wherein the metadata allows a controller to mount an empty filesystem on a host machine, starting a container from the container image without receiving all files associated with the container image, receiving files, from a container server, needed by the container based on an access sequence associated with the container. This allows a container to be started without downloading the entire container image and also conversed bandwidth by providing the files as needed based on the manner in which the container accesses files during execution.

Abstract: Methods, apparatus, and processor-readable storage media for automatically extracting information from conversation text data using machine learning techniques are provided herein. An example computer-implemented method includes generating one or more embeddings from conversation text data by processing at least a portion of the conversation text data using a first set of machine learning techniques; extracting information associated with one or more predefined categories from at least one set of input conversation text data by processing at least a portion of the at least one set of input conversation text data using a second set of machine learning techniques in connection with at least a portion of the one or more embeddings; and performing one or more automated actions based at least in part on the extracted information.

Abstract: A method includes receiving from a first user in an edge utility system information specifying a trust level for one or more providers of edge resources, and aggregating edge resources of a plurality of edge computing sites of the edge utility system, the aggregated edge resources including at least edge resources of the one or more providers. The method further includes selecting particular ones of the aggregated edge resources based at least in part on the specified trust level, and utilizing the selected particular ones of the aggregated edge resources to execute at least a portion of a workload of the first user. The method may include tracking trust factors for each of the one or more providers over time, with the selection of particular ones of the aggregated edge resources being based at least in part on the one or more tracked trust factors and the specified trust level.

Abstract: An apparatus comprises at least one processing device configured to establish, utilizing a communication network, a remote collaboration session between first and second computing devices associated with first and second users, and to determine data to be exchanged between the first and second computing devices as part of the remote collaboration session. The first and second computing devices each comprise at least one of an augmented reality and a virtual reality computing device. The processing device is also configured to generate different logical network slices of the communication network, each logical network slice having a set of network characteristics associated with at least one type of the data to be exchanged between the first and second computing devices as part of the remote collaboration session. The processing device is further configured to communicate the different types of data between the first and second computing devices utilizing the logical network slices.

Abstract: An apparatus comprises a first processing device, the first processing device comprising a physical hardware controller configured for coupling with a second processing device. The first processing device is configured to identify one or more remote storage service instances attached to the second processing device, and to initiate storage emulation modules for the remote storage service instances attached to the second processing device, the storage emulation modules emulating one or more physical storage devices configured for attachment to the second processing device.