Abstract: In general, this disclosure describes a multi-zone secure AI exchange. The multi-zone secure AI exchange may be implemented in a multi-cloud, multi-data center environment, where each zone may be in a different cloud or data center. The multi-zone secure AI exchange may include a data repository, a data exchange, and shared services. The data repository may be configured to store algorithms and datasets, each having a respective owning user. The data exchange may receive datasets and algorithms from the data repository, and may perform the algorithms to produce output data. Each of the data repository, data exchange, and shared services may have a different level of security. The data repository may implement the highest level of security, allowing the owner user, and only the owning user, to control how their data and algorithms move in and out of the data repository, or are changed while in the data repository.

Abstract: In general, this disclosure describes a multi-zone secure AI exchange. The multi-zone secure AI exchange may be implemented in a multi-cloud, multi-data center environment, where each zone may be in a different cloud or data center. The multi-zone secure AI exchange may include a data repository, a data exchange, and shared services. The data repository may be configured to store algorithms and datasets, each having a respective owning user. The data exchange may receive datasets and algorithms from the data repository, and may perform the algorithms to produce output data. Each of the data repository, data exchange, and shared services may have a different level of security. The data repository may implement the highest level of security, allowing the owner user, and only the owning user, to control how their data and algorithms move in and out of the data repository, or are changed while in the data repository.

Abstract: In general, this disclosure describes a multi-zone secure AI exchange. The multi-zone secure AI exchange may be implemented in a multi-cloud, multi-data center environment, where each zone may be in a different cloud or data center. The multi-zone secure AI exchange may include a data repository, a data exchange, and shared services. The data repository may be configured to store algorithms and datasets, each having a respective owning user. The data exchange may receive datasets and algorithms from the data repository, and may perform the algorithms to produce output data. Each of the data repository, data exchange, and shared services may have a different level of security. The data repository may implement the highest level of security, allowing the owner user, and only the owning user, to control how their data and algorithms move in and out of the data repository, or are changed while in the data repository.

Abstract: In general, this disclosure describes a multi-zone secure AI exchange. The multi-zone secure AI exchange may be implemented in a multi-cloud, multi-data center environment, where each zone may be in a different cloud or data center. The multi-zone secure AI exchange may include a data repository, a data exchange, and shared services. The data repository may be configured to store algorithms and datasets, each having a respective owning user. The data exchange may receive datasets and algorithms from the data repository, and may perform the algorithms to produce output data. Each of the data repository, data exchange, and shared services may have a different level of security. The data repository may implement the highest level of security, allowing the owner user, and only the owning user, to control how their data and algorithms move in and out of the data repository, or are changed while in the data repository.

Abstract: In general, this disclosure describes a system for securely training artificial intelligence (AI) models. The system may include communication circuitry for receiving from providers data sets, machine learning algorithms, and AI models. The data sets, machine learning algorithms, and AI models may be placed in a secure sandbox for access by a user. The user may securely train the AI models using the data sets in the secure sandbox. The secure sandbox may record AI model metadata associated with transactions involving the AI models and send the AI model metadata to a model registry. The model registry may attest to the lineage of the AI models.

Abstract: In some examples, a method includes receiving, by a data center infrastructure monitoring system, a registration request that indicates a method of a subscription application programming interface and specifies one or more event subjects of events describing a system operation of a data center; storing, by the data center infrastructure monitoring system to a data-topic map, respective mappings for the one or more event subjects to a topic of a cloud-based publication platform; monitoring, by a data center infrastructure monitoring system, a plurality of physical infrastructure assets that enable system operation within the data center to obtain an event that describes one of the event subjects; and publishing, by the data center infrastructure monitoring system, the event to the topic.

Abstract: In some examples, a method includes obtaining, by a data center infrastructure monitoring (DCIM) agent executing on a computing device deployed by a data center provider within a first data center of a plurality of geographically-distributed data centers, an infrastructure asset data object comprising infrastructure asset data for a physical infrastructure asset of a second data center of the plurality of geographically-distributed data centers; translating, by the DCIM agent using an asset mapping table, the infrastructure asset data object to formatted infrastructure asset data, wherein the asset mapping table maps one or more properties of infrastructure asset data objects to management protocol objects or control protocol objects for one of a management protocol or control protocol; and sending, by the DCIM agent to customer equipment co-located within the first data center, the formatted infrastructure asset data in a communication that conforms to one of the management protocol or control protocol.

Abstract: In some examples, a method includes receiving, by a data center infrastructure monitoring system, a registration request that indicates a method of a subscription application programming interface and specifies one or more event subjects of events describing a system operation of a data center; storing, by the data center infrastructure monitoring system to a data-topic map, respective mappings for the one or more event subjects to a topic of a cloud-based publication platform; monitoring, by a data center infrastructure monitoring system, a plurality of physical infrastructure assets that enable system operation within the data center to obtain an event that describes one of the event subjects; and publishing, by the data center infrastructure monitoring system, the event to the topic.

Abstract: A method includes receiving, by an asset privacy system, an input video feed that includes images of one or more assets located within a data center, identifying, by the asset privacy system, one or more corresponding assets for the images of the one or more assets, determining, by the asset privacy system, an asset of the one or more assets a customer is not authorized to view, and generating, from the input video feed, by the asset privacy system based at least on the determining, an output video feed that does not include images of the asset the customer is not authorized to view and that does include images of at least one other asset of the one or more assets.

