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 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 physical storage devices configured for attachment to the second processing device.

Abstract: One example method includes creating a manifest that specifies one or more requirements concerning execution of an application that resides at an end device in an N-tier configuration, identifying a workload that is associated with the application and executable at one or more edge stations of the N-tier configuration, gathering and evaluating network telemetry, orchestrating the workload based on the network telemetry and the manifest, scheduling performance of the workload at the one or more edge stations, and performing the workload at the one or more edge stations in accordance with the scheduling.

Abstract: One example method includes creating a manifest that specifies one or more requirements concerning execution of an application that resides at an end device in an N-tier configuration, identifying a workload that is associated with the application and executable at one or more edge stations of the N-tier configuration, gathering and evaluating network telemetry, orchestrating the workload based on the network telemetry and the manifest, scheduling performance of the workload at the one or more edge stations, and performing the workload at the one or more edge stations in accordance with the scheduling.

Abstract: A method includes identifying a first event that has been at least partly performed, wherein the first event comprises an element of a sequence of events, and the first event comprises performance of a first computing function, predicting a second event expected to occur next in the sequence after completion of the first event, and the second event comprises performance of a second computing function, predicting a start time of the second event, based on information about the second event, identifying a particular container capable of implementing the second computing function associated with the second event, predicting a start time for start-up of the container, starting up the container, and completing start-up of the container prior to receipt of a request for the second computing function to be performed by the container, wherein the container is ready to perform the second computing function immediately after start-up has been completed.

Abstract: A method for managing data includes obtaining a request for a machine learning (ML) pipeline selection from a client, wherein the request comprises a training dataset and a domain of the training dataset, and in response to the request: identifying a set of ML pipelines based on the domain, obtaining runtime statistics for the set of ML pipelines using the domain and at least a portion the training dataset, generating, using a user preference model, an ordering of the set of ML pipelines based on the runtime statistics and user preferences, and presenting the ordering, the runtime statistics, and a notification based on the ordering to the client.

Abstract: A resource usage platform is disclosed. The platform performs preemptive container load balancing, auto scaling, and placement in a computing system. Resource usage data is collected from containers and used to train a model that generates inferences regarding resource usage. The resource usage operations are performed based on the inferences and on environment data such as available resources, service needs, and hardware requirements.

Abstract: One example method includes receiving, as an input, an ML pipeline definition, identifying a group of layers required to be created for the ML pipeline definition, for one of more of the layers, receiving input concerning one or more characteristics of the layer, creating the layers for which input has been received, and packaging the created layers with the ML pipeline definition to create a production-ready ML model.

Abstract: One example method includes receiving, from a microservice, a service request that identifies a service needed by the microservice, and an API of an endpoint that provides the service, evaluating the service request to determine whether the service request conforms to a policy, when the service request has been determined to conform with the policy, evaluating the endpoint to determine if endpoint performance meets established guidelines, and when it is determined that the endpoint performance does not meet the established guidelines, identifying an alternative endpoint that meets the established guidelines and that provides the requested service. Next, the method includes transforming the API of the service identified in the service request to an alternative API of the service provided by the alternative endpoint, and sending the service request and the alternative API to the alternative endpoint.

Abstract: One example method includes performing dynamic access control in a computing network. A computing environment is configured such that an application can access a service without specifying secrets. The secrets needed to access the service are obtained and stored in a credential store. The secrets can be obtained using the service mesh in a manner that isolates the application from the secrets.

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: One example method includes determining that local resources at an edge site are inadequate to support performance of a function needed by software running on the edge site, invoking a client agent, in response to invoking the client agent, receiving an execution manifest, determining, by the client agent, where to execute the function, wherein the determining comprises identifying a target execution environment for the function and the determining is based in part on information contained in the execution manifest, and transmitting, by the client agent, the execution manifest to a server agent of the target execution environment, and the execution manifest facilitates execution of the function in the target execution environment.

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 multimedia service instances attached to the second processing device and to initiate, at the first processing device, one or more multimedia emulation modules for the remote multimedia service instances attached to the second processing device, the multimedia emulation modules emulating one or more physical multimedia input devices configured for attachment to the second processing device.

