Abstract: Techniques are disclosed for management of edge devices. For example, a method comprises coordinating operation of a plurality of edge devices in a system to process a plurality of workloads. The coordinating of the operation of the plurality of edge devices in the system comprises coordinating one or more times for changing a state of at least a subset of the plurality of edge devices from a first state corresponding to a first level of activity to a second state corresponding to a second level of activity. By way of further example, the coordinating of the operation of the plurality of edge devices in the system may further comprise coordinating one or more times for the processing of the plurality of workloads by at least the subset of the plurality of edge devices in 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 includes a digital twin of a microphone array. The digital twin acts as a digital copy of a physical microphone array. The digital array allows the microphone array to be analyzed, simulated and optimized. Further, the microphone array can be optimized for performing sound quality operations such as noise suppression and speech intelligibility.

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 performing sound quality operations. Microphone arrays are used to cancel or reduce or suppress background noise and to enhance speech. Subjective user input is received by an orchestration engine. The orchestration engine generates an output that includes at least adjustments to a microphone array. Controlling the microphone array based, in part, on subjective user feedback, allows desired speech or desired sound to be heard more clearly by the user.

Abstract: A collaborative distributed microphone array is configured to perform or be used in sound quality operations. A distributed microphone array can be operated to provide sound quality operations including sound suppression operations and speech intelligibility operations for multiple users in the same environment.

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 management of edge devices. For example, a method comprises coordinating operation of a plurality of edge devices in a system to process a plurality of workloads. The coordinating of the operation of the plurality of edge devices in the system comprises coordinating one or more times for changing a state of at least a subset of the plurality of edge devices from a first state corresponding to a first level of activity to a second state corresponding to a second level of activity. By way of further example, the coordinating of the operation of the plurality of edge devices in the system may further comprise coordinating one or more times for the processing of the plurality of workloads by at least the subset of the plurality of edge devices in the system.

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 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: Techniques are disclosed for management of edge resources. For example, a method comprises receiving energy consumption data corresponding to operation of a plurality of edge devices from a plurality of edge service providers. In the method, a plurality of energy efficiency scores are computed based, at least in part, on the energy consumption data. The energy efficiency scores correspond to operation of one or more of the edge devices associated with respective ones of the edge service providers. The method further comprises receiving one or more energy consumption parameters from at least one user device for the operation of the one or more edge devices, and identifying based, at least in part, on the energy efficiency scores, a subset of the edge devices corresponding to the energy consumption parameters. Data corresponding to the subset of the edge devices is transmitted to the at least one user device.

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: 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 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 collaborative distributed microphone array is configured to perform or be used in sound quality operations. A distributed microphone array can be operated to provide sound quality operations including sound suppression operations and speech intelligibility operations for multiple users in the same environment.

Abstract: One example method includes identifying an IO event comprising an IO made by a customer against a service hosted at an XaaS platform, and the IO event is associated with IO event metadata generated by the service, associating the IO event metadata with a billable customer operation, analyzing the IO event metadata and, based on the analyzing, associating the IO event with the customer, and generating a customer bill based on the associating of the IO event metadata with the billable customer operation, and based on the associating of the IO event with the customer.

Abstract: One example method includes performing learning management. A learning management system receives student related input including sensor data, profile data, and learning history. The learning management measures student interest levels, student engagement levels, and learning effectiveness. Educators view the measurements in real-time and are able to adapt to the real-time student statuses and measurements.

Abstract: One example method includes generating sound information regarding sound sources in an environment. The sound information is generated by separating and localizing the sound sources. The sound information is then presented as guidance to a user. The guidance may be presented graphically in a user interface, haptically, or in another manner.

Abstract: One example method includes determining representation bias in a data set. A bias detection engine is trained using a data set that is sufficiently diversified and/or unbiased. Once trained, test data sets can be evaluated by the bias detection engine to determine an amount of representation bias in the test data sets. The representation bias can be visually conveyed to a user and suggestions on how to reduce the representation bias may be provided and/or implemented to reduce the representation bias in the test data set. Suggestions can be implemented by adding or removing data from the test data that will reduce the representation bias.