Abstract: In some examples, a method includes obtaining, by a recommendation engine executing at a computing device, a list of prospective interconnection facilities administered by an interconnection facility provider and a list of prospective interconnection facility customers; identifying, by the recommendation engine, based at least in part on querying telemetry data that indicates interconnections of interconnection facility customers within the list of prospective interconnection facilities, sets of interconnection facilities from the list of prospective interconnection facilities in which respective interconnection facility customers are configured with existing interconnections; generating, by the recommendation engine and based at least in part on the list of prospective interconnection facilities, the sets of interconnection facilities, and the list of prospective interconnection facility customers, scoring data for the prospective interconnection facilities; and outputting, by the recommendation engine and f

Abstract: Techniques are described for distributed processing of Internet of Things (IoT) device data by edge systems co-located within a globally-distributed set of co-location facilities deployed and managed by a co-location facility provider. For example, a method includes selecting, by at least one of a plurality of edge computing systems co-located within respective co-location facilities each deployed and managed by a single co-location facility provider, a selected edge computing system of the plurality of edge computing systems to process data associated with events generated by an IoT device. The method also includes provisioning, at the selected edge computing system, an application programming interface (API) endpoint for communication with the IoT device, receiving, by the selected edge computing system at the endpoint, the data associated with the events generated by the IoT device, and processing, by the selected edge computing system, the data associated with the events generated by the IoT device.

Abstract: In some examples, a method includes receiving, by a data center infrastructure monitoring system, a registration request that indicates a method of a subscription application programming interface and specifies one or more event subjects of events describing a system operation of a data center; storing, by the data center infrastructure monitoring system to a data-topic map, respective mappings for the one or more event subjects to a topic of a cloud-based publication platform; monitoring, by a data center infrastructure monitoring system, a plurality of physical infrastructure assets that enable system operation within the data center to obtain an event that describes one of the event subjects; and publishing, by the data center infrastructure monitoring system, the event to the topic.

Abstract: In some examples, an application programming interface (API) and services engine is configured to determine, in response to receiving a service request from an application, an application identifier for the service request based at least on an authorization token included in the service request, wherein the application identifier identifies an application that issued the service request; select, based at least on the application identifier, an application data store uniquely associated with the application that issued the service request; and process the service request using data stored by the selected application data store.

Abstract: In some examples, an application delivery monitor system includes an application programming interface gateway to execute an application programming interface to receive, from a user device, a request for continuous application delivery progress for an application; and an orchestrator to send, in response to the request, respective sub-requests for the request to a plurality of continuous application delivery sub-systems of an application delivery platform, wherein the orchestrator is further configured to receive, in response to the sub-requests, respective indications of continuous application delivery progress from the plurality of continuous application delivery sub-systems, and wherein the orchestrator is further configured to send, to the user device, a consolidated response to the request, wherein the consolidated response includes the respective indications of continuous application delivery progress from the plurality of continuous application delivery sub-systems.

Abstract: Techniques are described for distributed processing of Internet of Things (IoT) device data by edge systems co-located within a globally-distributed set of co-location facilities deployed and managed by a co-location facility provider. For example, a method includes selecting, by at least one of a plurality of edge computing systems co-located within respective co-location facilities each deployed and managed by a single co-location facility provider, a selected edge computing system of the plurality of edge computing systems to process data associated with events generated by an IoT device. The method also includes provisioning, at the selected edge computing system, an application programming interface (API) endpoint for communication with the IoT device, receiving, by the selected edge computing system at the endpoint, the data associated with the events generated by the IoT device, and processing, by the selected edge computing system, the data associated with the events generated by the IoT device.

Abstract: A central infrastructure monitoring system includes an asset configurator; and a plurality of data center infrastructure monitoring systems each associated with a respective data center of a plurality of geographically distributed data centers that include one or more physical infrastructure assets of a plurality of physical infrastructure assets for enabling system operation within the respective data center. The data center infrastructure monitoring systems are coupled to the central infrastructure monitoring system.

Abstract: Techniques are described for distributed processing of Internet of Things (IoT) device data by edge systems co-located within a globally-distributed set of co-location facilities deployed and managed by a co-location facility provider. For example, a method includes selecting, by at least one of a plurality of edge computing systems co-located within respective co-location facilities each deployed and managed by a single co-location facility provider, a selected edge computing system of the plurality of edge computing systems to process data associated with events generated by an IoT device. The method also includes provisioning, at the selected edge computing system, an application programming interface (API) endpoint for communication with the IoT device, receiving, by the selected edge computing system at the endpoint, the data associated with the events generated by the IoT device, and processing, by the selected edge computing system, the data associated with the events generated by the IoT device.

Abstract: In general, techniques for integration of diverse software applications in i) a co-location facility, ii) communication interconnection network facility, or iii) both, are described. For example, a transformation engine for datacenter colocation and network interconnection products may assist in a dynamic i) transformation of commands and ii) data structure translation as well as data structure transformation between a source application (SA) and a target application (TA) via two or more translation assist templates.

Abstract: A central infrastructure monitoring system includes an asset configurator; and a plurality of data center infrastructure monitoring systems each associated with a respective data center of a plurality of geographically distributed data centers that include one or more physical infrastructure assets of a plurality of physical infrastructure assets for enabling system operation within the respective data center. The data center infrastructure monitoring systems are coupled to the central infrastructure monitoring system.

Abstract: Techniques are described for distributed processing of Internet of Things (IoT) device data by edge systems co-located within a globally-distributed set of co-location facilities deployed and managed by a co-location facility provider. For example, a method includes selecting, by at least one of a plurality of edge computing systems co-located within respective co-location facilities each deployed and managed by a single co-location facility provider, a selected edge computing system of the plurality of edge computing systems to process data associated with events generated by an IoT device. The method also includes provisioning, at the selected edge computing system, an application programming interface (API) endpoint for communication with the IoT device, receiving, by the selected edge computing system at the endpoint, the data associated with the events generated by the IoT device, and processing, by the selected edge computing system, the data associated with the events generated by the IoT device.