Abstract: One example method includes evaluating code of a quantum circuit, estimating one or more runtime statistics concerning the code, generating a recommendation based on the one or more runtime statistics, and the recommendation identifies one or more resources recommended to be used to execute the quantum circuit, checking availability of the resources for executing the quantum circuit, allocating resources, when available, sufficient to execute the quantum circuit, and using the allocated resources to execute the quantum circuit.

Abstract: One example method includes creating an image definition file, using the image definition file to create an image that is deployable to an edge device, copying an agent into the image, stripping any user passwords out of the image, and removing any unnecessary packages from the image, and after the stripping and the removing, the image becomes an immutable image. The immutable image may then be deployed to a group of edge devices, and one or more layers of the immutable image may be updated by the agent.

Abstract: One example method includes connecting to a server component, transmitting, to the server component, information concerning a hardware configuration associated with an asset having a capability that is fully utilized during a first time period and the capability is idle during a second time period, receiving, from the server component, cluster connection information, and using the cluster connection information to temporarily connect the asset to the cluster as a node of the cluster so that the capability is available during idle time to perform a workload of the cluster.

Abstract: Techniques described herein relate to a method for deploying workflows. The method may include obtaining, by a registration manager associated with accelerator pools, a first request from a client to perform a portion of a first workflow using accelerators; identifying a minimum quantity and a maximum quantity of accelerators associated with the first request; identifying an accelerator pool of the accelerator pools to perform the portion of the first workflow based on the minimum quantity and the maximum quantity of accelerators, where the accelerator pool includes at least the maximum quantity of accelerators; establishing a connection between the client and the accelerators of the accelerator pool to perform the portion of the first workflow; and initiating performance of the portion of the first workflow, wherein the portion of the first workflow is performed using at least the minimum quantity of accelerators.

Abstract: Techniques described herein relate to a method for managing workflows. The method may include obtaining, from a client, by a registration manager associated with accelerator pools, a request to perform a portion of a workflow using accelerators; identifying a minimum quantity and a maximum quantity of accelerators associated with the request; identifying a storage to store data associated with the portion of the workflow; identifying an accelerator pool of the accelerator pools that includes at least the maximum quantity of accelerators; establishing a connection between the client, the storage, and accelerators of the accelerator pool to perform the portion of the workflow; and initiating the performance of the portion of the workflow using the storage, the client, and the accelerators of the accelerator pool.

Abstract: Techniques for constructing and otherwise managing knowledge graphs in information processing system environments are disclosed. For example, a method comprises the following steps. The method collects data from a plurality of data sources. The method extracts structured data and unstructured data from the collected data, wherein unstructured data is extracted using an unsupervised machine learning process. The method forms a plurality of sub-graph structures comprising a sub-graph structure for each of the data sources based on at least a portion of the extracted structured data and unstructured data. The method combines the plurality of sub-graph structures to form a combined graph structure representing the collected data from the plurality of data sources. The resulting combined graph structure is a comprehensive knowledge graph.

Abstract: One example method includes receiving, from a microservice, a service request that identifies a service needed by the microservice, and an API of an endpoint that provides the service, evaluating the service request to determine whether the service request conforms to a policy, when the service request has been determined to conform with the policy, evaluating the endpoint to determine if endpoint performance meets established guidelines, and when it is determined that the endpoint performance does not meet the established guidelines, identifying an alternative endpoint that meets the established guidelines and that provides the requested service. Next, the method includes transforming the API of the service identified in the service request to an alternative API of the service provided by the alternative endpoint, and sending the service request and the alternative API to the alternative endpoint.

Abstract: One example method, which may be performed at an end device configured to communicate with an edge station, includes listening for a broadcast signal from the edge station, joining a broadcast channel, receiving edge station information, selecting an edge station, transmitting a manifest to the selected edge station, receiving route information from the selected edge station, accessing a container identified in the route information, and issuing a call to the selected edge station to execute an application workload on the container.