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.

Abstract: Techniques for function execution environment selection for a decomposed application are provided. In one example, an apparatus comprises at least one processing platform configured to execute a portion of an application program in a first virtual computing element, wherein the application program comprises one or more portions of marked code, receive a request for execution of one of the one or more portions of marked code, decide whether to execute the portion of marked code identified in the request in the first virtual computing element or in a second virtual computing element, determine an execution environment from one or more execution environments specified in the marked code for the second virtual computing element to execute the marked code, when it is decided to execute the portion of the marked code in the second virtual computing element, and cause the portion of marked code identified in the request to be executed.

Abstract: Techniques are disclosed for schedule management for machine learning model-based processing in a computing environment. For example, a method receives a machine learning model-based request and determines a scheduling decision for execution of the machine learning model-based request. Determination of the scheduling decision comprises identifying, based on one or more metrics, at least one cluster from a plurality of clusters as an execution environment in which the machine learning model-based request is to be executed. The machine learning model-based request may then be forwarded to the at least one identified cluster for execution.

Abstract: A method includes aggregating edge resources of a plurality of edge computing sites of an edge utility system, with the aggregated edge resources including edge resources provided by at least one user for utilization by one or more other users. The method further includes utilizing one or more portions of the aggregated edge resources to execute one or more workloads of the one or more other users, metering the utilization of the one or more portions of the aggregated edge resources in conjunction with the execution of the one or more workloads, and committing information characterizing at least the metered utilization of the aggregated edge resources to a distributed ledger.

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: Techniques are disclosed for chain schedule management for machine learning model-based processing in a computing environment. For example, a method receives a machine learning model-based request and determines a scheduling decision for execution of the machine learning model-based request. Determination of the scheduling decision comprises utilizing a set of one or more scheduling algorithms and comparing results of at least a portion of the set of one or more scheduling algorithms to identify execution environments of a computing environment in which the machine learning model-based request is to be executed. The identified execution environments may then be managed to execute the machine learning model-based request.

Abstract: An apparatus comprises a processing device configured to identify one or more dependent services for a workload deployed in a first computing cluster of a multi-cluster computing environment and to select, utilizing a global service catalog aggregating service information for sets of available services offered by at least two computing clusters of the multi-cluster computing environment, a second computing cluster of the multi-cluster computing environment to utilize for provisioning at least a given one of the one or more dependent services for the workload. The processing device is further configured to provision the given dependent service on a multi-cluster service mesh associated with the multi-cluster computing environment by configuring the multi-cluster service mesh to permit access by the workload on the first computing cluster of the multi-cluster computing environment to the given dependent service on the second computing cluster of the multi-cluster computing environment.

Abstract: Techniques are disclosed for virtualized computing resource management for machine learning model-based processing in a computing environment. For example, a method maintains one or more virtualized computing resources, wherein each of the one or more virtualized computing resources is created and one or more initializations are caused to be performed. After creation and performance of the one or more initializations, each of the one or more virtualized computing resources is placed in an idle state. The method then receives a machine learning model-based request, and removes at least one of the one or more virtualized computing resources from the idle state to process the machine learning model-based request.

Abstract: A method includes receiving inputs for respective users in an edge utility system comprising edge and core computing sites, with a first one of the inputs for a first user characterizing edge resources requested by that user for executing at least a portion of a workload of that user, and a second one of the inputs for a second user characterizing edge resources available from that user for executing at least a portion of a workload of another user. The method includes populating one or more data structures based at least in part on the received inputs, aggregating edge resources of multiple ones of the edge computing sites into an edge network based at least in part on the populated data structures, and utilizing at least a portion of the aggregated edge resources of the edge network to execute at least a portion of a workload of a particular user.

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 receive, from a host operating system of the second processing device, a discovery request, and to identify one or more emulation modules running on the first processing device that emulate physical hardware devices. The first processing device is also configured to provide, to the host operating system of the second processing device, a response to the discovery request indicating a set of capabilities associated with the emulated physical hardware devices. The first processing device is further configured to provision services to the second processing device by performing processing of the services utilizing hardware resources of the physical hardware controller and providing processing results to the second processing device via the emulated physical hardware devices.

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 remote security service instances attached to the second processing device and to initiate, at the first processing device, one or more network emulation modules for the remote security service instances attached to the second processing device that emulate physical network interface devices configured for attachment to the second processing device.

Abstract: A method comprises receiving a request for at least one network interface card resource, and identifying one or more network interface cards comprising the at least one network interface card resource. In the method, at least one virtualized computing resource image is received, and a workload is executed using an instance of the at least one virtualized computing resource image running on the one or more network interface cards. Workload execution includes implementation of a binding mechanism using one or more control groups and a given protocol. In addition, a cluster including one or more nodes comprising the identified one or more network interface cards is generated.

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.

Abstract: One example method includes receiving a computation workflow defined by a graph that includes quantum computing nodes, receiving a catalogue of quantum computing instances that are available in a hybrid classic-quantum computation infrastructure, transforming the graph to create a first graph transformation, and each of the quantum computing nodes is assigned a respective candidate resource allocation that identifies candidate resources operable to execute a respective quantum algorithm associated with that quantum computing node, and the transforming is performed using information from the catalogue, and optimizing the computation workflow by selecting, for each of the quantum computing nodes, a resource from the candidate resource allocation associated with that quantum computing node, and the optimizing includes transforming the first graph transformation to create a second graph transformation that specifies the selected resources for each node.

Abstract: One example method includes dynamically selecting a quantum processing unit. During execution of a hybrid application, a quantum execution bundle is processed to identify characteristics that are used to select an optimal quantum processing unit. For each iteration, the optimal quantum processing unit can be dynamically selected and inserted into the execution.

Abstract: Parallel quantum circuit execution is disclosed. When executing a quantum circuit, runtime characteristics of multiple quantum processing units are predicted and some of the quantum processing units are selected. The quantum circuit is executed in parallel at the selected quantum processing units.